Stacked Fisher autoencoder for SAR change detection
•The original SAE is expanded to suit with the multiplicative noise in SAR change detection.•The features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE.•Experiments on the simulated and real SAR dat...
Uložené v:
| Vydané v: | Pattern recognition Ročník 96; s. 106971 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.12.2019
|
| Predmet: | |
| ISSN: | 0031-3203, 1873-5142 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | •The original SAE is expanded to suit with the multiplicative noise in SAR change detection.•The features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE.•Experiments on the simulated and real SAR datasets reveal that the proposed SFAE algorithm is effective on multitemporal single/multi-polarization SAR change detection. Specifically, the proposed SFAE method is obviously superior to the real-time methods on detection accuracy and the non-realtime methods on computational complexity.
Stacked autoencoder is effective in image denoising and classification when it is used for synthetic aperture radar (SAR) change detection. However, the resulting features may not be discriminative enough for in some sense. To alleviate this problem, in this paper we propose a stacked Fisher autoencoder (SFAE) for SAR change detection. Specifically, in the framework of SFAE, unsupervised layer-wise feature learning and supervised fine-tuning are jointly performed when training the network. The trained network can be used to detect the changes in both of the single and multi-polarization SAR datasets in real-time. The proposed SFAE has two advantages. The first one is to expand the stacked autoencoder to suit the environment with the multiplicative noise in SAR change detection. The second is that the features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE. The results on the simulated and real SAR datasets indicate that the proposed SFAE algorithm has a significant advantage on multitemporal single/multi-polarization SAR (SAR/PolSAR) change detection. |
|---|---|
| AbstractList | •The original SAE is expanded to suit with the multiplicative noise in SAR change detection.•The features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE.•Experiments on the simulated and real SAR datasets reveal that the proposed SFAE algorithm is effective on multitemporal single/multi-polarization SAR change detection. Specifically, the proposed SFAE method is obviously superior to the real-time methods on detection accuracy and the non-realtime methods on computational complexity.
Stacked autoencoder is effective in image denoising and classification when it is used for synthetic aperture radar (SAR) change detection. However, the resulting features may not be discriminative enough for in some sense. To alleviate this problem, in this paper we propose a stacked Fisher autoencoder (SFAE) for SAR change detection. Specifically, in the framework of SFAE, unsupervised layer-wise feature learning and supervised fine-tuning are jointly performed when training the network. The trained network can be used to detect the changes in both of the single and multi-polarization SAR datasets in real-time. The proposed SFAE has two advantages. The first one is to expand the stacked autoencoder to suit the environment with the multiplicative noise in SAR change detection. The second is that the features extracted by SFAE are more discriminative than the original stacked autoencoder due to that Fisher discriminant criterion is incorporated into SFAE. The results on the simulated and real SAR datasets indicate that the proposed SFAE algorithm has a significant advantage on multitemporal single/multi-polarization SAR (SAR/PolSAR) change detection. |
| ArticleNumber | 106971 |
| Author | Li, Xuelong Dong, Yongsheng Liu, Ganchao Jiao, Licheng Li, Lingling |
| Author_xml | – sequence: 1 givenname: Ganchao surname: Liu fullname: Liu, Ganchao email: ganchliu@gmail.com organization: Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, China – sequence: 2 givenname: Lingling orcidid: 0000-0002-6130-2518 surname: Li fullname: Li, Lingling email: linglingxidian@gmail.com organization: School of Artificial Intelligence, Xidian University, Xi’an, China – sequence: 3 givenname: Licheng surname: Jiao fullname: Jiao, Licheng email: lchjiao@mail.xidian.edu.cn organization: School of Artificial Intelligence, Xidian University, Xi’an, China – sequence: 4 givenname: Yongsheng orcidid: 0000-0002-6281-9658 surname: Dong fullname: Dong, Yongsheng email: dongyongsheng98@163.com organization: School of Information Engineering, Henan University of Science and Technology, Luoyang, China – sequence: 5 givenname: Xuelong surname: Li fullname: Li, Xuelong email: xuelong_li@nwpu.edu.cn organization: Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, China |
| BookMark | eNqFkMFKw0AQhhepYFt9Aw95gdSd3SSbeBBKsVUoCFbPy2Z20m6t2bJZBd_elHjyoKcZZvh-5psJG7W-Jcaugc-AQ3Gznx1NRL-dCQ5VPyoqBWdsDKWSaQ6ZGLEx5xJSKbi8YJOu23MOql-MmdxEg29kk6XrdhQS8xE9teht3zc-JJv5c4I7024psRQJo_PtJTtvzKGjq586Za_L-5fFQ7p-Wj0u5usUpRIxxQpVA1BXRZkXYGxe20YJwU2GUpgc65IrgqwpFWFBGWXCFihyWRe5yEsDcsqyIReD77pAjT4G927ClwauT-J6rwdxfRLXg3iP3f7C0EVzOjwG4w7_wXcDTL3Yp6OgO3T9Q8i60Ntr693fAd9f43e1 |
| CitedBy_id | crossref_primary_10_3390_rs14071552 crossref_primary_10_3390_rs14020368 crossref_primary_10_1016_j_patcog_2024_111266 crossref_primary_10_1080_01431161_2024_2302351 crossref_primary_10_1080_01431161_2020_1826066 crossref_primary_10_3390_ijerph20043059 crossref_primary_10_1016_j_neucom_2020_03_107 crossref_primary_10_1016_j_ecoinf_2021_101310 crossref_primary_10_1109_MGRS_2021_3063465 crossref_primary_10_1109_TGRS_2024_3453501 crossref_primary_10_1109_TIP_2022_3154922 crossref_primary_10_1109_TGRS_2023_3241366 crossref_primary_10_1109_JSTARS_2024_3513694 crossref_primary_10_1109_TGRS_2020_3039847 crossref_primary_10_1016_j_jag_2023_103511 crossref_primary_10_1109_TGRS_2024_3407088 crossref_primary_10_1109_TGRS_2024_3379567 crossref_primary_10_3390_rs14030438 crossref_primary_10_1080_01431161_2023_2173033 crossref_primary_10_1016_j_knosys_2021_106921 crossref_primary_10_3390_rs15010123 crossref_primary_10_3390_rs14041018 crossref_primary_10_1080_01431161_2022_2115863 crossref_primary_10_3390_rs11161854 crossref_primary_10_3390_rs14246361 crossref_primary_10_3390_rs16111974 crossref_primary_10_1109_LGRS_2023_3262586 crossref_primary_10_1016_j_patcog_2022_108549 crossref_primary_10_1016_j_jag_2021_102615 crossref_primary_10_1117_1_JRS_18_016503 crossref_primary_10_1109_TGRS_2020_3026099 crossref_primary_10_1109_TGRS_2022_3229027 crossref_primary_10_1111_tgis_13133 crossref_primary_10_1109_JSTARS_2024_3409775 crossref_primary_10_1109_TGRS_2021_3071799 crossref_primary_10_1109_TGRS_2023_3245674 crossref_primary_10_1109_ACCESS_2022_3192967 crossref_primary_10_1109_JSTARS_2024_3350044 crossref_primary_10_3390_rs15020395 crossref_primary_10_1109_MGRS_2024_3412770 crossref_primary_10_1109_TGRS_2020_3001584 crossref_primary_10_1016_j_patcog_2019_107166 crossref_primary_10_1016_j_patcog_2022_108717 crossref_primary_10_7717_peerj_cs_2786 crossref_primary_10_1109_TGRS_2022_3168331 crossref_primary_10_1109_TGRS_2022_3144165 crossref_primary_10_1109_JSEN_2020_3030910 crossref_primary_10_1109_TGRS_2022_3201206 crossref_primary_10_3390_su14169847 crossref_primary_10_1007_s11760_025_04147_y crossref_primary_10_3390_s20051533 crossref_primary_10_3390_rs12101688 crossref_primary_10_1080_01431161_2025_2467303 crossref_primary_10_1109_ACCESS_2019_2957148 crossref_primary_10_1109_ACCESS_2024_3415054 crossref_primary_10_1016_j_neucom_2023_126611 crossref_primary_10_3390_electronics14071335 crossref_primary_10_1007_s11045_020_00758_5 crossref_primary_10_3390_app12020818 |
| Cites_doi | 10.1080/01431161.2014.882030 10.1109/MGRS.2013.2248301 10.1109/TGRS.2010.2087763 10.1109/TGRS.2005.857987 10.1109/LGRS.2009.2025059 10.1109/TGRS.2014.2363548 10.1109/JSTARS.2014.2328344 10.1109/TGRS.2016.2590145 10.1109/TGRS.2004.842441 10.1109/TIP.2007.896651 10.1080/01431169108929656 10.1109/LGRS.2007.900751 10.1109/TGRS.2012.2223219 10.1109/TGRS.2018.2856926 10.1109/JSTARS.2012.2201135 10.1109/TPAMI.1980.4766994 10.1109/TNNLS.2018.2847309 10.1109/5.726791 10.1007/s11263-014-0722-8 10.1109/TIP.2009.2029593 10.1109/TFUZZ.2013.2249072 10.1016/j.patcog.2014.09.027 10.1016/j.patcog.2017.01.002 10.1016/j.isprsjprs.2015.02.008 10.1109/TGRS.2018.2819367 10.1016/j.patcog.2016.07.040 10.1109/TGRS.2018.2805471 10.1109/36.789635 10.1109/TNNLS.2015.2435783 10.1109/TGRS.2002.808066 10.1109/TPAMI.2008.137 10.1109/TPAMI.2007.1096 10.1109/TGRS.2007.893568 10.1109/36.7708 10.1016/j.patcog.2012.02.004 10.1109/TGRS.2018.2863224 10.1162/neco.2006.18.7.1527 10.1109/TGRS.2008.918647 10.1109/TGRS.2002.1000322 10.1109/LGRS.2017.2762694 10.1109/TSP.2017.2712124 10.1109/TGRS.2016.2532320 10.1109/TGRS.2013.2284510 10.1109/TGRS.2012.2211883 10.1109/TGRS.2015.2407953 |
| ContentType | Journal Article |
| Copyright | 2019 Elsevier Ltd |
| Copyright_xml | – notice: 2019 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.patcog.2019.106971 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-5142 |
| ExternalDocumentID | 10_1016_j_patcog_2019_106971 S0031320319302742 |
| GroupedDBID | --K --M -D8 -DT -~X .DC .~1 0R~ 123 1B1 1RT 1~. 1~5 29O 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABFRF ABHFT ABJNI ABMAC ABTAH ABXDB ABYKQ ACBEA ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADMXK ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F0J F5P FD6 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM KZ1 LG9 LMP LY1 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SST SSV SSZ T5K TN5 UNMZH VOH WUQ XJE XPP ZMT ZY4 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c372t-c9c7f11b968561ad5bdf7220a4c32a5cb807e14f87ec6e4e42d6c253b65258a13 |
| ISICitedReferencesCount | 65 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000487569700003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0031-3203 |
| IngestDate | Tue Nov 18 21:44:55 EST 2025 Sat Nov 29 07:28:46 EST 2025 Fri Feb 23 02:25:25 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Stacked fisher autoencoder (SFAE) Stacked autoencoder (SAE) Synthetic aperture radar (SAR) Change detection Fisher criterion |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c372t-c9c7f11b968561ad5bdf7220a4c32a5cb807e14f87ec6e4e42d6c253b65258a13 |
| ORCID | 0000-0002-6281-9658 0000-0002-6130-2518 |
| ParticipantIDs | crossref_primary_10_1016_j_patcog_2019_106971 crossref_citationtrail_10_1016_j_patcog_2019_106971 elsevier_sciencedirect_doi_10_1016_j_patcog_2019_106971 |
| PublicationCentury | 2000 |
| PublicationDate | December 2019 2019-12-00 |
| PublicationDateYYYYMMDD | 2019-12-01 |
| PublicationDate_xml | – month: 12 year: 2019 text: December 2019 |
| PublicationDecade | 2010 |
| PublicationTitle | Pattern recognition |
| PublicationYear | 2019 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Bovolo, Marin, Bruzzone (bib0005) 2013; 51 Lv, Zhong, Zhao, Zhang (bib0048) 2018; 56 Lee (bib0001) 1980 Lê, Atto, Trouvé, Solikhin, Pinel (bib0024) 2015; 107 Ban, Yousif (bib0007) 2012; 5 Mou, Bruzzone, Zhu (bib0034) 2019; 57 Tao, Li, Wu, Maybank (bib0049) 2007; 29 Gong, Niu, Zhang, Li (bib0033) 2017; 14 Hachicha, Chaabane (bib0046) 2014; 35 Bovolo, Bruzzone (bib0004) 2005; 43 Graves, Liwicki, Fernández, Bertolami, Bunke, Schmidhuber (bib0030) 2009; 31 Conradsen, Nielsen, Schou, Skriver (bib0011) 2003; 41 Duda, Hart, Stork (bib0053) 2012 Hou, Wei, Zheng, Wang (bib0044) 2014; 7 Lee, Pottier (bib0041) 2009 Moreira, Prats-Iraola, Younis, Krieger, Hajnsek, Papathanassiou (bib0042) 2013; 1 Liu, Jiao, Liu, Zhong, Wang (bib0023) 2015; 48 Su, Gong, Zhang, Zhang, Liu, Yang (bib0037) 2017; 66 Marino, Cloude, Lopez-Sanchez (bib0012) 2013; 51 Socher, Pennington, Huang, Ng, Manning (bib0039) 2011 Ciuonzo, Vincenzo, Antonio (bib0013) 2017; 65 Xie, Xu, Chen (bib0032) 2012 Deledalle, Denis, Tupin (bib0002) 2009; 18 Chen, Chen, An, Cui, Yang (bib0003) 2011; 49 Marin, Bovolo, Bruzzone (bib0006) 2015; 53 White (bib0045) 1991; 12 Zhang, Jiao, Liu, Bo, Gong (bib0021) 2008; 46 Du (bib0050) 2007; 4 Hinton, Osindero, Teh (bib0026) 2006; 18 Inglada, Mercier (bib0010) 2007; 45 Liu, Jiao, Tang, Yang, Ma, Hou (bib0036) 2019; 30 Gong, Su, Jia, Chen (bib0019) 2014; 22 Domínguez, Meier, Small, Schaepman, Bruzzone, Henke (bib0020) 2018; 56 Li, Gong, Jiao, Li, Stolkin (bib0047) 2015; 53 Celik (bib0017) 2009; 6 Yang, Zhang, Feng, Zhang (bib0055) 2014; 109 Zanotta, Haertel (bib0008) 2012; 45 Yang, Yang, Yan, Song, Xia (bib0016) 2016; 54 Vu, Gomes, Pettersson, Dammert, Hellsten (bib0015) 2019; 57 Lee, Grunes, De Grandi (bib0040) 1999; 37 Marino, Hajnsek (bib0025) 2014; 52 Vincent, Larochelle, Lajoie, Bengio, Manzagol (bib0029) 2010; 11 Dierking, Skriver (bib0043) 2002; 40 LeCun, Bottou, Bengio, Haffner (bib0028) 1998; 86 Salakhutdinov, Mnih, Hinton (bib0027) 2007 Ng (bib0051) 2011; vol. 72 Marc’Aurelio Ranzato, Chopra, LeCun (bib0038) 2007 Touzi, Lopes, Bousquet (bib0052) 1988; 26 Zheng, Jiao, Liu, Zhang, Hou, Wang (bib0018) 2017; 61 Gong, Zhao, Liu, Miao, Jiao (bib0035) 2016; 27 Chatelain, Tourneret, Inglada, Ferrari (bib0009) 2007; 16 Wang, Yan, Xu, Tang, Huang (bib0054) 2007 Akbari, Anfinsen, Doulgeris, Eltoft, Moser, Serpico (bib0014) 2016; 54 Bazi, Bruzzone, Melgani (bib0022) 2005; 43 Ciresan, Meier, Schmidhuber (bib0031) 2012 Marin (10.1016/j.patcog.2019.106971_bib0006) 2015; 53 Lee (10.1016/j.patcog.2019.106971_bib0041) 2009 Moreira (10.1016/j.patcog.2019.106971_bib0042) 2013; 1 Zheng (10.1016/j.patcog.2019.106971_bib0018) 2017; 61 Wang (10.1016/j.patcog.2019.106971_bib0054) 2007 Gong (10.1016/j.patcog.2019.106971_bib0033) 2017; 14 Deledalle (10.1016/j.patcog.2019.106971_bib0002) 2009; 18 Gong (10.1016/j.patcog.2019.106971_bib0019) 2014; 22 Du (10.1016/j.patcog.2019.106971_bib0050) 2007; 4 Su (10.1016/j.patcog.2019.106971_bib0037) 2017; 66 Touzi (10.1016/j.patcog.2019.106971_bib0052) 1988; 26 Hachicha (10.1016/j.patcog.2019.106971_bib0046) 2014; 35 Dierking (10.1016/j.patcog.2019.106971_bib0043) 2002; 40 Marc’Aurelio Ranzato (10.1016/j.patcog.2019.106971_bib0038) 2007 Vincent (10.1016/j.patcog.2019.106971_bib0029) 2010; 11 Zhang (10.1016/j.patcog.2019.106971_bib0021) 2008; 46 Gong (10.1016/j.patcog.2019.106971_bib0035) 2016; 27 Lee (10.1016/j.patcog.2019.106971_bib0040) 1999; 37 Lê (10.1016/j.patcog.2019.106971_bib0024) 2015; 107 Bovolo (10.1016/j.patcog.2019.106971_bib0004) 2005; 43 Lee (10.1016/j.patcog.2019.106971_bib0001) 1980 Yang (10.1016/j.patcog.2019.106971_bib0016) 2016; 54 Ng (10.1016/j.patcog.2019.106971_bib0051) 2011; vol. 72 Ciuonzo (10.1016/j.patcog.2019.106971_bib0013) 2017; 65 LeCun (10.1016/j.patcog.2019.106971_bib0028) 1998; 86 Bovolo (10.1016/j.patcog.2019.106971_bib0005) 2013; 51 Liu (10.1016/j.patcog.2019.106971_bib0036) 2019; 30 Salakhutdinov (10.1016/j.patcog.2019.106971_bib0027) 2007 Chen (10.1016/j.patcog.2019.106971_bib0003) 2011; 49 Domínguez (10.1016/j.patcog.2019.106971_bib0020) 2018; 56 Yang (10.1016/j.patcog.2019.106971_bib0055) 2014; 109 Socher (10.1016/j.patcog.2019.106971_bib0039) 2011 Lv (10.1016/j.patcog.2019.106971_bib0048) 2018; 56 Bazi (10.1016/j.patcog.2019.106971_bib0022) 2005; 43 Akbari (10.1016/j.patcog.2019.106971_bib0014) 2016; 54 Chatelain (10.1016/j.patcog.2019.106971_bib0009) 2007; 16 Conradsen (10.1016/j.patcog.2019.106971_bib0011) 2003; 41 Inglada (10.1016/j.patcog.2019.106971_bib0010) 2007; 45 Hinton (10.1016/j.patcog.2019.106971_bib0026) 2006; 18 Li (10.1016/j.patcog.2019.106971_bib0047) 2015; 53 Tao (10.1016/j.patcog.2019.106971_bib0049) 2007; 29 Liu (10.1016/j.patcog.2019.106971_bib0023) 2015; 48 Ciresan (10.1016/j.patcog.2019.106971_bib0031) 2012 Marino (10.1016/j.patcog.2019.106971_bib0025) 2014; 52 Vu (10.1016/j.patcog.2019.106971_bib0015) 2019; 57 Hou (10.1016/j.patcog.2019.106971_bib0044) 2014; 7 Duda (10.1016/j.patcog.2019.106971_bib0053) 2012 Ban (10.1016/j.patcog.2019.106971_bib0007) 2012; 5 Mou (10.1016/j.patcog.2019.106971_bib0034) 2019; 57 Xie (10.1016/j.patcog.2019.106971_bib0032) 2012 White (10.1016/j.patcog.2019.106971_bib0045) 1991; 12 Celik (10.1016/j.patcog.2019.106971_bib0017) 2009; 6 Marino (10.1016/j.patcog.2019.106971_bib0012) 2013; 51 Zanotta (10.1016/j.patcog.2019.106971_bib0008) 2012; 45 Graves (10.1016/j.patcog.2019.106971_bib0030) 2009; 31 |
| References_xml | – volume: 43 start-page: 2963 year: 2005 end-page: 2972 ident: bib0004 article-title: A detail-preserving scale-driven approach to change detection in multitemporal SAR images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 107 start-page: 64 year: 2015 end-page: 76 ident: bib0024 article-title: Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 30 start-page: 818 year: 2019 end-page: 833 ident: bib0036 article-title: Local restricted convolutional neural network for change detection in polarimetric SAR images publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 6 start-page: 772 year: 2009 end-page: 776 ident: bib0017 article-title: Unsupervised change detection in satellite images using principal component analysis and publication-title: IEEE Geosci. Remote Sens. Lett. – start-page: 165 year: 1980 end-page: 168 ident: bib0001 article-title: Digital image enhancement and noise filtering by use of local statistics publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – year: 2012 ident: bib0053 article-title: Pattern Classification – volume: 49 start-page: 1744 year: 2011 end-page: 1754 ident: bib0003 article-title: Nonlocal filtering for polarimetric SAR data: a pretest approach publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 5 start-page: 1087 year: 2012 end-page: 1094 ident: bib0007 article-title: Multitemporal spaceborne SAR data for urban change detection in china publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. – volume: 51 start-page: 2042 year: 2013 end-page: 2054 ident: bib0005 article-title: A hierarchical approach to change detection in very high resolution SAR images for surveillance applications publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 22 start-page: 98 year: 2014 end-page: 109 ident: bib0019 article-title: Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images publication-title: IEEE Trans. Fuzzy Syst. – volume: 35 start-page: 1693 year: 2014 end-page: 1714 ident: bib0046 article-title: On the SAR change detection review and optimal decision publication-title: Int. J. Remote Sens. – volume: 27 start-page: 125 year: 2016 end-page: 1382 ident: bib0035 article-title: Change detection in synthetic aperture radar images based on deep neural networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 109 start-page: 209 year: 2014 end-page: 232 ident: bib0055 article-title: Sparse representation based fisher discrimination dictionary learning for image classification publication-title: Int. J. Comput. Vis. – start-page: 3642 year: 2012 end-page: 3649 ident: bib0031 article-title: Multi-column deep neural networks for image classification publication-title: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) – volume: 40 start-page: 618 year: 2002 end-page: 636 ident: bib0043 article-title: Change detection for thematic mapping by means of airborne multitemporal polarimetric SAR imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 18 start-page: 1527 year: 2006 end-page: 1554 ident: bib0026 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. – volume: 45 start-page: 2927 year: 2012 end-page: 2937 ident: bib0008 article-title: Gradual land cover change detection based on multitemporal fraction images publication-title: Pattern Recognit. – volume: 37 start-page: 2363 year: 1999 end-page: 2373 ident: bib0040 article-title: Polarimetric SAR speckle filtering and its implication for classification publication-title: IEEE Trans. Geosci. Remote Sens. – start-page: 1 year: 2007 end-page: 8 ident: bib0054 article-title: Trace ratio vs. ratio trace for dimensionality reduction publication-title: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) – volume: 43 start-page: 874 year: 2005 end-page: 887 ident: bib0022 article-title: An unsupervised approach based on the generalized gaussian model to automatic change detection in multitemporal SAR images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 52 start-page: 4781 year: 2014 end-page: 4798 ident: bib0025 article-title: A change detector based on an optimization with polarimetric SAR imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 86 start-page: 2278 year: 1998 end-page: 2324 ident: bib0028 article-title: Gradient-based learning applied to document recognition publication-title: Proc. IEEE – volume: 18 start-page: 2661 year: 2009 end-page: 2672 ident: bib0002 article-title: Iterative weighted maximum likelihood denoising with probabilistic patch-based weights publication-title: IEEE Trans. Image Process. – start-page: 341 year: 2012 end-page: 349 ident: bib0032 article-title: Image denoising and inpainting with deep neural networks publication-title: Proc. Adv. Neural Inf. Process. Syst. (NIPS) – volume: 4 start-page: 503 year: 2007 ident: bib0050 article-title: Modified fisher’s linear discriminant analysis for hyperspectral imagery publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 54 start-page: 6746 year: 2016 end-page: 6756 ident: bib0016 article-title: Region-based change detection for polarimetric SAR images using wishart mixture models publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 29 start-page: 1700 year: 2007 end-page: 1715 ident: bib0049 article-title: General tensor discriminant analysis and gabor features for gait recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 65 start-page: 5078 year: 2017 end-page: 5091 ident: bib0013 article-title: On multiple covariance equality testing with application to SAR change detection publication-title: IEEE Trans. Signal Process. – volume: 11 start-page: 3371 year: 2010 end-page: 3408 ident: bib0029 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – start-page: 791 year: 2007 end-page: 798 ident: bib0027 article-title: Restricted Boltzmann machines for collaborative filtering publication-title: Proc. of the 24th Int. Conf. on Machine Learning – volume: 54 start-page: 3953 year: 2016 end-page: 3966 ident: bib0014 article-title: Polarimetric SAR change detection with the complex Hotelling-Lawley trace statistic publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 56 start-page: 4002 year: 2018 end-page: 4015 ident: bib0048 article-title: Unsupervised change detection based on hybrid conditional random field model for high spatial resolution remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 45 start-page: 1432 year: 2007 end-page: 1445 ident: bib0010 article-title: A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 1 start-page: 6 year: 2013 end-page: 43 ident: bib0042 article-title: A tutorial on synthetic aperture radar publication-title: IEEE Geosci. Remote Sens. Mag. – start-page: 151 year: 2011 end-page: 161 ident: bib0039 article-title: Semi-supervised recursive autoencoders for predicting sentiment distributions publication-title: Proc. of the Conf. on Empirical Methods in Natural Language Proc. – volume: 41 start-page: 4 year: 2003 end-page: 19 ident: bib0011 article-title: A test statistic in the complex wishart distribution and its application to change detection in polarimetric SAR data publication-title: IEEE Trans. Geosci. Remote Sens. – volume: vol. 72 year: 2011 ident: bib0051 article-title: Sparse Autoencoder – volume: 57 start-page: 473 year: 2019 end-page: 481 ident: bib0015 article-title: Bivariate gamma distribution for wavelength-resolution SAR change detection publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 57 start-page: 924 year: 2019 end-page: 935 ident: bib0034 article-title: Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 16 start-page: 1796 year: 2007 end-page: 1806 ident: bib0009 article-title: Bivariate gamma distributions for image registration and change detection publication-title: IEEE Trans. Image Process. – volume: 51 start-page: 2986 year: 2013 end-page: 3000 ident: bib0012 article-title: A new polarimetric change detector in radar imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 31 start-page: 855 year: 2009 end-page: 868 ident: bib0030 article-title: A novel connectionist system for unconstrained handwriting recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 66 start-page: 213 year: 2017 end-page: 228 ident: bib0037 article-title: Deep learning and mapping based ternary change detection for information unbalanced images publication-title: Pattern Recognit. – volume: 46 start-page: 2126 year: 2008 end-page: 2136 ident: bib0021 article-title: Spectral clustering ensemble applied to SAR image segmentation publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 48 start-page: 685 year: 2015 end-page: 695 ident: bib0023 article-title: A new patch based change detector for polarimetric SAR data publication-title: Pattern Recognit. – volume: 26 start-page: 764 year: 1988 end-page: 773 ident: bib0052 article-title: A statistical and geometrical edge detector for sar images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 7 start-page: 3297 year: 2014 end-page: 3317 ident: bib0044 article-title: Unsupervised change detection in SAR image based on gauss-log ratio image fusion and compressed projection publication-title: IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. – year: 2009 ident: bib0041 article-title: Polarimetric Radar Imaging: From Basics to Applications – volume: 61 start-page: 309 year: 2017 end-page: 326 ident: bib0018 article-title: Unsupervised saliency-guided SAR image change detection publication-title: Pattern Recognit. – year: 2007 ident: bib0038 article-title: Efficient learning of sparse representations with an energy-based model publication-title: Proc. Adv. Neural Inf. Process. Syst. (NIPS) – volume: 14 start-page: 2310 year: 2017 end-page: 2314 ident: bib0033 article-title: Generative adversarial networks for change detection in multispectral imagery publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 53 start-page: 2664 year: 2015 end-page: 2682 ident: bib0006 article-title: Building change detection in multitemporal very high resolution SAR images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 56 start-page: 3611 year: 2018 end-page: 3623 ident: bib0020 article-title: A multisquint framework for change detection in high-resolution multitemporal SAR images publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 12 start-page: 339 year: 1991 end-page: 360 ident: bib0045 article-title: Change detection in SAR imagery publication-title: Int. J. Remote Sens. – volume: 53 start-page: 4712 year: 2015 end-page: 4723 ident: bib0047 article-title: Change-detection map learning using matching pursuit publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 35 start-page: 1693 issue: 5 year: 2014 ident: 10.1016/j.patcog.2019.106971_bib0046 article-title: On the SAR change detection review and optimal decision publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2014.882030 – volume: 1 start-page: 6 issue: 1 year: 2013 ident: 10.1016/j.patcog.2019.106971_bib0042 article-title: A tutorial on synthetic aperture radar publication-title: IEEE Geosci. Remote Sens. Mag. doi: 10.1109/MGRS.2013.2248301 – year: 2012 ident: 10.1016/j.patcog.2019.106971_bib0053 – volume: 49 start-page: 1744 issue: 5 year: 2011 ident: 10.1016/j.patcog.2019.106971_bib0003 article-title: Nonlocal filtering for polarimetric SAR data: a pretest approach publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2010.2087763 – volume: 43 start-page: 2963 issue: 12 year: 2005 ident: 10.1016/j.patcog.2019.106971_bib0004 article-title: A detail-preserving scale-driven approach to change detection in multitemporal SAR images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2005.857987 – volume: 6 start-page: 772 issue: 4 year: 2009 ident: 10.1016/j.patcog.2019.106971_bib0017 article-title: Unsupervised change detection in satellite images using principal component analysis and k-means clustering publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2009.2025059 – volume: 53 start-page: 2664 issue: 5 year: 2015 ident: 10.1016/j.patcog.2019.106971_bib0006 article-title: Building change detection in multitemporal very high resolution SAR images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2363548 – volume: 7 start-page: 3297 issue: 8 year: 2014 ident: 10.1016/j.patcog.2019.106971_bib0044 article-title: Unsupervised change detection in SAR image based on gauss-log ratio image fusion and compressed projection publication-title: IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2014.2328344 – volume: 54 start-page: 6746 issue: 11 year: 2016 ident: 10.1016/j.patcog.2019.106971_bib0016 article-title: Region-based change detection for polarimetric SAR images using wishart mixture models publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2016.2590145 – volume: 43 start-page: 874 issue: 4 year: 2005 ident: 10.1016/j.patcog.2019.106971_bib0022 article-title: An unsupervised approach based on the generalized gaussian model to automatic change detection in multitemporal SAR images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2004.842441 – volume: vol. 72 year: 2011 ident: 10.1016/j.patcog.2019.106971_bib0051 – volume: 16 start-page: 1796 issue: 7 year: 2007 ident: 10.1016/j.patcog.2019.106971_bib0009 article-title: Bivariate gamma distributions for image registration and change detection publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2007.896651 – start-page: 3642 year: 2012 ident: 10.1016/j.patcog.2019.106971_bib0031 article-title: Multi-column deep neural networks for image classification – volume: 11 start-page: 3371 year: 2010 ident: 10.1016/j.patcog.2019.106971_bib0029 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – year: 2009 ident: 10.1016/j.patcog.2019.106971_bib0041 – volume: 12 start-page: 339 issue: 2 year: 1991 ident: 10.1016/j.patcog.2019.106971_bib0045 article-title: Change detection in SAR imagery publication-title: Int. J. Remote Sens. doi: 10.1080/01431169108929656 – volume: 4 start-page: 503 issue: 4 year: 2007 ident: 10.1016/j.patcog.2019.106971_bib0050 article-title: Modified fisher’s linear discriminant analysis for hyperspectral imagery publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2007.900751 – volume: 51 start-page: 2042 issue: 4 year: 2013 ident: 10.1016/j.patcog.2019.106971_bib0005 article-title: A hierarchical approach to change detection in very high resolution SAR images for surveillance applications publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2012.2223219 – start-page: 341 year: 2012 ident: 10.1016/j.patcog.2019.106971_bib0032 article-title: Image denoising and inpainting with deep neural networks – volume: 57 start-page: 473 issue: 1 year: 2019 ident: 10.1016/j.patcog.2019.106971_bib0015 article-title: Bivariate gamma distribution for wavelength-resolution SAR change detection publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2856926 – volume: 5 start-page: 1087 issue: 4 year: 2012 ident: 10.1016/j.patcog.2019.106971_bib0007 article-title: Multitemporal spaceborne SAR data for urban change detection in china publication-title: IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. doi: 10.1109/JSTARS.2012.2201135 – year: 2007 ident: 10.1016/j.patcog.2019.106971_bib0038 article-title: Efficient learning of sparse representations with an energy-based model – start-page: 165 issue: 2 year: 1980 ident: 10.1016/j.patcog.2019.106971_bib0001 article-title: Digital image enhancement and noise filtering by use of local statistics publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.1980.4766994 – volume: 30 start-page: 818 issue: 3 year: 2019 ident: 10.1016/j.patcog.2019.106971_bib0036 article-title: Local restricted convolutional neural network for change detection in polarimetric SAR images publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2018.2847309 – volume: 86 start-page: 2278 issue: 11 year: 1998 ident: 10.1016/j.patcog.2019.106971_bib0028 article-title: Gradient-based learning applied to document recognition publication-title: Proc. IEEE doi: 10.1109/5.726791 – volume: 109 start-page: 209 issue: 3 year: 2014 ident: 10.1016/j.patcog.2019.106971_bib0055 article-title: Sparse representation based fisher discrimination dictionary learning for image classification publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-014-0722-8 – volume: 18 start-page: 2661 issue: 12 year: 2009 ident: 10.1016/j.patcog.2019.106971_bib0002 article-title: Iterative weighted maximum likelihood denoising with probabilistic patch-based weights publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2009.2029593 – volume: 22 start-page: 98 issue: 1 year: 2014 ident: 10.1016/j.patcog.2019.106971_bib0019 article-title: Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2013.2249072 – volume: 48 start-page: 685 issue: 3 year: 2015 ident: 10.1016/j.patcog.2019.106971_bib0023 article-title: A new patch based change detector for polarimetric SAR data publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2014.09.027 – volume: 66 start-page: 213 issue: C year: 2017 ident: 10.1016/j.patcog.2019.106971_bib0037 article-title: Deep learning and mapping based ternary change detection for information unbalanced images publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.01.002 – volume: 107 start-page: 64 year: 2015 ident: 10.1016/j.patcog.2019.106971_bib0024 article-title: Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2015.02.008 – start-page: 151 year: 2011 ident: 10.1016/j.patcog.2019.106971_bib0039 article-title: Semi-supervised recursive autoencoders for predicting sentiment distributions – volume: 56 start-page: 4002 issue: 7 year: 2018 ident: 10.1016/j.patcog.2019.106971_bib0048 article-title: Unsupervised change detection based on hybrid conditional random field model for high spatial resolution remote sensing imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2819367 – volume: 61 start-page: 309 year: 2017 ident: 10.1016/j.patcog.2019.106971_bib0018 article-title: Unsupervised saliency-guided SAR image change detection publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2016.07.040 – volume: 56 start-page: 3611 issue: 6 year: 2018 ident: 10.1016/j.patcog.2019.106971_bib0020 article-title: A multisquint framework for change detection in high-resolution multitemporal SAR images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2805471 – volume: 37 start-page: 2363 issue: 5 year: 1999 ident: 10.1016/j.patcog.2019.106971_bib0040 article-title: Polarimetric SAR speckle filtering and its implication for classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.789635 – volume: 27 start-page: 125 issue: 1 year: 2016 ident: 10.1016/j.patcog.2019.106971_bib0035 article-title: Change detection in synthetic aperture radar images based on deep neural networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2435783 – volume: 41 start-page: 4 issue: 1 year: 2003 ident: 10.1016/j.patcog.2019.106971_bib0011 article-title: A test statistic in the complex wishart distribution and its application to change detection in polarimetric SAR data publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2002.808066 – volume: 31 start-page: 855 issue: 5 year: 2009 ident: 10.1016/j.patcog.2019.106971_bib0030 article-title: A novel connectionist system for unconstrained handwriting recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2008.137 – volume: 29 start-page: 1700 issue: 10 year: 2007 ident: 10.1016/j.patcog.2019.106971_bib0049 article-title: General tensor discriminant analysis and gabor features for gait recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2007.1096 – volume: 45 start-page: 1432 issue: 5 year: 2007 ident: 10.1016/j.patcog.2019.106971_bib0010 article-title: A new statistical similarity measure for change detection in multitemporal SAR images and its extension to multiscale change analysis publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2007.893568 – volume: 26 start-page: 764 issue: 6 year: 1988 ident: 10.1016/j.patcog.2019.106971_bib0052 article-title: A statistical and geometrical edge detector for sar images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.7708 – volume: 45 start-page: 2927 issue: 8 year: 2012 ident: 10.1016/j.patcog.2019.106971_bib0008 article-title: Gradual land cover change detection based on multitemporal fraction images publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2012.02.004 – volume: 57 start-page: 924 issue: 2 year: 2019 ident: 10.1016/j.patcog.2019.106971_bib0034 article-title: Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2863224 – volume: 18 start-page: 1527 issue: 7 year: 2006 ident: 10.1016/j.patcog.2019.106971_bib0026 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. doi: 10.1162/neco.2006.18.7.1527 – start-page: 1 year: 2007 ident: 10.1016/j.patcog.2019.106971_bib0054 article-title: Trace ratio vs. ratio trace for dimensionality reduction – volume: 46 start-page: 2126 issue: 7 year: 2008 ident: 10.1016/j.patcog.2019.106971_bib0021 article-title: Spectral clustering ensemble applied to SAR image segmentation publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2008.918647 – volume: 40 start-page: 618 issue: 3 year: 2002 ident: 10.1016/j.patcog.2019.106971_bib0043 article-title: Change detection for thematic mapping by means of airborne multitemporal polarimetric SAR imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2002.1000322 – volume: 14 start-page: 2310 issue: 2 year: 2017 ident: 10.1016/j.patcog.2019.106971_bib0033 article-title: Generative adversarial networks for change detection in multispectral imagery publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2017.2762694 – volume: 65 start-page: 5078 issue: 19 year: 2017 ident: 10.1016/j.patcog.2019.106971_bib0013 article-title: On multiple covariance equality testing with application to SAR change detection publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2017.2712124 – volume: 54 start-page: 3953 issue: 7 year: 2016 ident: 10.1016/j.patcog.2019.106971_bib0014 article-title: Polarimetric SAR change detection with the complex Hotelling-Lawley trace statistic publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2016.2532320 – volume: 52 start-page: 4781 issue: 8 year: 2014 ident: 10.1016/j.patcog.2019.106971_bib0025 article-title: A change detector based on an optimization with polarimetric SAR imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2013.2284510 – volume: 51 start-page: 2986 issue: 5 year: 2013 ident: 10.1016/j.patcog.2019.106971_bib0012 article-title: A new polarimetric change detector in radar imagery publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2012.2211883 – start-page: 791 year: 2007 ident: 10.1016/j.patcog.2019.106971_bib0027 article-title: Restricted Boltzmann machines for collaborative filtering – volume: 53 start-page: 4712 issue: 8 year: 2015 ident: 10.1016/j.patcog.2019.106971_bib0047 article-title: Change-detection map learning using matching pursuit publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2015.2407953 |
| SSID | ssj0017142 |
| Score | 2.535563 |
| Snippet | •The original SAE is expanded to suit with the multiplicative noise in SAR change detection.•The features extracted by SFAE are more discriminative than the... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 106971 |
| SubjectTerms | Change detection Fisher criterion Stacked autoencoder (SAE) Stacked fisher autoencoder (SFAE) Synthetic aperture radar (SAR) |
| Title | Stacked Fisher autoencoder for SAR change detection |
| URI | https://dx.doi.org/10.1016/j.patcog.2019.106971 |
| Volume | 96 |
| WOSCitedRecordID | wos000487569700003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-5142 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017142 issn: 0031-3203 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwELbo0kMvLX2ptAX5wG0VFD8S28dVC6IVQohStJwix87CIpSs2GzFz-84dh6FCsqhlySaxE6UbzKecT7PILTDrJAstzyKwTuOeAoBijQpiVKTaNBnaXhTDObsUBwdyelUHYfyVsumnIAoS3l7qxb_FWqQAdhu6ewT4O46BQEcA-iwBdhh-0_Ag_sIX6Yd-6LmY72qK5es0uWMcJTCH5OTsNp3bIu6IWKVQw_1uEm46Ra5BGZR_5_-cL5qptFBUS511UtDbH9x3Q6DjpEz15WXg1b04q-BAXwO-2V3Isw6EDVgcARLykjEaMyGllQNTSHEmspXV7lnpf2EwdXuAkab6sLx69Ruf_mfSbHvDFYdhbBlp11lvpfM9ZL5Xp6hdSoSJUdoffJtb_q9-60kCPfp48PDt2spG8Lf_af5u68y8D9ON9DLEDjgiQf8NVoryjfoVVuUAwcb_RaxgD_2-OMB_hjwx4A_9vjjDv936Of-3umXgyhUxogME7SOjDJiRkiuUgn-r7ZJbmeC0lhzw6hOTC5jURA-k6IwacELTm1qaMLyNKGJ1IS9R6OyKosPCBPubL42SkhoHVtlqM3Br4YwV1tl-SZi7TvITEgb76qXXGcPIbCJoq7VwqdNeeR60b7eLLh-3qXLQGcebPnxiXf6hF70-vwZjeqbVbGFnptf9Xx5sx0U5jf0V3iH |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Stacked+Fisher+autoencoder+for+SAR+change+detection&rft.jtitle=Pattern+recognition&rft.au=Liu%2C+Ganchao&rft.au=Li%2C+Lingling&rft.au=Jiao%2C+Licheng&rft.au=Dong%2C+Yongsheng&rft.date=2019-12-01&rft.issn=0031-3203&rft.volume=96&rft.spage=106971&rft_id=info:doi/10.1016%2Fj.patcog.2019.106971&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_patcog_2019_106971 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0031-3203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0031-3203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0031-3203&client=summon |