Change detection in SAR images using deep belief network: a new training approach based on morphological images
In solving change detection problem, unsupervised methods are usually preferred to their supervised counterparts due to the difficulty of producing labelled data. Nevertheless, in this paper, a supervised deep learning-based method is presented for change detection in synthetic aperture radar (SAR)...
Saved in:
| Published in: | IET image processing Vol. 13; no. 12; pp. 2255 - 2264 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
The Institution of Engineering and Technology
17.10.2019
|
| Subjects: | |
| ISSN: | 1751-9659, 1751-9667 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In solving change detection problem, unsupervised methods are usually preferred to their supervised counterparts due to the difficulty of producing labelled data. Nevertheless, in this paper, a supervised deep learning-based method is presented for change detection in synthetic aperture radar (SAR) images. A Deep Belief Network (DBN) was employed as the deep architecture in the proposed method, and the training process of this network included unsupervised feature learning followed by supervised network fine-tuning. From a general perspective, the trained DBN produces a change detection map as the output. Studies on DBNs demonstrate that they do not produce ideal output without a proper dataset for training. Therefore, the proposed method in this study provided a dataset with an appropriate data volume and diversity for training the DBN using the input images and those obtained from applying the morphological operators on them. The great computational volume and the time-consuming nature of simulation are the drawbacks of deep learning-based algorithms. To overcome such disadvantages, a method was introduced to greatly reduce computations without compromising the performance of the trained DBN. Experimental results indicated that the proposed method had an acceptable implementation time in addition to its desirable performance and high accuracy. |
|---|---|
| AbstractList | In solving change detection problem, unsupervised methods are usually preferred to their supervised counterparts due to the difficulty of producing labelled data. Nevertheless, in this paper, a supervised deep learning-based method is presented for change detection in synthetic aperture radar (SAR) images. A Deep Belief Network (DBN) was employed as the deep architecture in the proposed method, and the training process of this network included unsupervised feature learning followed by supervised network fine-tuning. From a general perspective, the trained DBN produces a change detection map as the output. Studies on DBNs demonstrate that they do not produce ideal output without a proper dataset for training. Therefore, the proposed method in this study provided a dataset with an appropriate data volume and diversity for training the DBN using the input images and those obtained from applying the morphological operators on them. The great computational volume and the time-consuming nature of simulation are the drawbacks of deep learning-based algorithms. To overcome such disadvantages, a method was introduced to greatly reduce computations without compromising the performance of the trained DBN. Experimental results indicated that the proposed method had an acceptable implementation time in addition to its desirable performance and high accuracy. |
| Author | Kaabi, Hooman Akbarizadeh, Gholamreza Samadi, Farnaam |
| Author_xml | – sequence: 1 givenname: Farnaam surname: Samadi fullname: Samadi, Farnaam organization: Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran – sequence: 2 givenname: Gholamreza surname: Akbarizadeh fullname: Akbarizadeh, Gholamreza email: g.akbari@scu.ac.ir organization: Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran – sequence: 3 givenname: Hooman surname: Kaabi fullname: Kaabi, Hooman organization: Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran |
| BookMark | eNqFkMtOwzAQRS0EErTwAez8Aym2Eztxd1DxqIQE4rGOJs40NaR25ARV_XscFbFgUVYz0sy5Y58JOXbeISGXnM04y_SVxSGxXZgJxouZEllxRM54LnmilcqPf3upT8mk7z8Yk5oV8oz4xRpcg7TGAc1gvaPW0dfrF2o30GBPv3rrmjjFjlbYWlxRh8PWh885hdhu6RDAunEHui54MGtaQY81jUkbH7q1b31jDbQ_gefkZAVtjxc_dUre727fFg_J49P9cnH9mJg0zVSiABXwIpUA2gipKy1NwSUqFKwQuZKizvNK1AwAMsErxSpj0ho4QM2yWqdTwve5Jvi-D7gquxBfEHYlZ-VorIzGymisHI2Vo7HI5H8YYwcYpYyfbA-S8z25tS3u_j9VLp9fxM0d4zpXEU728Lj24b-Ci2IOHPsGKV6Xug |
| CitedBy_id | crossref_primary_10_1049_ipr2_12057 crossref_primary_10_1007_s11227_020_03604_4 crossref_primary_10_1049_iet_ipr_2018_6040 crossref_primary_10_1007_s13369_020_04481_y crossref_primary_10_1109_TGRS_2020_3003264 crossref_primary_10_1080_01431161_2023_2244641 crossref_primary_10_1007_s13369_019_04151_8 crossref_primary_10_1016_j_ecoinf_2021_101310 crossref_primary_10_1109_JSTARS_2023_3301158 crossref_primary_10_1109_LGRS_2021_3050891 crossref_primary_10_1080_01431161_2024_2365818 crossref_primary_10_1016_j_isprsjprs_2020_03_011 crossref_primary_10_1049_iet_ipr_2019_0873 crossref_primary_10_1109_TGRS_2021_3053257 crossref_primary_10_1109_TIP_2022_3154922 crossref_primary_10_1109_JSTARS_2022_3215773 crossref_primary_10_1080_01431161_2025_2505257 crossref_primary_10_1155_2020_8886932 crossref_primary_10_3390_app12147282 crossref_primary_10_3390_rs15194802 crossref_primary_10_1049_iet_com_2019_0980 crossref_primary_10_1109_JSTARS_2020_2992230 crossref_primary_10_1016_j_dsp_2023_104331 crossref_primary_10_1177_1687814020930469 crossref_primary_10_3390_rs14030438 crossref_primary_10_1109_TIM_2025_3551842 crossref_primary_10_3389_fnbot_2021_751037 crossref_primary_10_1007_s00521_022_07928_5 crossref_primary_10_1049_iet_ipr_2019_1398 crossref_primary_10_3233_JIFS_220689 crossref_primary_10_1007_s13369_019_04235_5 crossref_primary_10_1109_TGRS_2023_3263563 crossref_primary_10_1109_JSTARS_2024_3464735 crossref_primary_10_3390_s21248290 crossref_primary_10_1049_ipr2_12078 crossref_primary_10_1080_01431161_2023_2182652 crossref_primary_10_1016_j_dsp_2021_103250 crossref_primary_10_1049_joe_2020_0086 crossref_primary_10_1109_JSTARS_2023_3280741 crossref_primary_10_1109_JSTARS_2020_3024002 crossref_primary_10_1109_TGRS_2020_3034102 crossref_primary_10_1109_JSTARS_2020_3041783 crossref_primary_10_1109_LGRS_2021_3050746 crossref_primary_10_1080_01431161_2025_2511209 crossref_primary_10_1109_LGRS_2021_3073900 crossref_primary_10_7717_peerj_cs_894 crossref_primary_10_2478_ijssis_2025_0026 crossref_primary_10_1109_TAES_2020_2981267 crossref_primary_10_1155_2022_3404858 crossref_primary_10_1080_01431161_2025_2529005 crossref_primary_10_1109_TGRS_2022_3168331 crossref_primary_10_1155_2021_5522894 crossref_primary_10_1109_JSTARS_2025_3557434 crossref_primary_10_1109_LGRS_2020_3022804 crossref_primary_10_1109_LSP_2024_3396657 crossref_primary_10_1109_TAES_2024_3421908 crossref_primary_10_1007_s11760_025_04147_y crossref_primary_10_1080_01431161_2025_2518507 crossref_primary_10_1049_iet_ipr_2020_1078 crossref_primary_10_1049_iet_rsn_2020_0160 crossref_primary_10_1080_01431161_2025_2516691 crossref_primary_10_1016_j_isprsjprs_2020_10_007 crossref_primary_10_1080_14498596_2024_2384636 crossref_primary_10_1108_DTA_08_2019_0149 crossref_primary_10_3390_ijgi13040125 crossref_primary_10_1007_s00138_020_01110_4 crossref_primary_10_1109_TGRS_2020_3039738 crossref_primary_10_1109_JSTARS_2024_3471925 crossref_primary_10_1109_JSTARS_2025_3555627 crossref_primary_10_1080_2150704X_2022_2079389 crossref_primary_10_1080_01431161_2025_2557588 crossref_primary_10_1049_joe_2019_0996 crossref_primary_10_1049_iet_ipr_2018_6526 crossref_primary_10_1016_j_dsp_2021_103036 crossref_primary_10_1007_s00357_023_09448_w crossref_primary_10_1016_j_measurement_2020_108657 crossref_primary_10_1109_JSTARS_2024_3408339 crossref_primary_10_1007_s13369_020_04788_w crossref_primary_10_1016_j_measurement_2020_108417 crossref_primary_10_1049_ipr2_12523 crossref_primary_10_1155_2020_4609423 crossref_primary_10_3390_rs12132098 crossref_primary_10_4018_IJSI_293269 crossref_primary_10_1109_TETCI_2020_3041019 crossref_primary_10_3390_rs14092046 crossref_primary_10_3390_rs16203852 crossref_primary_10_1109_LGRS_2020_3040972 crossref_primary_10_1109_TPWRD_2020_3046161 crossref_primary_10_1007_s13369_019_04322_7 crossref_primary_10_3390_rs13204171 crossref_primary_10_1038_s41598_025_10972_5 crossref_primary_10_1109_TETCI_2020_3010017 crossref_primary_10_1016_j_cmpb_2019_105301 crossref_primary_10_1109_JSTARS_2023_3321769 crossref_primary_10_1109_TPWRD_2024_3421913 crossref_primary_10_3390_math10224387 crossref_primary_10_3390_rs14040957 crossref_primary_10_1016_j_jksuci_2020_10_008 crossref_primary_10_1109_JSTARS_2025_3542436 crossref_primary_10_1016_j_measurement_2020_107736 crossref_primary_10_1080_07900627_2024_2438203 crossref_primary_10_1109_JSTARS_2024_3424831 crossref_primary_10_1109_TGRS_2023_3245674 crossref_primary_10_3390_rs15153868 crossref_primary_10_1007_s10044_024_01370_0 crossref_primary_10_1109_JSTARS_2021_3088691 crossref_primary_10_1109_TIP_2025_3562054 crossref_primary_10_1049_ell2_12290 crossref_primary_10_1049_ipr2_12154 crossref_primary_10_1109_JSTARS_2024_3374054 crossref_primary_10_1007_s13369_019_04152_7 crossref_primary_10_3390_rs14040871 crossref_primary_10_1049_iet_ipr_2019_0652 crossref_primary_10_1080_22797254_2024_2372854 crossref_primary_10_1080_2150704X_2021_1897180 crossref_primary_10_3390_app142310770 crossref_primary_10_1109_TGRS_2022_3194903 crossref_primary_10_1109_ACCESS_2020_3006097 crossref_primary_10_1109_JSEN_2023_3314608 crossref_primary_10_1109_TGRS_2023_3344062 crossref_primary_10_1016_j_measurement_2019_107089 crossref_primary_10_1109_JSTARS_2025_3543591 crossref_primary_10_1049_iet_ipr_2019_0086 crossref_primary_10_1080_01431161_2023_2249595 crossref_primary_10_3390_rs12101688 crossref_primary_10_1080_01431161_2025_2467303 crossref_primary_10_1109_ACCESS_2024_3415054 crossref_primary_10_1109_JSTARS_2024_3393238 crossref_primary_10_1007_s00138_021_01171_z crossref_primary_10_1109_JSTARS_2024_3402243 |
| Cites_doi | 10.1109/TIP.2011.2170702 10.1109/TNNLS.2015.2435783 10.1080/01431161.2017.1371861 10.1117/1.1631315 10.1109/JSTARS.2013.2265697 10.1007/978-0-387-45528-0 10.1561/2200000006 10.1080/01431161.2014.916054 10.1016/j.sigpro.2009.10.018 10.1016/S0167-8655(03)00060-6 10.1109/TGRS.2012.2223219 10.1109/LGRS.2012.2189867 10.1109/ICIP.2013.6738559 10.1109/36.843009 10.1109/TNNLS.2013.2296046 10.1162/neco.2006.18.7.1527 10.1109/LGRS.2011.2167211 10.1109/JSTARS.2016.2541678 10.1007/s00500-014-1460-0 10.1109/TGRS.2004.842441 10.1109/36.905239 10.1126/science.1127647 10.1109/TGRS.2004.842478 |
| ContentType | Journal Article |
| Copyright | The Institution of Engineering and Technology 2021 The Authors. IET Image Processing published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
| Copyright_xml | – notice: The Institution of Engineering and Technology – notice: 2021 The Authors. IET Image Processing published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
| DBID | AAYXX CITATION |
| DOI | 10.1049/iet-ipr.2018.6248 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences |
| EISSN | 1751-9667 |
| EndPage | 2264 |
| ExternalDocumentID | 10_1049_iet_ipr_2018_6248 IPR2BF01976 |
| Genre | article |
| GrantInformation_xml | – fundername: Shahid Chamran University of Ahvaz grantid: 96/3/02/16670 – fundername: Shahid Chamran University of Ahvaz funderid: 97/3/02/26247 |
| GroupedDBID | 0R 24P 29I 5GY 6IK 8VB AAJGR ABPTK ACGFS ACIWK AENEX ALMA_UNASSIGNED_HOLDINGS BFFAM CS3 DU5 ESX HZ IFIPE IPLJI JAVBF LAI M43 MS O9- OCL P2P QWB RIE RNS RUI UNR ZL0 .DC 0R~ 1OC 4.4 8FE 8FG AAHHS AAHJG ABJCF ABQXS ACCFJ ACCMX ACESK ACXQS ADZOD AEEZP AEQDE AFKRA AIWBW AJBDE ALUQN ARAPS AVUZU BENPR BGLVJ CCPQU EBS EJD GROUPED_DOAJ HCIFZ HZ~ IAO ITC K1G L6V M7S MCNEO MS~ OK1 P62 PTHSS ROL S0W AAMMB AAYXX AEFGJ AFFHD AGXDD AIDQK AIDYY CITATION IDLOA PHGZM PHGZT PQGLB WIN |
| ID | FETCH-LOGICAL-c3346-6ae6a1835aa9c259b95c815e6e20827652d77b2d0aaa421b60bcc3da1aad04d93 |
| IEDL.DBID | 24P |
| ISICitedReferencesCount | 143 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000497707700024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1751-9659 |
| IngestDate | Tue Nov 18 20:42:54 EST 2025 Wed Oct 29 21:12:29 EDT 2025 Wed Jan 22 16:30:25 EST 2025 Tue Jan 05 21:44:15 EST 2021 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Keywords | supervised counterparts image classification morphological images unsupervised methods detection performance deep learning-based supervised method introduced method diversity supervised network fine-tuning deep belief network training approach radar imaging synthetic aperture radar image changes belief networks learning (artificial intelligence) change detection problems trained DBN input SAR images training process deep architecture unsupervised learning change detection map synthetic aperture radar labelled data appropriate data volume deep learning-based algorithms input images dataset |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3346-6ae6a1835aa9c259b95c815e6e20827652d77b2d0aaa421b60bcc3da1aad04d93 |
| PageCount | 10 |
| ParticipantIDs | wiley_primary_10_1049_iet_ipr_2018_6248_IPR2BF01976 iet_journals_10_1049_iet_ipr_2018_6248 crossref_primary_10_1049_iet_ipr_2018_6248 crossref_citationtrail_10_1049_iet_ipr_2018_6248 |
| ProviderPackageCode | RUI |
| PublicationCentury | 2000 |
| PublicationDate | 2019-10-17 |
| PublicationDateYYYYMMDD | 2019-10-17 |
| PublicationDate_xml | – month: 10 year: 2019 text: 2019-10-17 day: 17 |
| PublicationDecade | 2010 |
| PublicationTitle | IET image processing |
| PublicationYear | 2019 |
| Publisher | The Institution of Engineering and Technology |
| Publisher_xml | – name: The Institution of Engineering and Technology |
| References | Gong, M.; Zhao, J.; Liu, J. (C14) 2016; 27 Gong, M.; Jia, M.; Su, L. (C26) 2014; 35 Bazi, Y.; Bruzzone, L.; Melgani, F. (C2) 2005; 43 Zhang, X.; Chen, J.; Meng, H. (C15) 2013; 10 Tan, K.; Jin, X.; Plaza, A. (C7) 2016; 9 Sezgin, M.; Sankur, B. (C3) 2004; 13 Szymanski, L.; McCane, B. (C10) 2014; 25 Bengio, Y. (C11) 2009; 2 Gong, M.; Zhou, Z.; Ma, J. (C25) 2012; 21 Tan, K.; Li, E.; Du, Q. (C19) 2014; 7 Bruzzone, L.; Prieto, D.F. (C1) 2000; 38 Celik, T. (C5) 2010; 90 Liu, J.; Gong, M.; Zhao, J. (C12) 2016; 20 Rosin, P.L.; Ioannidis, E. (C24) 2003; 24 Cao, G.; Wang, B.; Xavier, H.C. (C13) 2017; 38 Hinton, G.E.; Salakhutdinov, R.R. (C22) 2006; 313 Gong, M.; Cao, Y.; Wu, Q. (C6) 2012; 9 Pesaresi, M.; Benediktsson, J.A. (C18) 2001; 39 Hinton, G.E.; Osindero, S.; Teh, Y.W. (C23) 2006; 18 Bovolo, F.; Marin, C.; Bruzzone, L. (C4) 2013; 51 Vincent, P.; Larochelle, H.; Lajoie, I. (C9) 2010; 11 Benediktsson, J.A.; Palmason, J.A.; Sveinsson, J.R. (C17) 2005; 43 2010; 11 2008 2014; 25 2006; 18 2005; 43 2006 2005 2006; 313 2000; 38 2013; 10 2017; 38 2013; 51 2004; 13 2003; 24 2016; 20 2014; 35 2001; 39 2013 2009; 2 2010; 90 2016; 27 2014; 7 2012; 21 2016; 9 2012; 9 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 Vincent P. (e_1_2_8_10_1) 2010; 11 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 e_1_2_8_18_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_14_1 e_1_2_8_15_1 Gonzalez R.C. (e_1_2_8_17_1) 2008 e_1_2_8_16_1 e_1_2_8_11_1 e_1_2_8_12_1 |
| References_xml | – volume: 2 start-page: 1 issue: 1 year: 2009 end-page: 127 ident: C11 article-title: Learning deep architectures for AI publication-title: Found. Trends® Mach. Learn. – volume: 20 start-page: 4645 issue: 12 year: 2016 end-page: 4657 ident: C12 article-title: Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images publication-title: Soft Comput. – volume: 9 start-page: 307 issue: 2 year: 2012 end-page: 311 ident: C6 article-title: A neighborhood-based ratio approach for change detection in SAR images publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 13 start-page: 146 issue: 1 year: 2004 end-page: 166 ident: C3 article-title: Survey over image thresholding techniques and quantitative performance evaluation publication-title: J. Electron. Imaging – volume: 11 start-page: 3371 issue: Dec year: 2010 end-page: 3408 ident: C9 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 21 start-page: 2141 issue: 4 year: 2012 end-page: 2151 ident: C25 article-title: Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering publication-title: IEEE Trans. Image Process. – volume: 43 start-page: 480 issue: 3 year: 2005 end-page: 491 ident: C17 article-title: Classification of hyperspectral data from urban areas based on extended morphological profiles publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 51 start-page: 2042 issue: 4 year: 2013 end-page: 2054 ident: C4 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: 18 start-page: 1527 issue: 7 year: 2006 end-page: 1554 ident: C23 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. – volume: 38 start-page: 1171 issue: 3 year: 2000 end-page: 1182 ident: C1 article-title: Automatic analysis of the difference image for unsupervised change detection publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 25 start-page: 1816 issue: 10 year: 2014 end-page: 1827 ident: C10 article-title: Deep networks are effective encoders of periodicity publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 10 start-page: 14 issue: 1 year: 2013 end-page: 18 ident: C15 article-title: A novel SAR image change detection based on graph-cut and generalized Gaussian model publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 24 start-page: 2345 issue: 14 year: 2003 end-page: 2356 ident: C24 article-title: Evaluation of global image thresholding for change detection publication-title: Pattern Recognit. Lett. – volume: 27 start-page: 125 issue: 1 year: 2016 end-page: 138 ident: C14 article-title: Change detection in synthetic aperture radar images based on deep neural networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 313 start-page: 504 issue: 5786 year: 2006 end-page: 507 ident: C22 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 90 start-page: 1471 issue: 5 year: 2010 end-page: 1485 ident: C5 article-title: A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images publication-title: Signal Process. – volume: 38 start-page: 7161 issue: 23 year: 2017 end-page: 7175 ident: C13 article-title: A new difference image creation method based on deep neural networks for change detection in remote-sensing images publication-title: Int. J. Remote Sens. – volume: 43 start-page: 874 issue: 4 year: 2005 end-page: 887 ident: C2 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: 39 start-page: 309 issue: 2 year: 2001 end-page: 320 ident: C18 article-title: A new approach for the morphological segmentation of high-resolution satellite imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 35 start-page: 4009 issue: 11–12 year: 2014 end-page: 4030 ident: C26 article-title: Detecting changes of the Yellow River estuary via SAR images based on a local fit-search model and kernel-induced graph cuts publication-title: Int. J. Remote Sens. – volume: 7 start-page: 40 issue: 1 year: 2014 end-page: 48 ident: C19 article-title: Hyperspectral image classification using band selection and morphological profiles publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 9 start-page: 3439 issue: 8 year: 2016 end-page: 3451 ident: C7 article-title: Automatic change detection in high-resolution remote sensing images by using a multiple classifier system and spectral–spatial features publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 38 start-page: 1171 issue: 3 year: 2000 end-page: 1182 article-title: Automatic analysis of the difference image for unsupervised change detection publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 10 start-page: 14 issue: 1 year: 2013 end-page: 18 article-title: A novel SAR image change detection based on graph‐cut and generalized Gaussian model publication-title: IEEE Geosci. Remote Sens. Lett. – volume: 7 start-page: 40 issue: 1 year: 2014 end-page: 48 article-title: Hyperspectral image classification using band selection and morphological profiles publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – start-page: 627 year: 2008 end-page: 628 – volume: 39 start-page: 309 issue: 2 year: 2001 end-page: 320 article-title: A new approach for the morphological segmentation of high‐resolution satellite imagery publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 11 start-page: 3371 issue: Dec year: 2010 end-page: 3408 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 313 start-page: 504 issue: 5786 year: 2006 end-page: 507 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 24 start-page: 2345 issue: 14 year: 2003 end-page: 2356 article-title: Evaluation of global image thresholding for change detection publication-title: Pattern Recognit. Lett. – start-page: 2713 year: 2013 end-page: 2717 – volume: 38 start-page: 7161 issue: 23 year: 2017 end-page: 7175 article-title: A new difference image creation method based on deep neural networks for change detection in remote‐sensing images publication-title: Int. J. Remote Sens. – volume: 35 start-page: 4009 issue: 11–12 year: 2014 end-page: 4030 article-title: Detecting changes of the Yellow River estuary via SAR images based on a local fit‐search model and kernel‐induced graph cuts publication-title: Int. J. Remote Sens. – volume: 90 start-page: 1471 issue: 5 year: 2010 end-page: 1485 article-title: A Bayesian approach to unsupervised multiscale change detection in synthetic aperture radar images publication-title: Signal Process. – volume: 20 start-page: 4645 issue: 12 year: 2016 end-page: 4657 article-title: Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images publication-title: Soft Comput. – volume: 18 start-page: 1527 issue: 7 year: 2006 end-page: 1554 article-title: A fast learning algorithm for deep belief nets publication-title: Neural Comput. – volume: 25 start-page: 1816 issue: 10 year: 2014 end-page: 1827 article-title: Deep networks are effective encoders of periodicity publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 43 start-page: 874 issue: 4 year: 2005 end-page: 887 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: 2 start-page: 1 issue: 1 year: 2009 end-page: 127 article-title: Learning deep architectures for AI publication-title: Found. Trends® Mach. Learn. – volume: 43 start-page: 480 issue: 3 year: 2005 end-page: 491 article-title: Classification of hyperspectral data from urban areas based on extended morphological profiles publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 51 start-page: 2042 issue: 4 year: 2013 end-page: 2054 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: 9 start-page: 3439 issue: 8 year: 2016 end-page: 3451 article-title: Automatic change detection in high‐resolution remote sensing images by using a multiple classifier system and spectral–spatial features publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 13 start-page: 146 issue: 1 year: 2004 end-page: 166 article-title: Survey over image thresholding techniques and quantitative performance evaluation publication-title: J. Electron. Imaging – year: 2006 – start-page: 33 year: 2005 end-page: 40 – volume: 21 start-page: 2141 issue: 4 year: 2012 end-page: 2151 article-title: Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering publication-title: IEEE Trans. Image Process. – volume: 27 start-page: 125 issue: 1 year: 2016 end-page: 138 article-title: Change detection in synthetic aperture radar images based on deep neural networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 9 start-page: 307 issue: 2 year: 2012 end-page: 311 article-title: A neighborhood‐based ratio approach for change detection in SAR images publication-title: IEEE Geosci. Remote Sens. Lett. – ident: e_1_2_8_26_1 doi: 10.1109/TIP.2011.2170702 – ident: e_1_2_8_15_1 doi: 10.1109/TNNLS.2015.2435783 – ident: e_1_2_8_14_1 doi: 10.1080/01431161.2017.1371861 – volume: 11 start-page: 3371 year: 2010 ident: e_1_2_8_10_1 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – ident: e_1_2_8_4_1 doi: 10.1117/1.1631315 – ident: e_1_2_8_20_1 doi: 10.1109/JSTARS.2013.2265697 – ident: e_1_2_8_22_1 – ident: e_1_2_8_21_1 doi: 10.1007/978-0-387-45528-0 – start-page: 627 volume-title: Digital image processing year: 2008 ident: e_1_2_8_17_1 – ident: e_1_2_8_12_1 doi: 10.1561/2200000006 – ident: e_1_2_8_27_1 doi: 10.1080/01431161.2014.916054 – ident: e_1_2_8_6_1 doi: 10.1016/j.sigpro.2009.10.018 – ident: e_1_2_8_25_1 doi: 10.1016/S0167-8655(03)00060-6 – ident: e_1_2_8_5_1 doi: 10.1109/TGRS.2012.2223219 – ident: e_1_2_8_16_1 doi: 10.1109/LGRS.2012.2189867 – ident: e_1_2_8_9_1 doi: 10.1109/ICIP.2013.6738559 – ident: e_1_2_8_2_1 doi: 10.1109/36.843009 – ident: e_1_2_8_11_1 doi: 10.1109/TNNLS.2013.2296046 – ident: e_1_2_8_24_1 doi: 10.1162/neco.2006.18.7.1527 – ident: e_1_2_8_7_1 doi: 10.1109/LGRS.2011.2167211 – ident: e_1_2_8_8_1 doi: 10.1109/JSTARS.2016.2541678 – ident: e_1_2_8_13_1 doi: 10.1007/s00500-014-1460-0 – ident: e_1_2_8_3_1 doi: 10.1109/TGRS.2004.842441 – ident: e_1_2_8_19_1 doi: 10.1109/36.905239 – ident: e_1_2_8_23_1 doi: 10.1126/science.1127647 – ident: e_1_2_8_18_1 doi: 10.1109/TGRS.2004.842478 |
| SSID | ssj0059085 |
| Score | 2.530279 |
| Snippet | In solving change detection problem, unsupervised methods are usually preferred to their supervised counterparts due to the difficulty of producing labelled... |
| SourceID | crossref wiley iet |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 2255 |
| SubjectTerms | appropriate data volume belief networks change detection map change detection problems dataset deep architecture deep belief network deep learning‐based algorithms deep learning‐based supervised method detection performance diversity image classification input images input SAR images introduced method labelled data learning (artificial intelligence) morphological images radar imaging Research Article supervised counterparts supervised network fine‐tuning synthetic aperture radar synthetic aperture radar image changes trained DBN training approach training process unsupervised learning unsupervised methods |
| Title | Change detection in SAR images using deep belief network: a new training approach based on morphological images |
| URI | http://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6248 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-ipr.2018.6248 |
| Volume | 13 |
| WOSCitedRecordID | wos000497707700024&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: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1751-9667 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0059085 issn: 1751-9659 databaseCode: WIN dateStart: 20130101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 1751-9667 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0059085 issn: 1751-9659 databaseCode: 24P dateStart: 20130101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrR3ZSsNAcLHVB1-sJ9aLfRAfhGg2x27XtyoWBSnFA_sWZo9IQNNiqv6-szkKIiiILyHsMWxmdq7szgwhh6BiaWxqPW6U8KLA-h5EoD2Zhkr63AZcloHCN2I47I3HcrRALppYmCo_xPyHm-OMUl47BgdVVSFBoxaJmNmZl01dSk_WO-FB1GuRRcZC4bZ2EI0acexqesdlVKSrJ89jOT_alKffQHxRTi3s_mqyljpn0PmX1a6SldrkpP1qj6yRBZuvk05tftKauYsNMqkiDaixs_J-Vk6znN71b2n2gkKnoO6K_BP22ilVuBqb0ry6Q35GAV8_aFNugjaJyqnTkYYipJcJ0rORszXATfIwuLy_uPLqegyeDsOIexwsBxQBMYDU6DYpGeseiy1SFA0JwePACKEC4wNAFDDFfaV1aIABGD8yMtwi7XyS221CUyUNB8GYQvxYI6SQCmIN6ICiA-OLLvEbQiS6TlbuPuI5KQ_NI5kgQhNEaOIQmjiEdsnxfMq0ytTx0-Aj11bza_HTwLAk6-8gk-vRbXA-QJNZ8J0_zdoly9gunWZkYo-0Z69vdp8s6fdZVrwelLsan4_Xw0_lrfyF |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrR3LahsxcEicQnuJ07QhadpEh5JDYdt9aCWrNyeNcahrTJqCb8vosWEhXpvYbX4_o30YQsGFktuykgZpRvOSNDMAH1GnyrrcBcJqGfDYhQFyNIHKE61C4WKhqkDhkRyPe9OpmmzBtzYWps4PsT5w85xRyWvP4P5AunY4uU-SWbhVUCx8Ts-o91nEvLcNO5y0ja9jEPNJK499Ue-0Cov0BeVFqtZ3m-rLXyCeaKdtan5qs1ZKZ9B9nunuwW5jdLJ-vUtew5Yr96HbGKCsYe_lG5jXsQbMulX1QqtkRcl-9q9ZMSOxs2T-kfwttboF0zQdl7OyfkX-lSF9PrC24ARrU5UzryUtI0izOVG0lbQNwLfwa3B5czEMmooMgUkSLgKBTiAJgRRRGXKctEpNL0od0ZRMCSnS2EqpYxsiIo8jLUJtTGIxQrQhtyo5gE45L90hsFwrK1BGkSb8OCuVVBpTg-SCkgsTyiMIW0pkpklX7hdxl1XX5lxlhNCMEJp5hGYeoUfwaT1kUefq2NT5zP9rOHa5qWNS0fXfILOryXV8PiCjWYp3_zXqFF4Ob36MstHV-PsxvKI-yuvJSL6Hzur-t_sAL8yfVbG8P6m2-CPn7v9Y |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrR3ZShxBsPAi5MUrCd72g_gQGDNHH9u-eS2KsixqwLeh-pgwoLOLu0l-3-o5FiSgEHwbpquLnqqua7qrCuAAjdDOFz6SzqiIpz6OkKONdJEZHUufSl0nCt-owaD38KCHc3De5cI09SFmP9yCZNT6Ogi4H7uiCTh5KJJZ-mlUjkNNz6R3JFPem4dFLkjXhvrOfNjp49DUW9RpkaGhvBR6drapf_yD4pV1mqfh1z5rbXT6Kx-z3FVYbp1OdtLskjWY89U6rLQOKGvFe_IFRk2uAXN-Wt_QqlhZsbuTW1Y-kdqZsHBJ_heN-jEztBxfsKq5RX7MkB7_sq7hBOtKlbNgJR0jTE8j4minaVuEX-Fn_-L-7DJqOzJENsu4jCR6iaQEBKK2FDgZLWwvEZ54Sq6EkiJ1SpnUxYjI08TI2FibOUwQXcydzr7BQjWq_AawwmgnUSWJIfp4p7TSBoVFCkEphInVJsQdJ3LblisPH_GY18fmXOdE0JwImgeC5oGgm_B9NmXc1Op4C_gwvGsldvIWYFbz9X2U-dXwNj3tk9Os5NZ_zdqHT8Pzfn5zNbjehs8EooOZTNQOLEyff_tdWLJ_puXkea_e4S-N8v7c |
| 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=Change+detection+in+SAR+images+using+deep+belief+network%3A+a+new+training+approach+based+on+morphological+images&rft.jtitle=IET+image+processing&rft.au=Samadi%2C+Farnaam&rft.au=Akbarizadeh%2C+Gholamreza&rft.au=Kaabi%2C+Hooman&rft.date=2019-10-17&rft.issn=1751-9659&rft.eissn=1751-9667&rft.volume=13&rft.issue=12&rft.spage=2255&rft.epage=2264&rft_id=info:doi/10.1049%2Fiet-ipr.2018.6248&rft.externalDBID=n%2Fa&rft.externalDocID=10_1049_iet_ipr_2018_6248 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1751-9659&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1751-9659&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1751-9659&client=summon |