A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 29; H. 3; S. 545 - 559
Hauptverfasser:	Liu, Jia, Gong, Maoguo, Qin, Kai, Zhang, Puzhao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Change detection Coupling Couplings deep neural network denoising autoencoder optical images Feature extraction Image acquisition Image detection Laser radar Neural networks Optical imaging Optical measuring instruments Optical properties Optical sensors Radar Radar imaging Sensors synthetic aperture radar images
ISSN:	2162-237X, 2162-2388
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.
AbstractList	We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.
Author	Zhang, Puzhao Gong, Maoguo Liu, Jia Qin, Kai
Author_xml	– sequence: 1 givenname: Jia surname: Liu fullname: Liu, Jia organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China – sequence: 2 givenname: Maoguo orcidid: 0000-0002-0415-8556 surname: Gong fullname: Gong, Maoguo email: gong@ieee.org organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China – sequence: 3 givenname: Kai surname: Qin fullname: Qin, Kai organization: School of Computer Science and Information Technology, RMIT University, Melbourne, VIC, Australia – sequence: 4 givenname: Puzhao surname: Zhang fullname: Zhang, Puzhao organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28026789$$D View this record in MEDLINE/PubMed
BookMark	eNp9kV1LHDEYhYNYqlX_gIIEeuPNbvPh5OPSbm0VlhWshd6FdyfvrLPOTqbJTEv_fTPu6oUXDYG8Cc85hHM-kP02tEjIKWdTzpn99LBYzL9PBeNqKpRUQug9cii4EhMhjdl_nfXPA3KS0prlpVihLu17ciAME0obe0jWV_QLYkdnof0dmqGvQwtNvg1dU7crusD-T4hPtAqRzh6hXWHGeyxHjn6GhJ7m4SY_xbDCFsOQ6F3X12U2gdbTe_AQ6e0GVpiOybsKmoQnu_OI_Ph6_TC7mczvvt3OruaT8pLrfsJBW4NQCKOsEcZXUEjGKymYLiFvrpcoS2-9LDyCNYpLI5YcrOJeVQblEbnY-nYx_Bow9W5TpxKbBp7_57gppBztTUY_vkHXYYg5geQEY8ZaZrXI1PmOGpYb9K6L9QbiX_eSYgbMFihjSCli5cq6hzGjPkLdOM7c2Jl77syNnbldZ1kq3khf3P8rOtuKakR8FWhtC1FY-Q9LiqEG
CODEN	ITNNAL
CitedBy_id	crossref_primary_10_5772_acrt_02 crossref_primary_10_1109_TNNLS_2022_3184414 crossref_primary_10_1109_LGRS_2019_2915251 crossref_primary_10_1016_j_cviu_2019_07_003 crossref_primary_10_1016_j_jag_2022_103127 crossref_primary_10_1007_s11432_020_3077_5 crossref_primary_10_1109_TGRS_2024_3390206 crossref_primary_10_1109_LGRS_2022_3217348 crossref_primary_10_1016_j_isprsjprs_2022_01_004 crossref_primary_10_1109_TGRS_2024_3403727 crossref_primary_10_1109_JSTARS_2025_3542469 crossref_primary_10_1109_LGRS_2020_3037930 crossref_primary_10_1109_TIP_2022_3154922 crossref_primary_10_1109_TNNLS_2024_3401696 crossref_primary_10_1109_LGRS_2021_3070145 crossref_primary_10_1109_JSTARS_2025_3582803 crossref_primary_10_1109_LGRS_2018_2843385 crossref_primary_10_3390_rs16040721 crossref_primary_10_1109_JSTARS_2021_3120381 crossref_primary_10_1109_TNNLS_2019_2920374 crossref_primary_10_1007_s12145_022_00914_4 crossref_primary_10_1016_j_jag_2023_103635 crossref_primary_10_1109_JSTARS_2024_3415171 crossref_primary_10_1016_j_isprsjprs_2024_01_002 crossref_primary_10_1109_JSTARS_2025_3542487 crossref_primary_10_1109_TNNLS_2019_2946869 crossref_primary_10_1109_JSTARS_2021_3069242 crossref_primary_10_1016_j_neucom_2019_12_104 crossref_primary_10_1109_JSTARS_2023_3260006 crossref_primary_10_1016_j_inffus_2024_102240 crossref_primary_10_1109_TIP_2025_3542276 crossref_primary_10_1016_j_jag_2023_103630 crossref_primary_10_3390_rs12111868 crossref_primary_10_1109_LGRS_2024_3370548 crossref_primary_10_1109_TGRS_2020_3026099 crossref_primary_10_1109_TGRS_2022_3224293 crossref_primary_10_3390_s25123740 crossref_primary_10_3390_rs14030722 crossref_primary_10_1109_TGRS_2023_3264811 crossref_primary_10_1109_TNNLS_2019_2910571 crossref_primary_10_1038_s41598_024_68129_9 crossref_primary_10_1016_j_isprsjprs_2019_09_016 crossref_primary_10_1109_JSTARS_2024_3461988 crossref_primary_10_1109_TGRS_2022_3228776 crossref_primary_10_1016_j_isprsjprs_2023_11_004 crossref_primary_10_1109_JSTARS_2020_3046838 crossref_primary_10_1016_j_dsp_2021_103250 crossref_primary_10_1109_TGRS_2017_2739800 crossref_primary_10_1109_ACCESS_2024_3520428 crossref_primary_10_1109_ACCESS_2025_3559992 crossref_primary_10_3390_ijgi10090591 crossref_primary_10_1109_TGRS_2019_2956756 crossref_primary_10_1109_TGRS_2024_3381196 crossref_primary_10_1109_TGRS_2020_3000296 crossref_primary_10_1155_2022_3404858 crossref_primary_10_1109_ACCESS_2025_3552444 crossref_primary_10_1109_TGRS_2025_3544651 crossref_primary_10_1080_10106049_2024_2329673 crossref_primary_10_1109_TGRS_2024_3381752 crossref_primary_10_3390_rs16193624 crossref_primary_10_1007_s13042_023_01880_z crossref_primary_10_1109_LGRS_2024_3496567 crossref_primary_10_1109_TGRS_2025_3584073 crossref_primary_10_1109_LGRS_2022_3173300 crossref_primary_10_1109_MGRS_2021_3088865 crossref_primary_10_1109_TGRS_2023_3334471 crossref_primary_10_1109_JSTARS_2022_3192251 crossref_primary_10_1109_TGRS_2022_3190504 crossref_primary_10_3390_rs14184646 crossref_primary_10_3390_rs15153896 crossref_primary_10_1109_TGRS_2021_3053571 crossref_primary_10_1007_s11220_019_0252_0 crossref_primary_10_1016_j_patcog_2025_111355 crossref_primary_10_1109_JSTARS_2025_3540041 crossref_primary_10_3390_rs11212593 crossref_primary_10_1016_j_isprsjprs_2025_03_008 crossref_primary_10_1007_s12517_020_05717_9 crossref_primary_10_1109_TNNLS_2021_3079627 crossref_primary_10_1016_j_asoc_2025_112725 crossref_primary_10_1016_j_patcog_2020_107598 crossref_primary_10_1109_JSTARS_2020_2964409 crossref_primary_10_1109_JSTARS_2021_3113831 crossref_primary_10_1109_JSTARS_2024_3373201 crossref_primary_10_1109_TGRS_2019_2944419 crossref_primary_10_1016_j_jag_2021_102638 crossref_primary_10_1109_JSTARS_2019_2892951 crossref_primary_10_1016_j_knosys_2024_112159 crossref_primary_10_3390_rs16081387 crossref_primary_10_1109_TGRS_2022_3200684 crossref_primary_10_1109_TGRS_2022_3221489 crossref_primary_10_1109_TGRS_2023_3294300 crossref_primary_10_1109_TGRS_2023_3278739 crossref_primary_10_1016_j_isprsjprs_2019_12_002 crossref_primary_10_3390_rs12172669 crossref_primary_10_3390_electronics12183766 crossref_primary_10_1109_JSTARS_2025_3588154 crossref_primary_10_1080_01431161_2020_1826066 crossref_primary_10_1109_LGRS_2018_2869608 crossref_primary_10_3390_rs13244971 crossref_primary_10_3390_rs15030588 crossref_primary_10_1007_s12524_021_01476_6 crossref_primary_10_1080_01431161_2018_1547934 crossref_primary_10_1109_JSTARS_2022_3174780 crossref_primary_10_1111_phor_12495 crossref_primary_10_1109_JSTARS_2024_3522350 crossref_primary_10_1109_TGRS_2024_3402391 crossref_primary_10_1109_TIP_2025_3539461 crossref_primary_10_1109_TNNLS_2020_3015858 crossref_primary_10_1109_TIP_2020_3017339 crossref_primary_10_3390_rs16203852 crossref_primary_10_1016_j_isprsjprs_2024_12_010 crossref_primary_10_1080_2150704X_2021_1892851 crossref_primary_10_1002_jbio_202300321 crossref_primary_10_3390_rs14040841 crossref_primary_10_3390_rs12223815 crossref_primary_10_3390_rs13234918 crossref_primary_10_1016_j_jag_2023_103399 crossref_primary_10_1109_TGRS_2021_3064501 crossref_primary_10_3390_rs14143323 crossref_primary_10_1016_j_jag_2021_102615 crossref_primary_10_1109_TGRS_2023_3348878 crossref_primary_10_1109_TGRS_2025_3526211 crossref_primary_10_1016_j_displa_2024_102840 crossref_primary_10_3390_rs13234802 crossref_primary_10_1109_JSTARS_2020_3037893 crossref_primary_10_1109_TGRS_2025_3604532 crossref_primary_10_1016_j_neucom_2024_128080 crossref_primary_10_1109_TGRS_2023_3294884 crossref_primary_10_3390_rs16061077 crossref_primary_10_1109_LGRS_2020_2988032 crossref_primary_10_1109_TGRS_2020_2986239 crossref_primary_10_1007_s11432_022_3588_0 crossref_primary_10_1016_j_dsp_2024_104825 crossref_primary_10_1109_TNNLS_2023_3334764 crossref_primary_10_3390_electronics13050867 crossref_primary_10_1109_JSTARS_2021_3073994 crossref_primary_10_1109_MGRS_2024_3412770 crossref_primary_10_1002_gj_4677 crossref_primary_10_1109_TGRS_2019_2930322 crossref_primary_10_3390_rs14040871 crossref_primary_10_1109_JSTARS_2024_3387093 crossref_primary_10_1016_j_rse_2024_114373 crossref_primary_10_3390_rs17152581 crossref_primary_10_1109_LGRS_2018_2878420 crossref_primary_10_1109_TGRS_2022_3199502 crossref_primary_10_1109_JSEN_2021_3119747 crossref_primary_10_1080_01431161_2021_1941390 crossref_primary_10_1038_s41598_022_16329_6 crossref_primary_10_1080_17538947_2024_2347457 crossref_primary_10_1109_TIP_2019_2933747 crossref_primary_10_1016_j_isprsjprs_2018_09_002 crossref_primary_10_1109_LGRS_2019_2953754 crossref_primary_10_1109_TIP_2021_3093766 crossref_primary_10_1016_j_rse_2021_112308 crossref_primary_10_3390_rs13071236 crossref_primary_10_1109_JSTARS_2021_3077545 crossref_primary_10_1007_s00521_022_07637_z crossref_primary_10_1109_TGRS_2025_3542814 crossref_primary_10_3390_rs13224597 crossref_primary_10_1109_TGRS_2022_3209972 crossref_primary_10_1016_j_isprsjprs_2023_03_004 crossref_primary_10_1002_aisy_201900088 crossref_primary_10_1109_ACCESS_2019_2957148 crossref_primary_10_1016_j_patcog_2022_108845 crossref_primary_10_1109_TGRS_2024_3382474 crossref_primary_10_1109_TGRS_2017_2750220 crossref_primary_10_3390_rs11020142 crossref_primary_10_1109_TVT_2022_3230265 crossref_primary_10_1016_j_isprsjprs_2025_02_004 crossref_primary_10_1109_TGRS_2019_2948659 crossref_primary_10_3390_rs14071552 crossref_primary_10_3390_rs14215622 crossref_primary_10_1109_TGRS_2019_2913095 crossref_primary_10_3390_rs11151836 crossref_primary_10_1109_JSEN_2021_3085995 crossref_primary_10_3390_rs14153734 crossref_primary_10_1109_TGRS_2022_3158741 crossref_primary_10_1016_j_jag_2023_103361 crossref_primary_10_1016_j_isprsjprs_2025_05_029 crossref_primary_10_1109_MAES_2021_3117369 crossref_primary_10_1016_j_jag_2023_103366 crossref_primary_10_1109_TGRS_2021_3061686 crossref_primary_10_1007_s12145_019_00380_5 crossref_primary_10_1016_j_ecoinf_2021_101310 crossref_primary_10_3390_rs13010078 crossref_primary_10_1016_j_ipm_2021_102726 crossref_primary_10_1109_TGRS_2021_3109957 crossref_primary_10_1109_TGRS_2025_3598766 crossref_primary_10_3390_rs16111961 crossref_primary_10_1080_07038992_2021_1925530 crossref_primary_10_1016_j_patcog_2025_112394 crossref_primary_10_1109_JSTARS_2022_3215773 crossref_primary_10_1109_LGRS_2023_3266891 crossref_primary_10_1109_TGRS_2023_3330494 crossref_primary_10_1109_ACCESS_2018_2889326 crossref_primary_10_1109_JSTARS_2020_3026838 crossref_primary_10_1109_JSTARS_2024_3422687 crossref_primary_10_3390_rs13163336 crossref_primary_10_3390_rs12152460 crossref_primary_10_1109_JPROC_2022_3219376 crossref_primary_10_1016_j_isprsjprs_2021_05_019 crossref_primary_10_3390_rs14030438 crossref_primary_10_3390_rs16050804 crossref_primary_10_1109_TGRS_2024_3519195 crossref_primary_10_3390_rs12091404 crossref_primary_10_1109_LGRS_2018_2889307 crossref_primary_10_1109_TGRS_2022_3154390 crossref_primary_10_1109_TGRS_2024_3516001 crossref_primary_10_1109_LGRS_2018_2868704 crossref_primary_10_3390_rs16111850 crossref_primary_10_3390_rs12101662 crossref_primary_10_1080_17538947_2024_2398051 crossref_primary_10_1109_TGRS_2024_3480091 crossref_primary_10_1111_tgis_13133 crossref_primary_10_1109_LGRS_2019_2955309 crossref_primary_10_3390_rs14184478 crossref_primary_10_1109_JSTARS_2024_3464735 crossref_primary_10_1109_TGRS_2021_3055584 crossref_primary_10_1016_j_isprsjprs_2023_04_001 crossref_primary_10_3390_rs13183750 crossref_primary_10_1007_s00521_020_05008_0 crossref_primary_10_1016_j_engappai_2024_108446 crossref_primary_10_1109_JSTARS_2020_2974276 crossref_primary_10_1109_TGRS_2023_3314217 crossref_primary_10_1109_TGRS_2024_3421581 crossref_primary_10_3390_rs14041000 crossref_primary_10_1016_j_isprsjprs_2025_08_029 crossref_primary_10_3390_rs12020205 crossref_primary_10_1109_LGRS_2019_2916601 crossref_primary_10_1016_j_jag_2022_102769 crossref_primary_10_1109_LGRS_2022_3154745 crossref_primary_10_1016_j_isprsjprs_2023_04_008 crossref_primary_10_1016_j_ophoto_2023_100044 crossref_primary_10_3390_rs13173394 crossref_primary_10_1109_JSTARS_2019_2915842 crossref_primary_10_1016_j_isprsjprs_2023_09_012 crossref_primary_10_3390_rs11111292 crossref_primary_10_1109_TGRS_2018_2872509 crossref_primary_10_1134_S2635167624601190 crossref_primary_10_1109_TNNLS_2023_3242075 crossref_primary_10_3390_rs15092433 crossref_primary_10_1109_TNNLS_2020_3016321 crossref_primary_10_1117_1_JRS_16_046509 crossref_primary_10_1109_TNNLS_2025_3570425 crossref_primary_10_1016_j_cageo_2022_105249 crossref_primary_10_1109_TGRS_2019_2931620 crossref_primary_10_1109_TAES_2025_3529431 crossref_primary_10_1109_TGRS_2023_3235981 crossref_primary_10_1587_transinf_2020EDL8129 crossref_primary_10_1109_LGRS_2023_3300308 crossref_primary_10_1109_TGRS_2023_3281792 crossref_primary_10_1109_LGRS_2024_3415819 crossref_primary_10_1109_LGRS_2021_3111040 crossref_primary_10_1109_TGRS_2024_3456548 crossref_primary_10_1109_TGRS_2024_3362728 crossref_primary_10_1109_TNNLS_2022_3172183 crossref_primary_10_1016_j_neunet_2019_01_004 crossref_primary_10_1109_TGRS_2025_3605961 crossref_primary_10_1109_TGRS_2024_3524639 crossref_primary_10_1109_TGRS_2025_3591814 crossref_primary_10_1109_TGRS_2025_3600456 crossref_primary_10_3390_rs15153742 crossref_primary_10_1016_j_jag_2024_103969 crossref_primary_10_1016_j_inffus_2024_102313 crossref_primary_10_1109_TNNLS_2021_3056238 crossref_primary_10_1109_TGRS_2023_3277496 crossref_primary_10_3390_rs15030621 crossref_primary_10_3390_rs10081228 crossref_primary_10_1109_JSTARS_2020_3009116 crossref_primary_10_1109_JSTARS_2025_3591834 crossref_primary_10_1080_10095020_2024_2446307 crossref_primary_10_1109_TGRS_2021_3097717 crossref_primary_10_3390_ijgi11070385 crossref_primary_10_1109_JSTARS_2019_2934602 crossref_primary_10_1109_LGRS_2023_3309301 crossref_primary_10_1109_LGRS_2021_3085022 crossref_primary_10_3390_rs11030240 crossref_primary_10_1109_TGRS_2020_3013673 crossref_primary_10_1016_j_isprsjprs_2021_07_007 crossref_primary_10_1109_JSTARS_2024_3416183 crossref_primary_10_3390_rs15010045 crossref_primary_10_1109_TNNLS_2017_2676130 crossref_primary_10_1080_01431161_2024_2343139 crossref_primary_10_3390_rs12183057 crossref_primary_10_1109_ACCESS_2018_2849110 crossref_primary_10_1109_TGRS_2024_3363431 crossref_primary_10_1109_TGRS_2022_3229027 crossref_primary_10_1109_TGRS_2023_3241436 crossref_primary_10_1109_TGRS_2021_3078741 crossref_primary_10_1109_TGRS_2023_3320805 crossref_primary_10_1080_10095020_2022_2085633 crossref_primary_10_1109_JSTARS_2024_3424831 crossref_primary_10_3390_rs12233922 crossref_primary_10_1109_LGRS_2023_3312734 crossref_primary_10_1016_j_asoc_2020_106510 crossref_primary_10_1080_17538947_2024_2316109 crossref_primary_10_3390_rs15010151 crossref_primary_10_1109_TGRS_2021_3056196 crossref_primary_10_1109_TGRS_2025_3578386 crossref_primary_10_3390_rs14133100 crossref_primary_10_1109_JSTARS_2024_3479703 crossref_primary_10_1109_TGRS_2021_3066432 crossref_primary_10_1109_TGRS_2022_3161337 crossref_primary_10_1109_TGRS_2020_3001584 crossref_primary_10_1080_01431161_2019_1708507 crossref_primary_10_1109_JSTARS_2020_2983993 crossref_primary_10_1109_JSTARS_2023_3333959 crossref_primary_10_1109_JSTARS_2023_3347571 crossref_primary_10_1109_TGRS_2021_3110998 crossref_primary_10_1038_s41598_025_07252_7 crossref_primary_10_1080_01431161_2024_2421935 crossref_primary_10_1109_TAI_2024_3357667 crossref_primary_10_1109_TIE_2020_2982085 crossref_primary_10_1109_TGRS_2021_3106381 crossref_primary_10_3390_rs14143394 crossref_primary_10_1109_JSTARS_2025_3574173 crossref_primary_10_1109_TGRS_2021_3071347 crossref_primary_10_3390_rs16214109 crossref_primary_10_1109_TGRS_2020_2981051 crossref_primary_10_1016_j_inffus_2023_102123 crossref_primary_10_32604_cmc_2020_012364 crossref_primary_10_1109_LGRS_2022_3201003 crossref_primary_10_1109_TGRS_2024_3515258 crossref_primary_10_1109_TPAMI_2023_3237896 crossref_primary_10_1109_LGRS_2022_3201925 crossref_primary_10_1109_TGRS_2022_3140404 crossref_primary_10_1016_j_asr_2025_08_045 crossref_primary_10_1016_j_cviu_2024_104253 crossref_primary_10_1109_TGRS_2019_2916212 crossref_primary_10_1111_exsy_12398 crossref_primary_10_1007_s40031_024_01084_1 crossref_primary_10_1109_TGRS_2020_2968098 crossref_primary_10_1109_TGRS_2023_3346879 crossref_primary_10_3390_rs12101688 crossref_primary_10_3390_rs16132355 crossref_primary_10_3390_rs14184570 crossref_primary_10_1109_TNNLS_2022_3157689 crossref_primary_10_1038_s41598_024_79266_6 crossref_primary_10_1007_s11063_020_10211_0 crossref_primary_10_1007_s12559_021_09968_w crossref_primary_10_1109_LGRS_2017_2738149 crossref_primary_10_1016_j_rsase_2024_101168 crossref_primary_10_1109_TGRS_2021_3113912
Cites_doi	10.1109/TFUZZ.2013.2249072 10.1109/TNN.2006.875978 10.1109/TGRS.2013.2281391 10.1080/01431161.2013.805282 10.1109/TGRS.2004.834630 10.1109/LGRS.2011.2167211 10.1109/TGRS.2008.916476 10.1016/j.jag.2011.10.013 10.1162/neco.2006.18.7.1527 10.1109/TIP.2014.2387013 10.1038/nature14539 10.1177/001316448104100307 10.1109/LGRS.2013.2250908 10.4236/ars.2012.13008 10.1109/TIP.2010.2045070 10.1109/TGRS.2004.842441 10.1109/TIP.2012.2219547 10.1109/TGRS.2006.885408 10.1109/TIP.2008.916047 10.1109/TPAMI.2010.37 10.1109/TGRS.2013.2288271 10.1109/TGRS.2006.876288 10.1109/TPAMI.2013.50 10.1016/j.sigpro.2014.09.009 10.1016/j.isprsjprs.2015.02.005 10.1109/TGRS.2005.857987 10.1109/TNNLS.2015.2469673 10.1109/TGRS.2004.835304 10.1109/TGRS.2009.2038274 10.1109/TGRS.2014.2374218 10.1109/TNNLS.2014.2359471 10.1109/ICASSP.2015.7178223 10.1109/TGRS.2008.916201 10.1016/j.patcog.2014.03.025 10.1109/TGRS.2010.2045506 10.1109/TIP.2004.838698 10.1109/TNNLS.2015.2435783 10.1109/TNNLS.2014.2307532 10.1109/TIP.2006.888195 10.1016/j.asoc.2015.10.044 10.1109/TNNLS.2014.2336852 10.1109/TIP.2013.2259833 10.1109/TGRS.2013.2240692 10.1109/TIP.2011.2170702 10.1080/01431168908903939 10.1109/5.726791 10.1145/1390156.1390294 10.1109/TGRS.2007.893568 10.1109/TIP.2002.999678
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018
DBID	97E RIA RIE AAYXX CITATION NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8
DOI	10.1109/TNNLS.2016.2636227
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic
DatabaseTitle	CrossRef PubMed Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Materials Business File Aerospace Database Engineered Materials Abstracts Biotechnology Research Abstracts Chemoreception Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts Neurosciences Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts Corrosion Abstracts MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic Materials Research Database PubMed
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	2162-2388
EndPage	559
ExternalDocumentID	28026789 10_1109_TNNLS_2016_2636227 7795259
Genre	orig-research Research Support, Non-U.S. Gov't Journal Article
GrantInformation_xml	– fundername: National Program for Support of Top-Notch Young Professionals of China – fundername: Specialized Research Fund for the Doctoral Program of Higher Education grantid: 20130203110011 – fundername: National Natural Science Foundation of China grantid: 61422209 funderid: 10.13039/501100001809
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF M43 MS~ O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION NPM RIG 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8
ID	FETCH-LOGICAL-c417t-1a798ea52869828dfa5301f3207ca7ca17be3cd9d35dea9861382b1a961d6f8e3
IEDL.DBID	RIE
ISICitedReferencesCount	341
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000426344600004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2162-237X
IngestDate	Thu Oct 02 06:13:55 EDT 2025 Sun Oct 05 00:17:05 EDT 2025 Thu Apr 03 06:49:58 EDT 2025 Sat Nov 29 01:39:56 EST 2025 Tue Nov 18 22:11:20 EST 2025 Wed Aug 27 02:52:37 EDT 2025
IsPeerReviewed	false
IsScholarly	true
Issue	3
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c417t-1a798ea52869828dfa5301f3207ca7ca17be3cd9d35dea9861382b1a961d6f8e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-0415-8556
PMID	28026789
PQID	2008990972
PQPubID	85436
PageCount	15
ParticipantIDs	ieee_primary_7795259 proquest_miscellaneous_1853352868 proquest_journals_2008990972 crossref_primary_10_1109_TNNLS_2016_2636227 pubmed_primary_28026789 crossref_citationtrail_10_1109_TNNLS_2016_2636227
PublicationCentury	2000
PublicationDate	2018-03-01
PublicationDateYYYYMMDD	2018-03-01
PublicationDate_xml	– month: 03 year: 2018 text: 2018-03-01 day: 01
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: Piscataway
PublicationTitle	IEEE transaction on neural networks and learning systems
PublicationTitleAbbrev	TNNLS
PublicationTitleAlternate	IEEE Trans Neural Netw Learn Syst
PublicationYear	2018
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref57 ref13 ref56 ref12 ref15 ref14 ref52 ref55 ref11 ref10 ref17 ref16 ref19 ref18 ref50 ref46 ref45 ref47 ref41 jensen (ref35) 1987; 53 rifai (ref43) 2011 ref49 ref8 hou (ref48) 2015; 26 ref7 ref9 ref4 ref3 ref6 ref5 ref40 bengio (ref53) 2007 ref34 ref37 ref36 ref31 ref30 ref33 ref32 bazaraa (ref54) 2013 ref2 ref39 ref38 chandar (ref51) 2013 ref24 ref23 ref26 ref25 vincent (ref42) 2010; 11 ref20 ref22 ref21 ref28 ref27 ref29 gong (ref1) 2008; 37 lee (ref44) 2007
References_xml	– ident: ref29 doi: 10.1109/TFUZZ.2013.2249072 – ident: ref26 doi: 10.1109/TNN.2006.875978 – volume: 37 start-page: 757 year: 2008 ident: ref1 article-title: A review of multi-temporal remote sensing data change detection algorithms publication-title: Int Arch Photogramm Remote Sens Spatial Inf Sci – ident: ref7 doi: 10.1109/TGRS.2013.2281391 – ident: ref36 doi: 10.1080/01431161.2013.805282 – ident: ref4 doi: 10.1109/TGRS.2004.834630 – start-page: 833 year: 2011 ident: ref43 article-title: Contractive auto-encoders: Explicit invariance during feature extraction publication-title: Proc 28th Int Conf Mach Learn – ident: ref22 doi: 10.1109/LGRS.2011.2167211 – ident: ref32 doi: 10.1109/TGRS.2008.916476 – start-page: 1 year: 2013 ident: ref51 article-title: Multilingual deep learning publication-title: Proc NIPS – ident: ref37 doi: 10.1016/j.jag.2011.10.013 – ident: ref40 doi: 10.1162/neco.2006.18.7.1527 – ident: ref16 doi: 10.1109/TIP.2014.2387013 – ident: ref52 doi: 10.1038/nature14539 – ident: ref57 doi: 10.1177/001316448104100307 – ident: ref31 doi: 10.1109/LGRS.2013.2250908 – ident: ref5 doi: 10.4236/ars.2012.13008 – ident: ref21 doi: 10.1109/TIP.2010.2045070 – ident: ref23 doi: 10.1109/TGRS.2004.842441 – year: 2013 ident: ref54 publication-title: Nonlinear Programming Thory and Algorithms – ident: ref56 doi: 10.1109/TIP.2012.2219547 – ident: ref18 doi: 10.1109/TGRS.2006.885408 – ident: ref14 doi: 10.1109/TIP.2008.916047 – ident: ref9 doi: 10.1109/TPAMI.2010.37 – ident: ref13 doi: 10.1109/TGRS.2013.2288271 – start-page: 153 year: 2007 ident: ref53 article-title: Greedy layer-wise training of deep networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref24 doi: 10.1109/TGRS.2006.876288 – ident: ref41 doi: 10.1109/TPAMI.2013.50 – ident: ref17 doi: 10.1016/j.sigpro.2014.09.009 – ident: ref34 doi: 10.1016/j.isprsjprs.2015.02.005 – ident: ref11 doi: 10.1109/TGRS.2005.857987 – ident: ref46 doi: 10.1109/TNNLS.2015.2469673 – ident: ref20 doi: 10.1109/TGRS.2004.835304 – ident: ref6 doi: 10.1109/TGRS.2009.2038274 – start-page: 873 year: 2007 ident: ref44 article-title: Sparse deep belief net model for visual area V2 publication-title: Proc Adv Neural Inf Process Syst – ident: ref50 doi: 10.1109/TGRS.2014.2374218 – ident: ref47 doi: 10.1109/TNNLS.2014.2359471 – ident: ref33 doi: 10.1109/ICASSP.2015.7178223 – ident: ref30 doi: 10.1109/TGRS.2008.916201 – ident: ref45 doi: 10.1016/j.patcog.2014.03.025 – ident: ref25 doi: 10.1109/TGRS.2010.2045506 – ident: ref2 doi: 10.1109/TIP.2004.838698 – volume: 11 start-page: 3371 year: 2010 ident: ref42 article-title: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion publication-title: J Mach Learn Res – ident: ref38 doi: 10.1109/TNNLS.2015.2435783 – ident: ref49 doi: 10.1109/TNNLS.2014.2307532 – ident: ref8 doi: 10.1109/TIP.2006.888195 – volume: 53 start-page: 521 year: 1987 ident: ref35 article-title: Inland wetland change detection using aircraft MSS data publication-title: Photogram Eng Remote Sens – ident: ref15 doi: 10.1016/j.asoc.2015.10.044 – volume: 26 start-page: 1275 year: 2015 ident: ref48 article-title: Blind image quality assessment via deep learning publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2014.2336852 – ident: ref27 doi: 10.1109/TIP.2013.2259833 – ident: ref10 doi: 10.1109/TGRS.2013.2240692 – ident: ref28 doi: 10.1109/TIP.2011.2170702 – ident: ref3 doi: 10.1080/01431168908903939 – ident: ref39 doi: 10.1109/5.726791 – ident: ref55 doi: 10.1145/1390156.1390294 – ident: ref12 doi: 10.1109/TGRS.2007.893568 – ident: ref19 doi: 10.1109/TIP.2002.999678
SSID	ssj0000605649
Score	2.6576676
Snippet	We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	545
SubjectTerms	Change detection Coupling Couplings deep neural network denoising autoencoder optical images Feature extraction Image acquisition Image detection Laser radar Neural networks Optical imaging Optical measuring instruments Optical properties Optical sensors Radar Radar imaging Sensors synthetic aperture radar images
Title	A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images
URI	https://ieeexplore.ieee.org/document/7795259 https://www.ncbi.nlm.nih.gov/pubmed/28026789 https://www.proquest.com/docview/2008990972 https://www.proquest.com/docview/1853352868
Volume	29
WOSCitedRecordID	wos000426344600004&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: PRVIEE databaseName: IEEE Xplore customDbUrl: eissn: 2162-2388 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000605649 issn: 2162-237X databaseCode: RIE dateStart: 20120101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Jb9UwEB61FQcuFChLoFRG4gZpYyfxciwPqiJVAUGR3i1ybKcCQfL0lv5-ZpxFHAAJKQdLsRPLM_Z8tmfmA3hVtJJzayJNqqdrxjzFbZBLG7TthdNlcG1MmX-lqkovl-bTHryZY2FCCNH5LJxSMd7l-97t6KjsTClTIlzfh32l1BCrNZ-nZIjLZUS7gkuRilwtpxiZzJxdV9XVF3LkkqdC4potiHxPaKJfIoL330xS5Fj5O9yMZufi8P86fB_ujfCSnQ_68AD2QvcQDifqBjbO5CP4fs7ehbBii767HZUPmy36HQXo3rBqcA5niGjZEH-A1bfRa6tjb9HweYaFS3Kl6VEDQ7_bsI-reCzObOfZZ-vtmn34iYvV5hF8vXh_vbhMR9qF1BVcbVNuldHBlkJLg_sx39oSV4E2F5lyFh-umpA7b3xe-mCNlpTGsEGBS-5lq0P-GA66vgtPgRWU3y7PPa4clpBfkxVKWu68C7y0RZMAn0a-dmNOcqLG-FHHvUlm6ii4mgRXj4JL4PXcZjVk5Phn7SMSy1xzlEgCx5OA63HSbiIjJxpno0QCL-fXON3oDsXGsawJ3lBCHKkTeDIoxvztSZ-e_fmfz-Eu9kwPDmzHcLBd78ILuONut9826xPU6aU-iTr9C_Vu7rg
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VgkQvLVBa0hYwEjdIGzuJH8eyUG3FEhAs0t4ix3YQCJLVPvr7O3Ye4gBISDlYip1YnrHnsz0zH8DLrOaUahVoUq2_Zkxj3AaZuELbnhmZO1OHlPkzURRysVCfduD1GAvjnAvOZ-7cF8Ndvm3N1h-VXQihcoTrd-BunmWMdtFa44lKgsicB7zLKGcxS8ViiJJJ1MW8KGZfvCsXP2ccV23m6feY9ARMnuL9N6MUWFb-DjiD4bk6-L8uP4D9HmCSy04jHsKOax7BwUDeQPq5fAg_Lslb55Zk0jY3vfphs0m79SG630jRuYcTxLSki0DA6pvgt9WQN2j6LMHC1DvTtKiDrt2uycdlOBgnurHks7Z6Ra5_4XK1fgxfr97NJ9O4J16ITUbFJqZaKOl0ziRXuCOztc5xHahTlgij8aGicqmxyqa5dVpJ7hMZVihyTi2vpUuPYLdpG_cESOYz3KWpxbVDe-xXJZngmhprHM11VkVAh5EvTZ-V3JNj_CzD7iRRZRBc6QVX9oKL4NXYZtnl5Phn7UMvlrFmL5EIzgYBl_20XQdOTjTPSrAIXoyvccL5WxQdxrL0AMenxOEyguNOMcZvD_p08ud_Pof70_mHWTm7Lt6fwh72UnbubGewu1lt3VO4Z24239erZ0GzbwHlBPEX
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=A+Deep+Convolutional+Coupling+Network+for+Change+Detection+Based+on+Heterogeneous+Optical+and+Radar+Images&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Liu%2C+Jia&rft.au=Gong%2C+Maoguo&rft.au=Qin%2C+Kai&rft.au=Zhang%2C+Puzhao&rft.date=2018-03-01&rft.pub=IEEE&rft.issn=2162-237X&rft.volume=29&rft.issue=3&rft.spage=545&rft.epage=559&rft_id=info:doi/10.1109%2FTNNLS.2016.2636227&rft_id=info%3Apmid%2F28026789&rft.externalDocID=7795259
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-237X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-237X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-237X&client=summon