FCCDN: Feature constraint network for VHR image change detection
Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective superv...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Pan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Umfang: |
19 |
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| Übergeordnetes Werk: |
Enthalten in: In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid - Skiadopoulos, V. ELSEVIER, 2013, official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:187 ; year:2022 ; pages:101-119 ; extent:19 |
| Links: |
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| DOI / URN: |
10.1016/j.isprsjprs.2022.02.021 |
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| Katalog-ID: |
ELV057452598 |
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| 520 | |a Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. | ||
| 520 | |a Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. | ||
| 650 | 7 | |a Deep learning |2 Elsevier | |
| 650 | 7 | |a Feature constraint |2 Elsevier | |
| 650 | 7 | |a Change detection |2 Elsevier | |
| 700 | 1 | |a Zhang, Bing |4 oth | |
| 700 | 1 | |a Hong, Danfeng |4 oth | |
| 700 | 1 | |a Chen, Zhengchao |4 oth | |
| 700 | 1 | |a Yang, Xuan |4 oth | |
| 700 | 1 | |a Li, Baipeng |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Skiadopoulos, V. ELSEVIER |t In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid |d 2013 |d official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) |g Amsterdam [u.a.] |w (DE-627)ELV016966376 |
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10.1016/j.isprsjprs.2022.02.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001744.pica (DE-627)ELV057452598 (ELSEVIER)S0924-2716(22)00063-6 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Chen, Pan verfasserin aut FCCDN: Feature constraint network for VHR image change detection 2022transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Deep learning Elsevier Feature constraint Elsevier Change detection Elsevier Zhang, Bing oth Hong, Danfeng oth Chen, Zhengchao oth Yang, Xuan oth Li, Baipeng oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:187 year:2022 pages:101-119 extent:19 https://doi.org/10.1016/j.isprsjprs.2022.02.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 187 2022 101-119 19 |
| spelling |
10.1016/j.isprsjprs.2022.02.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001744.pica (DE-627)ELV057452598 (ELSEVIER)S0924-2716(22)00063-6 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Chen, Pan verfasserin aut FCCDN: Feature constraint network for VHR image change detection 2022transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Deep learning Elsevier Feature constraint Elsevier Change detection Elsevier Zhang, Bing oth Hong, Danfeng oth Chen, Zhengchao oth Yang, Xuan oth Li, Baipeng oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:187 year:2022 pages:101-119 extent:19 https://doi.org/10.1016/j.isprsjprs.2022.02.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 187 2022 101-119 19 |
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10.1016/j.isprsjprs.2022.02.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001744.pica (DE-627)ELV057452598 (ELSEVIER)S0924-2716(22)00063-6 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Chen, Pan verfasserin aut FCCDN: Feature constraint network for VHR image change detection 2022transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Deep learning Elsevier Feature constraint Elsevier Change detection Elsevier Zhang, Bing oth Hong, Danfeng oth Chen, Zhengchao oth Yang, Xuan oth Li, Baipeng oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:187 year:2022 pages:101-119 extent:19 https://doi.org/10.1016/j.isprsjprs.2022.02.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 187 2022 101-119 19 |
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10.1016/j.isprsjprs.2022.02.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001744.pica (DE-627)ELV057452598 (ELSEVIER)S0924-2716(22)00063-6 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Chen, Pan verfasserin aut FCCDN: Feature constraint network for VHR image change detection 2022transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Deep learning Elsevier Feature constraint Elsevier Change detection Elsevier Zhang, Bing oth Hong, Danfeng oth Chen, Zhengchao oth Yang, Xuan oth Li, Baipeng oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:187 year:2022 pages:101-119 extent:19 https://doi.org/10.1016/j.isprsjprs.2022.02.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 187 2022 101-119 19 |
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10.1016/j.isprsjprs.2022.02.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001744.pica (DE-627)ELV057452598 (ELSEVIER)S0924-2716(22)00063-6 DE-627 ger DE-627 rakwb eng 570 VZ 610 VZ 620 VZ 52.57 bkl 53.36 bkl Chen, Pan verfasserin aut FCCDN: Feature constraint network for VHR image change detection 2022transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. Deep learning Elsevier Feature constraint Elsevier Change detection Elsevier Zhang, Bing oth Hong, Danfeng oth Chen, Zhengchao oth Yang, Xuan oth Li, Baipeng oth Enthalten in Elsevier Skiadopoulos, V. ELSEVIER In Vitro and In Vivo UV Light Skin Protection by an Antioxidant Derivative of NSAID Tolfenamic Acid 2013 official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS) Amsterdam [u.a.] (DE-627)ELV016966376 volume:187 year:2022 pages:101-119 extent:19 https://doi.org/10.1016/j.isprsjprs.2022.02.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_70 52.57 Energiespeicherung VZ 53.36 Energiedirektumwandler elektrische Energiespeicher VZ AR 187 2022 101-119 19 |
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Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. |
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Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. |
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Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. |
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