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Change Detection in Dual-Temporal Remote Sensing Data Based on a Lightweight Siamese Network with Effective Preprocessing
Change detection (CD) is a crucial application in the field of remote sensing. Most current CD methods are based on deep learning and revolve around multispectral data. However, a common issue arising from these methods is the large number of model parameters due to the high dimensionality of the da...
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| creator | Huang, Yankun Wei, Maosheng Ge, Baoyu Zhang, Yun Ji, Zhenyuan |
| description | Change detection (CD) is a crucial application in the field of remote sensing. Most current CD methods are based on deep learning and revolve around multispectral data. However, a common issue arising from these methods is the large number of model parameters due to the high dimensionality of the data channels. In this paper, we propose a lightweight Siamese network structure that minimally incorporates conventional convolutional layers. Additionally, in terms of data preprocessing, we select the R, G, and B channels of multispectral data for dehazing processing, and employ the processed data as inputs to the network. Experimental results illustrate the outstanding effectiveness and efficiency of the proposed method. |
| doi_str_mv | 10.1109/IGARSS53475.2024.10642488 |
| format | conference_proceeding |
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Most current CD methods are based on deep learning and revolve around multispectral data. However, a common issue arising from these methods is the large number of model parameters due to the high dimensionality of the data channels. In this paper, we propose a lightweight Siamese network structure that minimally incorporates conventional convolutional layers. Additionally, in terms of data preprocessing, we select the R, G, and B channels of multispectral data for dehazing processing, and employ the processed data as inputs to the network. Experimental results illustrate the outstanding effectiveness and efficiency of the proposed method.</description><subject>change detection (CD)</subject><subject>Data models</subject><subject>Data preprocessing</subject><subject>Deep learning</subject><subject>Feature extraction</subject><subject>lightweight network</subject><subject>Network architecture</subject><subject>Remote sensing</subject><issn>2153-7003</issn><isbn>9798350360325</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNqFj91Kw0AUhFdBsNa-gRfHB0jcn2yTXGpTf6CINL0vh3qSrCbZsLsa-vY2oNfezAzM8MEwdit4LATP716e7rdlqVWS6lhymcSCLxOZZNkZW-RpninN1ZIrqc_ZTAqtopRzdcmuvP84hUxyPmPHVYN9TVBQoEMwtgfTQ_GFbbSjbrAOW9hSZwNBSb03fQ0FBoQH9PQOpzXCxtRNGGlSKA125AleKYzWfcJoQgPrqprQ3wRvjgZnD-Qn0DW7qLD1tPj1Obt5XO9Wz5Ehov3gTIfuuP-7pP6pfwA_71Fp</recordid><startdate>20240707</startdate><enddate>20240707</enddate><creator>Huang, Yankun</creator><creator>Wei, Maosheng</creator><creator>Ge, Baoyu</creator><creator>Zhang, Yun</creator><creator>Ji, Zhenyuan</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20240707</creationdate><title>Change Detection in Dual-Temporal Remote Sensing Data Based on a Lightweight Siamese Network with Effective Preprocessing</title><author>Huang, Yankun ; Wei, Maosheng ; Ge, Baoyu ; Zhang, Yun ; Ji, Zhenyuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_106424883</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>change detection (CD)</topic><topic>Data models</topic><topic>Data preprocessing</topic><topic>Deep learning</topic><topic>Feature extraction</topic><topic>lightweight network</topic><topic>Network architecture</topic><topic>Remote sensing</topic><toplevel>online_resources</toplevel><creatorcontrib>Huang, Yankun</creatorcontrib><creatorcontrib>Wei, Maosheng</creatorcontrib><creatorcontrib>Ge, Baoyu</creatorcontrib><creatorcontrib>Zhang, Yun</creatorcontrib><creatorcontrib>Ji, Zhenyuan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Huang, Yankun</au><au>Wei, Maosheng</au><au>Ge, Baoyu</au><au>Zhang, Yun</au><au>Ji, Zhenyuan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Change Detection in Dual-Temporal Remote Sensing Data Based on a Lightweight Siamese Network with Effective Preprocessing</atitle><btitle>IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium</btitle><stitle>IGARSS</stitle><date>2024-07-07</date><risdate>2024</risdate><spage>8397</spage><epage>8400</epage><pages>8397-8400</pages><eissn>2153-7003</eissn><eisbn>9798350360325</eisbn><abstract>Change detection (CD) is a crucial application in the field of remote sensing. Most current CD methods are based on deep learning and revolve around multispectral data. However, a common issue arising from these methods is the large number of model parameters due to the high dimensionality of the data channels. In this paper, we propose a lightweight Siamese network structure that minimally incorporates conventional convolutional layers. Additionally, in terms of data preprocessing, we select the R, G, and B channels of multispectral data for dehazing processing, and employ the processed data as inputs to the network. Experimental results illustrate the outstanding effectiveness and efficiency of the proposed method.</abstract><pub>IEEE</pub><doi>10.1109/IGARSS53475.2024.10642488</doi></addata></record> |
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| identifier | EISSN: 2153-7003 |
| ispartof | IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 2024, p.8397-8400 |
| issn | 2153-7003 |
| language | eng |
| recordid | cdi_ieee_primary_10642488 |
| source | IEEE Xplore All Conference Series |
| subjects | change detection (CD) Data models Data preprocessing Deep learning Feature extraction lightweight network Network architecture Remote sensing |
| title | Change Detection in Dual-Temporal Remote Sensing Data Based on a Lightweight Siamese Network with Effective Preprocessing |
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