Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification
Land cover classification has the goal to attribute each pixel of high-resolution remoste sensing image with planimetric category labels (such as vegetation, building, and water). In recent years, many serial deep-learning architectures (features are delivered through a single path, such as in <i...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10356620/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850040774274580480 |
|---|---|
| author | Zongqian Zhan Zirou Xiong Xin Huang Chun Yang Yi Liu Xin Wang |
| author_facet | Zongqian Zhan Zirou Xiong Xin Huang Chun Yang Yi Liu Xin Wang |
| author_sort | Zongqian Zhan |
| collection | DOAJ |
| description | Land cover classification has the goal to attribute each pixel of high-resolution remoste sensing image with planimetric category labels (such as vegetation, building, and water). In recent years, many serial deep-learning architectures (features are delivered through a single path, such as in <italic>ResNet</italic>, <italic>MobileNet</italic>, and <italic>Segformer</italic>) based on convolutional neural networks and attention mechanisms have been widely explored in land cover classification. However, high-resolution remote sensing images typically have abundant textual details, variable scales in objects, large intraclass variance, and similar interclass correlation, which bring challenges to land cover classification. In this work, we present two pluggable modules to further boost serial learning architecture: first, to cope with ambiguous boundaries caused by lost details and fragmented segmentation stemmed from scale variances, a combination of spatial attention and channel attention is proposed for multiscale feature reconstruction (MSFR); second, to mitigate the classification error caused by intraclass variance and interclass correlation, we explore an interclass attention weighting (ICAW) module, which builds feature vectors for each category, and applies a multihead attention model to capture self-attention dependence among different categories. The experimental results demonstrate that the proposed modules are feasible to the existing serial learning architectures and can improve overall accuracy (OA) by 5.64% on the ISPRS Vaihingen two-dimensional dataset (using <italic>ResNet50</italic> as a backbone); in particular, the OA values are 80.68% and 86.32% before and after using the proposed modules, respectively. In addition, compared with other state-of-the-art models, our method can achieve similar or even better classification results, yet offer superior inference performance. |
| format | Article |
| id | doaj-art-c8046fbb9cf8459c95e36a38b5633f1e |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-c8046fbb9cf8459c95e36a38b5633f1e2025-08-20T02:55:58ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-01171921193710.1109/JSTARS.2023.334245310356620Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover ClassificationZongqian Zhan0https://orcid.org/0000-0002-1324-8430Zirou Xiong1Xin Huang2Chun Yang3https://orcid.org/0000-0001-5262-3048Yi Liu4https://orcid.org/0000-0002-6741-6815Xin Wang5https://orcid.org/0000-0002-5132-4465School of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaHangzhou SensingX Technology Company Ltd., Hangzhou, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaLand cover classification has the goal to attribute each pixel of high-resolution remoste sensing image with planimetric category labels (such as vegetation, building, and water). In recent years, many serial deep-learning architectures (features are delivered through a single path, such as in <italic>ResNet</italic>, <italic>MobileNet</italic>, and <italic>Segformer</italic>) based on convolutional neural networks and attention mechanisms have been widely explored in land cover classification. However, high-resolution remote sensing images typically have abundant textual details, variable scales in objects, large intraclass variance, and similar interclass correlation, which bring challenges to land cover classification. In this work, we present two pluggable modules to further boost serial learning architecture: first, to cope with ambiguous boundaries caused by lost details and fragmented segmentation stemmed from scale variances, a combination of spatial attention and channel attention is proposed for multiscale feature reconstruction (MSFR); second, to mitigate the classification error caused by intraclass variance and interclass correlation, we explore an interclass attention weighting (ICAW) module, which builds feature vectors for each category, and applies a multihead attention model to capture self-attention dependence among different categories. The experimental results demonstrate that the proposed modules are feasible to the existing serial learning architectures and can improve overall accuracy (OA) by 5.64% on the ISPRS Vaihingen two-dimensional dataset (using <italic>ResNet50</italic> as a backbone); in particular, the OA values are 80.68% and 86.32% before and after using the proposed modules, respectively. In addition, compared with other state-of-the-art models, our method can achieve similar or even better classification results, yet offer superior inference performance.https://ieeexplore.ieee.org/document/10356620/Interclass attentionland cover classificationmultiscale feature reconstruction (MSFR)remote sensing imagesemantic segmentation |
| spellingShingle | Zongqian Zhan Zirou Xiong Xin Huang Chun Yang Yi Liu Xin Wang Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Interclass attention land cover classification multiscale feature reconstruction (MSFR) remote sensing image semantic segmentation |
| title | Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification |
| title_full | Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification |
| title_fullStr | Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification |
| title_full_unstemmed | Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification |
| title_short | Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification |
| title_sort | multiscale feature reconstruction and interclass attention weighting for land cover classification |
| topic | Interclass attention land cover classification multiscale feature reconstruction (MSFR) remote sensing image semantic segmentation |
| url | https://ieeexplore.ieee.org/document/10356620/ |
| work_keys_str_mv | AT zongqianzhan multiscalefeaturereconstructionandinterclassattentionweightingforlandcoverclassification AT zirouxiong multiscalefeaturereconstructionandinterclassattentionweightingforlandcoverclassification AT xinhuang multiscalefeaturereconstructionandinterclassattentionweightingforlandcoverclassification AT chunyang multiscalefeaturereconstructionandinterclassattentionweightingforlandcoverclassification AT yiliu multiscalefeaturereconstructionandinterclassattentionweightingforlandcoverclassification AT xinwang multiscalefeaturereconstructionandinterclassattentionweightingforlandcoverclassification |