Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network
Remote sensing technology has become a popular tool for crop classification, but it faces challenges in accurately identifying crops in areas with fragmented land plots and complex planting structures. To address this issue, we propose an improved method for crop identification in high-resolution re...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/21/5088 |
| _version_ | 1797631383488692224 |
|---|---|
| author | Zhu Chang Hu Li Donghua Chen Yufeng Liu Chen Zou Jian Chen Weijie Han Saisai Liu Naiming Zhang |
| author_facet | Zhu Chang Hu Li Donghua Chen Yufeng Liu Chen Zou Jian Chen Weijie Han Saisai Liu Naiming Zhang |
| author_sort | Zhu Chang |
| collection | DOAJ |
| description | Remote sensing technology has become a popular tool for crop classification, but it faces challenges in accurately identifying crops in areas with fragmented land plots and complex planting structures. To address this issue, we propose an improved method for crop identification in high-resolution remote sensing images, achieved by modifying the DeepLab V3+ semantic segmentation network. In this paper, the typical crop area in the Jianghuai watershed is taken as the experimental area, and Gaofen-2 satellite images with high spatial resolutions are used as the data source. Based on the original DeepLab V3+ model, CI and OSAVI vegetation indices are added to the input layers, and MobileNet V2 is used as the backbone network. Meanwhile, the upper sampling layer of the network is added, and the attention mechanism is added to the ASPP and the upper sampling layers. The accuracy verification of the identification results shows that the MIoU and PA of this model in the test set reach 85.63% and 95.30%, the IoU and F1_Score of wheat are 93.76% and 96.78%, and the IoU and F1_Score of rape are 74.24% and 85.51%, respectively. The identification accuracy of this model is significantly better than that of the original DeepLab V3+ model and other related models. The proposed method in this paper can accurately extract the distribution information of wheat and rape from high-resolution remote sensing images. This provides a new technical approach for the application of high-resolution remote sensing images in identifying wheat and rape. |
| first_indexed | 2024-03-11T11:21:59Z |
| format | Article |
| id | doaj.art-09d54c3b9ffc4be9b3626b1eecc43ef0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T11:21:59Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-09d54c3b9ffc4be9b3626b1eecc43ef02023-11-10T15:11:00ZengMDPI AGRemote Sensing2072-42922023-10-011521508810.3390/rs15215088Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ NetworkZhu Chang0Hu Li1Donghua Chen2Yufeng Liu3Chen Zou4Jian Chen5Weijie Han6Saisai Liu7Naiming Zhang8School of Geography and Tourism, Anhui Normal University, Wuhu 241003, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241003, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241003, ChinaSchool of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241003, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241003, ChinaAnhui National Defense Science and Technology Information Institute, Hefei 230041, ChinaSchool of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, ChinaSchool of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, ChinaRemote sensing technology has become a popular tool for crop classification, but it faces challenges in accurately identifying crops in areas with fragmented land plots and complex planting structures. To address this issue, we propose an improved method for crop identification in high-resolution remote sensing images, achieved by modifying the DeepLab V3+ semantic segmentation network. In this paper, the typical crop area in the Jianghuai watershed is taken as the experimental area, and Gaofen-2 satellite images with high spatial resolutions are used as the data source. Based on the original DeepLab V3+ model, CI and OSAVI vegetation indices are added to the input layers, and MobileNet V2 is used as the backbone network. Meanwhile, the upper sampling layer of the network is added, and the attention mechanism is added to the ASPP and the upper sampling layers. The accuracy verification of the identification results shows that the MIoU and PA of this model in the test set reach 85.63% and 95.30%, the IoU and F1_Score of wheat are 93.76% and 96.78%, and the IoU and F1_Score of rape are 74.24% and 85.51%, respectively. The identification accuracy of this model is significantly better than that of the original DeepLab V3+ model and other related models. The proposed method in this paper can accurately extract the distribution information of wheat and rape from high-resolution remote sensing images. This provides a new technical approach for the application of high-resolution remote sensing images in identifying wheat and rape.https://www.mdpi.com/2072-4292/15/21/5088crop identificationimproved DeepLab V3+ networkGaoFen-2Jianghuai watershed |
| spellingShingle | Zhu Chang Hu Li Donghua Chen Yufeng Liu Chen Zou Jian Chen Weijie Han Saisai Liu Naiming Zhang Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network Remote Sensing crop identification improved DeepLab V3+ network GaoFen-2 Jianghuai watershed |
| title | Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network |
| title_full | Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network |
| title_fullStr | Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network |
| title_full_unstemmed | Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network |
| title_short | Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network |
| title_sort | crop type identification using high resolution remote sensing images based on an improved deeplabv3 network |
| topic | crop identification improved DeepLab V3+ network GaoFen-2 Jianghuai watershed |
| url | https://www.mdpi.com/2072-4292/15/21/5088 |
| work_keys_str_mv | AT zhuchang croptypeidentificationusinghighresolutionremotesensingimagesbasedonanimproveddeeplabv3network AT huli croptypeidentificationusinghighresolutionremotesensingimagesbasedonanimproveddeeplabv3network AT donghuachen croptypeidentificationusinghighresolutionremotesensingimagesbasedonanimproveddeeplabv3network AT yufengliu croptypeidentificationusinghighresolutionremotesensingimagesbasedonanimproveddeeplabv3network AT chenzou croptypeidentificationusinghighresolutionremotesensingimagesbasedonanimproveddeeplabv3network AT jianchen croptypeidentificationusinghighresolutionremotesensingimagesbasedonanimproveddeeplabv3network AT weijiehan croptypeidentificationusinghighresolutionremotesensingimagesbasedonanimproveddeeplabv3network AT saisailiu croptypeidentificationusinghighresolutionremotesensingimagesbasedonanimproveddeeplabv3network AT naimingzhang croptypeidentificationusinghighresolutionremotesensingimagesbasedonanimproveddeeplabv3network |