Extraction of Cropland Based on Multi-Source Remote Sensing and an Improved Version of the Deep Learning-Based Segment Anything Model (SAM)
Fine extraction of cropland parcels is an essential prerequisite for achieving precision agriculture. Remote sensing technology, due to its large-scale and multi-dimensional characteristics, can effectively enhance the efficiency of collecting information on agricultural land parcels. Currently, sem...
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MDPI AG
2025-05-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1139 |
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| author | Kunjian Tao He Li Chong Huang Qingsheng Liu Junyan Zhang Ruoqi Du |
| author_facet | Kunjian Tao He Li Chong Huang Qingsheng Liu Junyan Zhang Ruoqi Du |
| author_sort | Kunjian Tao |
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| description | Fine extraction of cropland parcels is an essential prerequisite for achieving precision agriculture. Remote sensing technology, due to its large-scale and multi-dimensional characteristics, can effectively enhance the efficiency of collecting information on agricultural land parcels. Currently, semantic segmentation models based on high-resolution remote sensing imagery utilize limited spectral information and rely heavily on a large amount of fine data annotation, while pixel classification models based on medium-to-low-resolution multi-temporal remote sensing imagery are limited by the mixed pixel problem. To address this, the study utilizes GF-2 high-resolution imagery and Sentinel-2 multi-temporal data, in conjunction with the basic image segmentation model SAM, by additionally introducing a prompt generation module (Box module and Auto module) to achieve automatic fine extraction of cropland parcels. The research results indicate the following: (1) The <i>mIoU</i> of SAM with the Box module is 0.711, and the <i>OA</i> is 0.831, showing better performance, while the <i>mIoU</i> of SAM with the Auto module is 0.679, and the <i>OA</i> is 0.81, yielding higher-quality cropland masks; (2) The combination of various prompts (box, point, and mask), along with the hierarchical extraction strategy, can effectively improve the performance of Box module SAM; (3) Employing a more accurate prompt data source can significantly boost model performance. The <i>mIoU</i> of the superior-performing Box module SAM is increased to 0.920, and the <i>OA</i> is raised to 0.958. Overall, the improved SAM, while reducing the demand for mask annotation and model training, can achieve high-precision extraction results for cropland parcels. |
| format | Article |
| id | doaj-art-3ad2dfc042954bcc9cd3bf195ab59a8c |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-3ad2dfc042954bcc9cd3bf195ab59a8c2025-08-20T03:47:48ZengMDPI AGAgronomy2073-43952025-05-01155113910.3390/agronomy15051139Extraction of Cropland Based on Multi-Source Remote Sensing and an Improved Version of the Deep Learning-Based Segment Anything Model (SAM)Kunjian Tao0He Li1Chong Huang2Qingsheng Liu3Junyan Zhang4Ruoqi Du5State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaFine extraction of cropland parcels is an essential prerequisite for achieving precision agriculture. Remote sensing technology, due to its large-scale and multi-dimensional characteristics, can effectively enhance the efficiency of collecting information on agricultural land parcels. Currently, semantic segmentation models based on high-resolution remote sensing imagery utilize limited spectral information and rely heavily on a large amount of fine data annotation, while pixel classification models based on medium-to-low-resolution multi-temporal remote sensing imagery are limited by the mixed pixel problem. To address this, the study utilizes GF-2 high-resolution imagery and Sentinel-2 multi-temporal data, in conjunction with the basic image segmentation model SAM, by additionally introducing a prompt generation module (Box module and Auto module) to achieve automatic fine extraction of cropland parcels. The research results indicate the following: (1) The <i>mIoU</i> of SAM with the Box module is 0.711, and the <i>OA</i> is 0.831, showing better performance, while the <i>mIoU</i> of SAM with the Auto module is 0.679, and the <i>OA</i> is 0.81, yielding higher-quality cropland masks; (2) The combination of various prompts (box, point, and mask), along with the hierarchical extraction strategy, can effectively improve the performance of Box module SAM; (3) Employing a more accurate prompt data source can significantly boost model performance. The <i>mIoU</i> of the superior-performing Box module SAM is increased to 0.920, and the <i>OA</i> is raised to 0.958. Overall, the improved SAM, while reducing the demand for mask annotation and model training, can achieve high-precision extraction results for cropland parcels.https://www.mdpi.com/2073-4395/15/5/1139croplandremote sensingsemantic segmentationSAMprompt generation modulemultimodal satellite data |
| spellingShingle | Kunjian Tao He Li Chong Huang Qingsheng Liu Junyan Zhang Ruoqi Du Extraction of Cropland Based on Multi-Source Remote Sensing and an Improved Version of the Deep Learning-Based Segment Anything Model (SAM) Agronomy cropland remote sensing semantic segmentation SAM prompt generation module multimodal satellite data |
| title | Extraction of Cropland Based on Multi-Source Remote Sensing and an Improved Version of the Deep Learning-Based Segment Anything Model (SAM) |
| title_full | Extraction of Cropland Based on Multi-Source Remote Sensing and an Improved Version of the Deep Learning-Based Segment Anything Model (SAM) |
| title_fullStr | Extraction of Cropland Based on Multi-Source Remote Sensing and an Improved Version of the Deep Learning-Based Segment Anything Model (SAM) |
| title_full_unstemmed | Extraction of Cropland Based on Multi-Source Remote Sensing and an Improved Version of the Deep Learning-Based Segment Anything Model (SAM) |
| title_short | Extraction of Cropland Based on Multi-Source Remote Sensing and an Improved Version of the Deep Learning-Based Segment Anything Model (SAM) |
| title_sort | extraction of cropland based on multi source remote sensing and an improved version of the deep learning based segment anything model sam |
| topic | cropland remote sensing semantic segmentation SAM prompt generation module multimodal satellite data |
| url | https://www.mdpi.com/2073-4395/15/5/1139 |
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