GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model
Desert encroachment significantly threatens the living and activity space of humanity, and undertaking human-directed vegetation restoration is one of the effective ways to prevent desert expansion. In the process of desert vegetation restoration, counting the number of tree saplings for rapidly ass...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/4/691 |
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| author | Shuzhen Hua Biao Yang Xinchang Zhang Ji Qi Fengxi Su Jing Sun Yongjian Ruan |
| author_facet | Shuzhen Hua Biao Yang Xinchang Zhang Ji Qi Fengxi Su Jing Sun Yongjian Ruan |
| author_sort | Shuzhen Hua |
| collection | DOAJ |
| description | Desert encroachment significantly threatens the living and activity space of humanity, and undertaking human-directed vegetation restoration is one of the effective ways to prevent desert expansion. In the process of desert vegetation restoration, counting the number of tree saplings for rapidly assessing the survival rate of vegetation (such as <i>Haloxylon ammodendron</i>) is a critical task within the restoration process. However, traditional ground-based statistical methods are resource-intensive and time-consuming. This paper proposed a novel unsupervised fine segmentation framework driven by Grounding DINO prompt generation and optimization segment anything model, termed GDPGO-SAM, designed for the segmentation of desert vegetation from UAV-derived remote sensing imagery, thereby facilitating the rapid inventory of tree saplings counts. The framework combines the Grounding DINO object detector and the pre-trained visual model SAM, employing a task-prior-based prompt optimization mechanism to effectively capture the innate features of desert vegetation. This method achieves zero-sample instance segmentation of desert vegetation with an overall accuracy (OA) of 96.56%, a mean Intersection over Union (mIoU) of 81.50%, and a kappa coefficient (kappa) of 0.782, successfully overcoming the limitations of traditional supervised models that rely on passive memorization rather than true recognition. This research significantly enhances the precision of vegetation extraction and canopy depiction, providing strong support for the management of desert vegetation restoration and combating desert expansion. |
| format | Article |
| id | doaj-art-7df16c478daf4cf3a83d796129f48ebb |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7df16c478daf4cf3a83d796129f48ebb2025-08-20T02:44:47ZengMDPI AGRemote Sensing2072-42922025-02-0117469110.3390/rs17040691GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything ModelShuzhen Hua0Biao Yang1Xinchang Zhang2Ji Qi3Fengxi Su4Jing Sun5Yongjian Ruan6School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaSociety of Entrepreneurs & Ecology Foundation, Beijing 100027, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaSociety of Entrepreneurs & Ecology Foundation, Beijing 100027, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaDesert encroachment significantly threatens the living and activity space of humanity, and undertaking human-directed vegetation restoration is one of the effective ways to prevent desert expansion. In the process of desert vegetation restoration, counting the number of tree saplings for rapidly assessing the survival rate of vegetation (such as <i>Haloxylon ammodendron</i>) is a critical task within the restoration process. However, traditional ground-based statistical methods are resource-intensive and time-consuming. This paper proposed a novel unsupervised fine segmentation framework driven by Grounding DINO prompt generation and optimization segment anything model, termed GDPGO-SAM, designed for the segmentation of desert vegetation from UAV-derived remote sensing imagery, thereby facilitating the rapid inventory of tree saplings counts. The framework combines the Grounding DINO object detector and the pre-trained visual model SAM, employing a task-prior-based prompt optimization mechanism to effectively capture the innate features of desert vegetation. This method achieves zero-sample instance segmentation of desert vegetation with an overall accuracy (OA) of 96.56%, a mean Intersection over Union (mIoU) of 81.50%, and a kappa coefficient (kappa) of 0.782, successfully overcoming the limitations of traditional supervised models that rely on passive memorization rather than true recognition. This research significantly enhances the precision of vegetation extraction and canopy depiction, providing strong support for the management of desert vegetation restoration and combating desert expansion.https://www.mdpi.com/2072-4292/17/4/691vegetation canopy segmentationinstance segmentationzero-shotsegment anything model |
| spellingShingle | Shuzhen Hua Biao Yang Xinchang Zhang Ji Qi Fengxi Su Jing Sun Yongjian Ruan GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model Remote Sensing vegetation canopy segmentation instance segmentation zero-shot segment anything model |
| title | GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model |
| title_full | GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model |
| title_fullStr | GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model |
| title_full_unstemmed | GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model |
| title_short | GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model |
| title_sort | gdpgo sam an unsupervised fine segmentation of desert vegetation driven by grounding dino prompt generation and optimization segment anything model |
| topic | vegetation canopy segmentation instance segmentation zero-shot segment anything model |
| url | https://www.mdpi.com/2072-4292/17/4/691 |
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