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...
Saved in:
| Main Authors: | Kunjian Tao, He Li, Chong Huang, Qingsheng Liu, Junyan Zhang, Ruoqi Du |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/5/1139 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A segment anything model-based geological remote sensing interpretation method with a distributed data-parallel deep learning framework
by: Xiaohui Huang, et al.
Published: (2025-08-01) -
Enhancing satellite image compositing with temporal proximity weighting for deep learning–based cropland segmentation
by: Reza Maleki, et al.
Published: (2025-09-01) -
Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images
by: Mengyuan Yang, et al.
Published: (2025-08-01) -
Extraction of Abandoned Cropland Using Multisource Remote Sensing Images in Suburban Regions: A Case Study of Zengcheng, Guangdong Province
by: Shanshan Feng, et al.
Published: (2025-01-01) -
Cropland change analysis in Zhejiang coastal region
by: DING Han, et al.
Published: (2006-09-01)