Identifying Dike-Pond System Using an Improved Cascade R-CNN Model and High-Resolution Satellite Images
نویسندگان
چکیده
The dike-pond system (DPS) is the integration of a natural or man-made pond and crop cultivation on dikes, widely distributed in Pearl River Delta Jianghan plain China. It plays key role preserving biodiversity, enhancing nutrient cycle, increasing production. However, DPS rarely mapped at large scale with satellite data, due to limitations training dataset traditional classification methods. This study improved deep learning algorithm Cascade Region Convolutional Neural Network (Cascade R-CNN) detect Qianjiang City using high-resolution data. In proposed mCascade R-CNN, regular convolution layer backbone was modified into deformable convolutional layer, which more suitable for features variable shapes orientations. R-CNN yielded most accurate detection DPS, an average precision (AP) value that 2.71% higher than 11.84% You Look Only Once-v4 (YOLOv4). area oilseed rape growing dikes accounted 3.42% total planting area. demonstrates potential leaning methods combined images detecting integrated agriculture systems.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14030717