Loading...
Please wait, while we are loading the content...
Similar Documents
Identifying Dike-Pond System Using an Improved Cascade R-CNN Model and High-Resolution Satellite Images
| Content Provider | MDPI |
|---|---|
| Author | Ma, Yintao Zhou, Zheng She, Xiaoxiong Zhou, Longyu Ren, Tao Liu, Shishi Lu, Jianwei |
| Copyright Year | 2022 |
| Description | The dike-pond system (DPS) is the integration of a natural or man-made pond and crop cultivation on dikes, widely distributed in the Pearl River Delta and Jianghan plain in China. It plays a key role in preserving biodiversity, enhancing the nutrient cycle, and increasing crop production. However, DPS is rarely mapped at a large scale with satellite data, due to the limitations in the training dataset and traditional classification methods. This study improved the deep learning algorithm Cascade Region Convolutional Neural Network (Cascade R-CNN) algorithm to detect the DPS in Qianjiang City using high-resolution satellite data. In the proposed mCascade R-CNN, the regular convolution layer in the backbone was modified into the deformable convolutional layer, which was more suitable for learning the features of DPS with variable shapes and orientations. The mCascade R-CNN yielded the most accurate detection of DPS, with an average precision (AP) value that was 2.71% higher than Cascade R-CNN and 11.84% higher than You Look Only Once-v4 (YOLOv4). The area of oilseed rape growing on the dikes accounted for 3.42% of the total oilseed rape planting area. This study demonstrates the potential of the deep leaning methods combined with high-resolution satellite images in detecting integrated agriculture systems. |
| Starting Page | 717 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14030717 |
| Journal | Remote Sensing |
| Issue Number | 3 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-03 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Dike-pond Detection High-resolution Satellite Deep Learning Algorithm Cascade R-cnn Yolov4 |
| Content Type | Text |
| Resource Type | Article |
