River Extraction from Remote Sensing Images in Cold and Arid Regions Based on Attention Mechanism

نویسندگان

چکیده

The extraction of rivers in cold and arid regions is great significance for applications such as ecological environment monitoring, agricultural planning, disaster warning. However, there are few related studies on river regions, it still its infancy. accuracy low, the details blurred. rapid development deep learning has provided us with new ideas, but lack corresponding professional datasets, current semantic segmentation network not high. This study mainly presents following. (1) According to characteristics a dataset was made support from remote sensing images these regions. (2) Combine transfer learning, migrate ResNet-101 LinkNet network, introduce attention mechanism obtain AR-LinkNet which used improve recognition network. (3) A channel module spatial residual structure proposed strengthen effective features accuracy. (4) Combining dense atrous pyramid pooling (DenseASPP) expands receptive field, can extract more detailed information increase coherence extracted rivers. (5) For first time, binary cross-entropy loss function combined Dice applied function, accelerates convergence improves image quality. Validation shows that, compared typical networks, method performs better evaluation metrics recall, intersection ratio, precision, F 1 score, clearer coherent.

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ژورنال

عنوان ژورنال: Wireless Communications and Mobile Computing

سال: 2022

ISSN: ['1530-8669', '1530-8677']

DOI: https://doi.org/10.1155/2022/9410381