Remote sensing landform recognition algorithm based on improved attention mechanism

doi:10.13474/j.cnki.11-2246.2020.0326

Abstract

Abstract: Recognizing remote sensing landforms has been a research hotspot in the field of remote sensing image applications in recent years. Using deep learning algorithms to identify remote sensing images has higher accuracy and robustness than traditional methods. Aimed at the problems of high target complexity and feature information in remote sensing images. This paper proposes a remote sensing image recognition algorithm based on an improved attention mechanism. A parallel attention mechanism (CS) and neural network model are combined to improve training with weakly supervised learning. At the same time, a double loss function is used to alleviate the problem of data overfitting. The experimental results show that the total accuracy of the model in this paper is 98.35%, and the Kappa coefficient is 0.95, which is better than other deep learning algorithms and can effectively recognize natural landforms.

Key words: remote sensing recognition, deep learning, parallel attention mechanism, weakly supervised training, double loss function

CLC Number:

P237

ZHANG Zhentong, SHAN Yugang, YUAN Jie. Remote sensing landform recognition algorithm based on improved attention mechanism[J]. Bulletin of Surveying and Mapping, 2020, 0(10): 93-96,100.

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