改进注意力机制的遥感地貌识别算法

对遥感地貌进行识别，近年来一直是遥感图像应用领域的研究热点。使用深度学习算法识别遥感影像具有比传统方法更高的准确率和稳健性。针对遥感影像中目标复杂度高、特征信息多等问题，本文提出了一种基于改进注意力机制的遥感图像识别算法，即将并联注意力机制（CS）和神经网络模型相结合，借助弱监督学习来辅助训练。同时采用双损失函数来缓解数据过拟合问题。试验结果表明，本文模型总精度为98.35%，Kappa系数达0.95，优于其他深度学习算法，能有效地识别出自然地貌。

Remote sensing landform recognition algorithm based on improved attention mechanism

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.