Remote Sensing Image Recognition Based on Multi-attention Residual Fusion Networks
Weiwei Cai, Zhanguo Wei, Runmin Liu, Yuan Zhuang, Yan Wang, Xin Ning
Abstract
Since each sample in a hyperspectral remote sensing image is made up of high-dimensional features and contains a wealth of remote sensing features, feature selection and mining become more difficult. To address this issue, a multi-attention residual integrated network (MARB-Net) algorithm is proposed, which reduces redundant features while increasing feature fusion and, as a result, improves hyperspectral image recognition. First, assign multiple weights to each feature using multiple attention mechanism models; then, deep mine and integrate the features using the residual network; and finally, perform contextual semantic integration on the deep fusion features using the Bi-LSTM network. The recognition task should be completed by the Softmax classifier. The experimental results on three multi-class public data sets show that the MARB-Net algorithm proposed in this paper is effective.