关键词: 卷积神经网络, 深度学习, 标准分数, 高光谱遥感图像, 分类

Abstract:

Aiming at the problems of Hughes phenomenon and low utilization efficiency of spatial and spectral features in hyperspectral images, a hyperspectral image classification algorithm combining standard score dimensionality reduction and deep learning is proposed. Standard scores are used to evaluate the band quality of hyperspectral data to eliminate redundant bands in hyperspectral remote sensing images. Combined with the optimized 3D Convolutional Neural Network（3D-CNN） classification method, a large stride convolution layer is used to replace the pooling layer, and a series of tricks such as L2 regularization, Batch Normalization（BN） and Dropout are introduced to reduce network parameters and effectively prevent overfitting. Through the experimental tests of two hyperspectral datasets of Pavia Centre and Pavia University, the algorithm greatly reduces the parameters and computational costs of the network model, and achieves classification accuracy of 99.01% and 95.99%.

Key words: convolutional neural network, deep learning, standard scores, hyperspectral remote sensing images, classification