高光谱成像
人工智能
模式识别(心理学)
计算机科学
卷积神经网络
Gabor滤波器
空间分析
特征提取
相似性(几何)
降维
滤波器(信号处理)
支持向量机
计算机视觉
数学
图像(数学)
统计
作者
Uzair Aslam Bhatti,Zhaoyuan Yu,Jocelyn Chanussot,Zeeshan Zeeshan,Linwang Yuan,Wen Luo,Saqib Ali Nawaz,Mughair Aslam Bhatti,Qurat Ul Ain,Adeel Mehmood
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-15
被引量:111
标识
DOI:10.1109/tgrs.2021.3090410
摘要
Currently, the different deep neural network (DNN) learning approaches have done much for the classification of hyperspectral images (HSIs), especially most of them use the convolutional neural network (CNN). HSI data have the characteristics of multidimensionality, correlation, nonlinearity, and a large amount of data. Therefore, it is particularly important to extract deeper features in HSIs by reducing dimensionalities which help improve the classification in both spectral and spatial domains. In this article, we present a spatial–spectral HSI classification algorithm, local similarity projection Gabor filtering (LSPGF), which uses local similarity projection (LSP)-based reduced dimensional CNN with a 2-D Gabor filtering algorithm. First, use the local similarity analysis to reduce the dimensionality of the hyperspectral data, and then we use the 2-D Gabor filter to filter the reduced hyperspectral data to generate spatial tunnel information. Second, use the CNN to extract features from the original hyperspectral data to generate spectral tunnel information. Third, the spatial tunnel information and the spectral tunnel information are fused to form the spatial–spectral feature information, which is input into the deep CNN to extract more effective features; and finally, a dual optimization classifier is used to classify the final extracted features. This article compares the performance of the proposed method with other algorithms in three public HSI databases and shows that the overall accuracy of the classification of LSPGF outperforms all datasets.
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