Abstract
To achieve effective deep fusion features for improving the classification accuracy of hyperspectral remote sensing images (HRSIs), a pixel frequency spectrum feature is presented and introduced to convolutional neural networks (CNNs). Firstly, the fast Fourier transform is performed on each spectral pixel to obtain the amplitude spectrum, i.e., the pixel frequency spectrum feature. Then, the obtained pixel frequency spectrum is combined with the spectral pixel to form a mixed feature, i.e., spectral and frequency spectrum mixed feature (SFMF). Several multi-branch CNNs fed with pixel frequency spectrum, SFMF, spectral pixel, and spatial features are designed for extracting deep fusion features. A pre-learning strategy, i.e., basic single branch CNNs are used to pre-learn the weights of a multi-branch CNN, is also presented for improving the network convergence speed and avoiding the network from getting into a locally optimal solution to a certain extent. And after reducing the dimensionality of SFMF by principal component analysis (PCA), a 3-dimensionality (3-D) CNN is also designed to further extract the joint spatial-SFMF feature. The experimental results of three real HRSIs show that adding the presented frequency spectrum feature into CNNs can achieve better recognition results, which in turn proves that the presented multi-branch CNNs can obtain the deep fusion features with more discriminant information.
Highlights
Hyperspectral remote sensing images (HRSIs) contain rich spectral and spatial information and have the characteristics of high spectral resolution and large amounts of data and have been applied to many fields [1,2,3,4,5]
Using same training samples and testing samples, the experimental results of the basic convolutional neural networks (CNNs) fed with frequency spectrum feature (CNNfre ), the experimental results of the basic CNN fed with spectral feature (CNNspe ), basic CNN fed with the presented
The average overall accuracy (AOA) of ten runs and the standard deviation (SD), confuse matrix, and receiver operating characteristic (ROC) curve are used to evaluate the performance of the network
Summary
Hyperspectral remote sensing images (HRSIs) contain rich spectral and spatial information and have the characteristics of high spectral resolution and large amounts of data and have been applied to many fields [1,2,3,4,5]. It is difficult for traditional shallow models to achieve satisfactory classification results. Hu et al [8] input spectral pixel information into a CNN for feature extraction and classification, but this method does not use spatial information. A method using spectral and spatial information is proposed (Yue et al.) [9], this method firstly uses principal component analysis (PCA) for dimensionality reduction of HRSIs, and a CNN is used for feature extraction and classification by inputting the spatial neighborhoods of the several first principal components, but the spectral information of pixels is not fully used
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