Abstract
Jointly using spectral and spatial information has become a mainstream strategy in the field of hyperspectral image (HSI) processing, especially for classification. However, due to the existence of noisy or correlated spectral bands in the spectral domain and inhomogeneous pixels in the spatial neighborhood, HSI classification results are often degraded and unsatisfactory. Motivated by the attention mechanism, this paper proposes a spatial–spectral squeeze-and-excitation (SSSE) module to adaptively learn the weights for different spectral bands and for different neighboring pixels. The SSSE structure can suppress or motivate features at a certain position, which can effectively resist noise interference and improve the classification results. Furthermore, we embed several SSSE modules into a residual network architecture and generate an SSSE-based residual network (SSSERN) model for HSI classification. The proposed SSSERN method is compared with several existing deep learning networks on two benchmark hyperspectral data sets. Experimental results demonstrate the effectiveness of our proposed network.
Highlights
Hyperspectral sensors collect information as a series of images, represented by hundreds of narrow and contiguous spectral bands across a wide range of the spectrum, which allows detailed spectral signatures to be identified for different imaged materials [1,2,3]
To overcome the redundancy in the spectral channels and the pixel inconsistency in the spatial neighborhoods, we propose a spatial–spectral squeeze-and-excitation (SSSE) structure, which can adaptively learn the weights for different spectral bands and for different neighboring pixels at the same time
We investigate the effect of parameters on the classification performance of SSSE-based residual network (SSSERN)
Summary
Hyperspectral sensors collect information as a series of images, represented by hundreds of narrow and contiguous spectral bands across a wide range of the spectrum, which allows detailed spectral signatures to be identified for different imaged materials [1,2,3]. The resulting hyperspectral image (HSI) can be used to find objects, identify specific materials and detect processes in different application fields [1,3], such as military, agriculture, and mineralogy. Among these applications, classification is a basic problem which aims to assign a class label to each pixel in a HSI [4]. Compared with the large number of spectral bands, in practice the number of labeled training samples is usually quite limited This high dimensionality-small sample problem makes classification much more difficult and can lead to the Hughes phenomenon [5]. Spectral–spatial features are widely used, and HSI classification performance has gradually improved from the use of only spectral features to the joint use of spectral–spatial features [8,9,10,11]
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