Hierarchical Shrinkage Multiscale Network for Hyperspectral Image Classification With Hierarchical Feature Fusion

Abstract

Recently, deep learning (DL) based hyperspectral image classification (HSIC) has attracted substantial attention.Many works based on the convolutional neural network (CNN) model have been certificated to be significantly successful for boosting the performance of HSIC. However, most of these methods extract features by using a fixed convolutional kernel and ignore multi-scale features of the ground objects of hyperspectral images (HSIs). Although some recent methods have proposed multi-scale feature extraction schemes, more computing and storage resources were consumed. Moreover, when using CNN to implement hyperspectral image classification, many methods only use the high-level semantic information extracted from the end of the network, ignoring the edge information extracted from shallow layers of the network. To settle the preceding two issues, a novel HSIC method based on hierarchical shrinkage multi-scale network (HSMSN) and the hierarchical feature fusion (HFF) is proposed, with which the newly proposed classification framework can fuse features generated by both of multi-scale receptive field and multiple levels. Specifically, multi-depth and multi-scale residual block (MDMSRB) is constructed by superposition dilated convolution to realize multi-scale feature extraction. Furthermore, according to the change of feature size in different stages of the neural networks, we design a hierarchical shrinkage multi-scale feature extraction network by pruning MDMSRB to reduce the redundancy of network structure. In addition, to make full use of the features extracted in each stage of the network, the proposed network hierarchically integrates low-level edge features and high-level semantic features effectively. Experimental results demonstrate that the proposed method achieves more competitive performance with a limited computational cost than other state-of-the-art methods. Index Terms-Hyperspectral image classification (HSIC), convolutional neural network (CNN), hierarchical shrinkage multi-scale network (HSMSN), multi-depth and multi-scale residual block (MDMSRB), hierarchical feature fusion (HFF)Ⅰ. INTRODUCTION ith the rapid development of imaging spectrometers and platforms, imaging spectroscopy (also called This paragraph of the first footnote will contain the date on which you submitted your paper for review.

Hyperspectral Image Dimensionality Reduction Algorithm Based on Spatial–Spectral Adaptive Multiple Manifolds

“…where d ij is the Euclidean distance between the spatial coordinates of two pixels x and y , i.e., d ij = (a − c) 2 + (b − d) 2 . The choice of λ depends on the specific application and the data characteristics.…”

“…A hyperspectral image (HSI) is a three-dimensional cubic structure with high spectral resolution, which occupies a prominent position in the remote sensing earth observation system [1]. HSIs are widely used in ground-object classification [2][3][4], change detection [5,6], anomaly detection [7][8][9], target detection [10], spectral unmixing [11], etc.…”

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

“…Furthermore, traditional CNN mostly uses fixed convolution kernels to extract features, which is not friendly to multi-scale features in hyperspectral images. In order to solve the above problems, Gao et al proposed a multi-depth and multi-scale residual block (MDMSRB), which can fuse multi-scale receptive fields and multi-level features [55]. Although MDMSRB can integrate multi-scale receptive fields, the problem of blind spots in the receptive fields has not really been solved.…”

An Improved 3D-2D Convolutional Neural Network Based on Feature Optimization for Hyperspectral Image Classification

“…In this regard, dilated convolution [27] can increase the size of the convolution kernel while ensuring that the parameters remain unchanged. Gao et al [28] introduced dilated convolution into multi-scale feature extraction module to classify hyperspectral images.…”