Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification

Abstract

Hyperspectral image classification has received a lot of attention in the remote sensing field. However, most classification methods require a large number of training samples to obtain satisfactory performance. In real applications, it is difficult for users to label sufficient samples. To overcome this problem, in this work, a novel multi-scale superpixel-guided structural profile method is proposed for the classification of hyperspectral images. First, the spectral number (of the original image) is reduced with an averaging fusion method. Then, multi-scale structural profiles are extracted with the help of the superpixel segmentation method. Finally, the extracted multi-scale structural profiles are fused with an unsupervised feature selection method followed by a spectral classifier to obtain classification results. Experiments on several hyperspectral datasets verify that the proposed method can produce outstanding classification effects in the case of limited samples compared to other advanced classification methods. The classification accuracies obtained by the proposed method on the Salinas dataset are increased by 43.25%, 31.34%, and 46.82% in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient compared to recently proposed deep learning methods.

Mutual Information-Driven Feature Reduction for Hyperspectral Image Classification

“…A hyperspectral image (HSI), which is acquired at a contiguous spectral wavelength of the electromagnetic spectrum (EM), is a rich data source for a wide range of real-world remote sensing applications, including agriculture, geology, mining, military surveillance, and others [ 1 , 2 ]. Moreover, an HSI is set up as a hypercube and often has hundreds of contiguous, narrow bands in the spectral image [ 3 , 4 ].…”

An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation

“…It could reconstruct an image with fewer image patches, termed superpixels [ 1 ], which generalize pixel-wise primitives as region-level descriptors for subsequent processes. Many advanced visual tasks adopt it as a pre-processing tool by exploiting the potential to diminish the redundancy of input information, ranging from remote sensing [ 2 ], traffic analysis [ 3 ], geographic monitoring [ 4 ] and target detection [ 5 ].…”