2D-SSA based multiscale feature fusion for feature extraction and data classification in hyperspectral imagery

Singular spectrum analysis (SSA) and its 2-D variation (2D-SSA) have been successfully applied for effective feature extraction in hyperspectral imaging (HSI). However, they both cannot effectively use the spectral-spatial information, leading to a limited accuracy in classification. To tackle this problem, a novel 2D-SSA based multiscale feature fusion method, combining with segmented principal component analysis (SPCA), is proposed in this paper. The SPCA method is used for dimension reduction and spectral feature extraction, while multiscale 2D-SSA can extract abundant spatial features at different scales. In addition, a postprocessing via SPCA is applied on fused features to enhance the spectral discriminability. Experiments on two widely used datasets show that the proposed method outperforms two conventional SSA methods and other spectral-spatial classification methods in terms of the classification accuracy and computational cost

Feature reduction of hyperspectral images: Discriminant analysis and the first principal component

When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has not the limitation of linear discriminant analysis (LDA) in the number of extracted features. In DA-PC1, the dominant structure of distribution is preserved by PC1 and the class separability is increased by DA. The experimental results show the good performance of DA-PC1 compared to some state-of-the-art feature extraction methods

A novel band selection and spatial noise reduction method for hyperspectral image classification.

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral-spatial features of HSI. We proposed a new Neighboring band Grouping and Normalized Matching Filter (NGNMF) for BS, which can reduce the data dimension whilst preserve the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intra-class variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral-spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods

Large kernel spectral and spatial attention networks for hyperspectral image classification.

Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI