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• A novel data augmentation strategy called feature-preserving generative adversarial network data augmentation (FPGANDA) is designed to alleviate the small-sample and sample imbalance problems in hyperspectral image classification. • A novel generative adversarial network and a new band selection and mixture strategy are developed for synthesizing mixed data possessing the main features of original data and the diverse features of generated data. • Experimental results show that the proposed method can get competitive performance. In recent years, significant breakthroughs have been achieved in hyperspectral image (HSI) processing using deep learning techniques, including classification, object detection, and anomaly detection. However, the practical application of deep learning in HSI processing is limited by challenges such as small-sample size and sample imbalance issues. To mitigate these limitations, we propose a novel data augmentation strategy called Feature-Preserving Generative Adversarial Network Data Augmentation (FPGANDA). What sets our data augmentation strategy apart from existing generative model-based approaches is that we preserve the main spectral bands of HSI data using a newly designed band selection method. Additionally, our proposed generative model generates synthetic spectral bands, which are combined with the real spectral bands using a mixture strategy to create augmented data. This approach ensures that the augmented data retain the main features of the original data while also incorporating diverse features from the generated data. We evaluate our method on three different HSI datasets, comparing it with state-of-the-art techniques. Experimental results demonstrate that our proposed method significantly improves classification performance in most scenes and exhibits remarkable compatibility. [ABSTRACT FROM AUTHOR]

The band selection (BS) is an essential task in hyperspectral images (HSIs), consisting of huge spectral bands with noises and redundancies. The attention mechanism can be used for BS. However, the existing attention-based BS schemes are failed to capture the cross-dimension interaction between the input spectral and spatial dimensions during computation of attention weights and may produce poor feature representations. Again, the used reconstruction network in the existing BS methods unable to detect the HSIs features in multiple scales by blindly increasing the depth of the network. To deal with these problems, a novel end-to-end unsupervised triplet-attention multiscale reconstruction network for BS (TAttMSRecNet) has been proposed. The proposed network utilizes a triplet-attention mechanism having three parallel branches responsible to aggregate interactive cross-dimensional features between the spatial and spectral dimensions. After that, the network restores the original HSIs by using a 3D multiscale reconstruction network that applies multiple size convolution kernels to capture the discriminative HSIs features over the multiple scales where these features also communicate themselves to find the most efficacious HSIs information. In this way, the rich features are captured at a little computation cost, and the most informative bands can be effectively chosen for classification. Three standard data sets — Indian Pines (IP), Salinas (SA), and University of Pavia (UP) have been taken to conduct the experiments. The presented TAttMSRecNet can efficiently suppress the redundant or useless bands and selects more informative bands for better classification performance and also outperforms the other existing BS methods. • We propose an end-to-end network for band selection in hyperspectral images. • It applies a triplet-attention with a multiscale reconstruction network. • Captures the robust feature representations at a low computation overhead. • Finds the most informative subset of spectral bands. • Yields significant classification performance improvements. [ABSTRACT FROM AUTHOR]

In this paper, a segmented minimum noise fraction (MNF) transformation is proposed for efficient feature extraction of hyperspectral images (HSIs). The original bands can be partitioned into several highly correlated subgroups based on the correlation matrix image of the hyperspectral data. The MNF is implemented separately on each subgroup of the data, and then, the Bhattacharyya distance is used as the band separability measure for feature extraction. Consequently, the extracted features can then be significantly classified using state-of-art classifiers, i.e., k -NN or SVM. Experiments on two benchmark HSIs collected by AVIRIS and ROSIS demonstrate that the proposed method significantly reduces the transformation time in comparison with the conventional MNF. The Fisher scalars’ criterion shows that the class separability with the segmented MNF is the best, and the extracted features even exhibit higher classification accuracy compared with the PCA or MNF. [ABSTRACT FROM AUTHOR]