Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier

Over the past decades, the hyperspectral remote sensing technology development has attracted growing interest among scientists in various domains. The rich and detailed spectral information provided by the hyperspectral sensors has improved the monitoring and detection capabilities of the earth surface substances. However, the high dimensionality of the hyperspectral images (HSI) is one of the main challenges for the analysis of the collected data. The existence of noisy, redundant and irrelevant bands increases the computational complexity, induce the Hughes phenomenon and decrease the target's classification accuracy. Hence, the dimensionality reduction is an essential step to face the dimensionality challenges. In this paper, we propose a novel filter approach based on the maximization of the spectral interaction measure and the support vector machines for dimensionality reduction and classification of the HSI. The proposed Max Relevance Max Synergy (MRMS) algorithm evaluates the relevance of every band through the combination of spectral synergy, redundancy and relevance measures. Our objective is to select the optimal subset of synergistic bands providing accurate classification of the supervised scene materials. Experimental results have been performed using three different hyperspectral datasets: "Indiana Pine", "Pavia University" and "Salinas" provided by the "NASA-AVIRIS" and the "ROSIS" spectrometers. Furthermore, a comparison with the state of the art band selection methods has been carried out in order to demonstrate the robustness and efficiency of the proposed approach. Keywords: Hyperspectral images, remote sensing, dimensionality reduction, classification, synergic, correlation, spectral interaction information, mutual infor

Hyperspectral image unsupervised classification by robust manifold matrix factorization

© 2019 Elsevier Inc. Hyperspectral remote sensing image unsupervised classification, which assigns each pixel of the image into a certain land-cover class without any training samples, plays an important role in the hyperspectral image processing but still leaves huge challenges due to the complicated and high-dimensional data observation. Although many advanced hyperspectral remote sensing image classification techniques based on supervised and semi-supervised learning had been proposed and confirmed effective in recent years, they require a certain number of high quality training samples to learn a classifier, and thus can't work in the unsupervised manner. In this work, we propose a hyperspectral image unsupervised classification framework based on robust manifold matrix factorization and its out-of-sample extension. In order to address the high feature dimensionality of the hyperspectral image, we propose a unified low-rank matrix factorization to jointly perform the dimensionality reduction and data clustering, by which the clustering result can be exactly reproduced, which is significantly superior to the existing data clustering algorithms such as the k-means and spectral clustering. In particular, in the proposed matrix factorization, the ℓ 2,1 -norm is used to measure the reconstruction loss, which helps to reduce the errors brought by the possible noisy observation. The widely considered manifold regularization is also adopted to further promote the proposed model. Furthermore, we have designed a novel Augmented Lagrangian Method (ALM) based procedure to seek the local optimal solution of the proposed optimization and suggested an additional out-of-sample extension trick to make the method can deal with the large-scale hyperspectral remote sensing images. Several experimental results on the standard hyperspectral images show that the proposed method presents competitive clustering accuracy and comparative running time compared to the existing data clustering algorithms