Joint bilateral filtering and spectral similarity-based sparse representation: A generic framework for effective feature extraction and data classification in hyperspectral imaging

Classification of hyperspectral images (HSI) has been a challenging problem under active investigation for years especially due to the extremely high data dimensionality and limited number of samples available for training. It is found that hyperspectral image classification can be generally improved only if the feature extraction technique and the classifier are both addressed. In this paper, a novel classification framework for hyperspectral images based on the joint bilateral filter and sparse representation classification (SRC) is proposed. By employing the first principal component as the guidance image for the joint bilateral filter, spatial features can be extracted with minimum edge blurring thus improving the quality of the band-to-band images. For this reason, the performance of the joint bilateral filter has shown better than that of the conventional bilateral filter in this work. In addition, the spectral similarity-based joint SRC (SS-JSRC) is proposed to overcome the weakness of the traditional JSRC method. By combining the joint bilateral filtering and SS-JSRC together, the superiority of the proposed classification framework is demonstrated with respect to several state-of-the-art spectral-spatial classification approaches commonly employed in the HSI community, with better classification accuracy and Kappa coefficient achieved

Value Focused Thinking Applications to Supervised Pattern Classification with Extensions to Hyperspectral Anomaly Detection Algorithms

Hyperspectral imaging (HSI) is an emerging analytical tool with flexible applications in different target detection and classification environments, including Military Intelligence, environmental conservation, etc. Algorithms are being developed at a rapid rate, solving various related detection problems under certain assumptions. At the core of these algorithms is the concept of supervised pattern classification, which trains an algorithm to data with enough generalizability that it can be applied to multiple instances of data. It is necessary to develop a logical methodology that can weigh responses and provide an output value that can help determine an optimum algorithm. This research focuses on the comparison of supervised learning classification algorithms through the development of a value focused thinking (VFT) hierarchy. This hierarchy represents a fusion of qualitative/ quantitative parameter values developed with Subject Matter Expert a priori information. Parameters include a fusion of bias/variance values decomposed from quadratic and zero/one loss functions, and a comparison of cross-validation methodologies and resulting error. This methodology is utilized to compare the aforementioned classifiers as applied to hyperspectral imaging data. Conclusions reached include a proof of concept of the credibility and applicability of the value focused thinking process to determine an optimal algorithm in various conditions