Precise identification of objects in a hyperspectral image by characterizing the distribution of pure signatures

Hyperspectral image (HSI) has been widely adopted in many real-world applications due to its potential to provide detailed information from spectral and spatial data in each pixel. However, precise classification of an object from HSI is challenging due to complex and highly correlated features that exhibit a nonlinear relationship between the acquired spectral unique to the HSI object. In literature, many research works have been conducted to address this problem. However, the problem of processing high-dimensional data and achieving the best resolution factor for any set of regions remains to be evolved with a suitable strategy. Therefore, the proposed study introduces simplified modeling of the hyperspectral image in which precise detection of regions is carried out based on the characterization of pure signatures based on the estimation of the maximum pixel mixing ratio. Moreover, the proposed system emphasizes the pixel unmixing problem, where input data is processed concerning wavelength computation, feature extraction, and hypercube construction. Further, a non-iterative matrix-based operation with a linear square method is performed to classify the region from the input hyperspectral image. The simulation outcome exhibits efficient and precise object classification is achieved by the proposed system in terms classified HSI object and processing time

Joint Local Abundance Sparse Unmixing for Hyperspectral Images

Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. This implies the low-rankness of the abundance in terms of the endmember. From this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves estimation performance. In this study, we propose an algorithm that exploits the low-rank local abundance by applying the nuclear norm to the abundance matrix for local regions of spatial and abundance domains. In our optimization problem, the local abundance regularizer is collaborated with the L 2 , 1 norm and the total variation for sparsity and spatial information, respectively. We conducted experiments for real and simulated hyperspectral data sets assuming with and without the presence of pure pixels. The experiments showed that our algorithm yields competitive results and performs better than the conventional algorithms