Multiobjective Discrete Spectral and Spatial Optimized Representation for Land-Cover Classification using Landsat Hyperspectral Images

Abstract

The availability of hyperspectral images with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. . The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban area is challenging due to their diversified activities and large spatial and temporal variations. In order to improve urban land cover classification and mapping, a novel framework named as Multiobjective Discrete Spectral and Spatial optimized representation for end member extraction has been proposed in this paper. It is considered as hyperspectral (HS) data exploitation model on identification of pure spectral signatures (endmembers) and their corresponding fractional abundances in each pixel of the HS data cube. High dimensionality of the data leads to computational complexity as it represents the Hughes phenomenon. Feature reduction strategy based on principle component analysis has been employed to generate reduced dimensionality of the features on retaining the most useful information. The reduced features have been taken for the spectral analysis and spatial analysis using Multiobjective Discrete Spectral and Spatial optimized representation model through encompassing the sparse and low-rank structure on the spectral signature of pixels. Identification and mapping of the land cover classification categorized as agriculture area and bare land has been identified using spectral indices (end members). The spectral indices calculation provides the type of land cover on the pixel purity index and it classifies based on the spectral and spatial value using N finder algorithm. N finder Algorithm is a change vector analysis. Experimental analysis has been carried out using Landsat-8 dataset to evaluating the performance of the proposed representative framework using available spectral indices against the state of art approaches. Proposed framework achieves accuracy of 99% on reflectance value against the different wavelength which superior with other existing classification approaches.