Deep learning framework based on Spectral and Spatial properties for Land-Cover Classification using Landsat Hyperspectral Images (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Hyperspectral Imaging is used to monitor the earth on basis of spectral continuous data ranges starting from visible to short wave infrared region of the electromagnetic spectrum. It enables the detailed identification and classification of minerals and land cover on basis of with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. Several challenges have been generated due to Hughes phenomenon (curse of dimensionality) and Quantification of land cover in urban area. In order to alleviate those problems, novel framework named as Deep learning framework on spectral and spatial properties on Landsat image has been proposed which composed of several techniques. Initially 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 has been proposed. 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. Further Ensemble based method has been proposed in addition to generate diverse classification results and the discrete high correlation classifier method which can enhance the accuracy and diversity of a single classifier simultaneously. Finally an efficient agriculture land cover spectral evolution mapping has been proposed using Multivariate principle component analysis. It is considered as change detection method explores efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of Context towards mapping Agriculture Land cover spectral evolution. It computes the spectral correlation between two images on spectral similarity. It predicts the accurately on temporal changes of the earth surfaces. 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.