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.
When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature