Developing satellite hyperspectral image processing using a maximum abundance classifier with nine ground truth classes - Ismaeel, Ghassan Ahmad; Gheni, Mina Basheer; Abbas, Saad Qasim; Jaber, Mustafa Musa; Ali, Mohammed Hasan (Eastern-European Journal of Enterprise Technologies, 2023)
In the hundreds of bands of the photographed substance, hyperspectral imaging delivers a great density of spectral data. This allows the images to be used for a variety of purposes, including agriculture, geosciences, and biomedical imaging. Previous work didn’t discuss the best classifier with sufficient ground truth classes. This work presents the application of maximum abundance classification (MAC) for classifying a variety of areas over hyperspectral images. The allocation of an end-member throughout hyperspectral images can be described with abundance maps. Since each pixel's abundance values represent the proportion of each end-member that is present in that pixel, the pixels in a hyperspectral image will be classified in this study by determining the highest abundance rate of every pixel and allocating it to the corresponding end-member category. The ground truth classes are represented by nine end-members in the test data: Bitumen, Shadows, Self-Blocking Bricks, Bare Soil, highlighted Metal area, Gravel, Meadows, Trees, and Asphalt. By uniformly distributing the range of wavelength over the amount of spectral domains, we initially determine the central wavelength for each band to visualize loaded data and the end-member signatures of nine ground truth classes. Next, we estimate the end-members abundance maps. Finally, we classify the Max Abundance of every pixel to present a color-coded image, the overlaid, and the classified hyperspectral image areas over their category labels. The result demonstrates that brick, bare soil, trees, and asphalt zones have all been correctly identified in the photographs, which is beneficial for the identification or detection of materials