Hyperspectral Image Classification using Spectral Angle Mapper

This paper explains how to use the SAM method for satellite image categorization and how to implement it. Hyperspectral Images (HI) provide diverse pixel spectrums that retrieve rich surface information for visualization and differentiation of spectrally similar (but distinct) items. By the HI sensor, HI with multiple bands of spectrum are captured on the Remote Sensing Satellite (RSS). For the deployment of the Spectral Angle Mapper (SAM) on Hyperspectral Images, classifications are performed. In the different surfaces the fake color composite of the image is further obtained for better observation. For SAM implementation, HI of various bands are piled one after another in the form a three-dimensional Cube of images. A supervised classification technique SAM that recognizes the various classes within an image, allowing the spectral angle to be calculated. The spectral angle can be determined using the vector created for each pixel and, as a result, the reference vector created for each of the reference classes chosen. The outcomes are acquired by combining several 2-D data to 3-D data with single compact cube and reading and observing them. Because of the reference vector is used for SAM classification, the angle between the reference vector and the pixel vector is calculated to match the threshold angle value. The color coding is then used to distinguish between the several classes that the SAM algorithm recognizes. As a result, SAM is used to analyze hyperspectral photos in order to thematic information extract such as land water bodies, cover, and clouds.