• Vaibhav A. Didore
• Renuka Bhokarkar Vaidya
• Dhanajay Nalawade
• Dr.KV Kale

In remote sensing, hyperspectral images contain most of the information that can be processed to identify objects. Satellite imagery classification plays a key role in various object recognition systems. Satellite imagery classification requires a selection of suitable classification techniques based on the user’s requirement. In this paper, two supervised classification technique namely minimum distance classification (MDC) and Spectral angle mapper (SAM) has been used for the analysis of the remotely sensed data. In this experimental analysis, EO-1 Hyperion sensor data downloaded from USGS earth explorer has been used. The MDC technique estimates the Euclidean distance of each pixel to the mean vector of the class and assigns each pixel to the closest class. Mean vector and covariance metrics are the key component of MDC that can be retrieved from training data. The classification accuracy has been achieved for the MDC in terms of overall accuracy 79.31% and overall kappa accuracy is 73.99%. similarly overall accuracy calculated for SAM is 84.12% and kappa accuracy is 79.54%. From the experimental analysis it is evident that Minimum Distance classification technique is useful for small size data and it takes less time for classification, however more classification accuracy is achieved by using SAM.

"Classification of EO-1 Hyperion Data using Supervised Minimum Distance Algorithm and Spectral Angle Mapper", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.f148-f152, October-2021, Available :http://www.jetir.org/papers/JETIR2110522.pdf

"Classification of EO-1 Hyperion Data using Supervised Minimum Distance Algorithm and Spectral Angle Mapper", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. ppf148-f152, October-2021, Available at : http://www.jetir.org/papers/JETIR2110522.pdf