• R.Priya
• Dr. M. Senthil Murugan,

Hyperspectral remote sensing is one of the advance technologies which began in early 1980s and is one of the most significant breakthroughs in remote sensing. It emerged as a promising technology in remote sensing for studying earth surface materials by two ways spectrally & spatially. A multiple classifier system is a powerful solution to classify the Hyperspectral Remote Sensing Images. Image acquisition, feature extraction and classification play a vital role during the classification of hyperspectral remote sensing images. This paper concentrates on all the three parts, firstly the image acquisition is analyzed and the images are acquired by using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Sensor, secondly the Nonparametric Weighted Fuzzy Feature Extraction technique is proposed for feature extraction and Minimum Spanning Forest-Markov Random Field classifier is introduced which uses minimum spanning forest for classifying spectral information and markov random field for classifying spatial information. Finally, the results of Minimum Spanning Forest combined with Markov Random Field using Decision Fusion.

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), Nonparametric Weighted Fuzzy Feature Extraction (NWFFE), Minimum Spanning Forest (MSF), Markov Random Field (MRF), Nonparametric Weighted Feature Extraction (NWFE), Nonparametric Fuzzy Feature Extraction (NFFE).

"MINIMUM SPANNING FOREST-MARKOV RANDOM FIELD CLASSIFIER FOR HYPERSPECTRAL REMOTE SENSING IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.417-424, November-2018, Available :http://www.jetir.org/papers/JETIR1811150.pdf

"MINIMUM SPANNING FOREST-MARKOV RANDOM FIELD CLASSIFIER FOR HYPERSPECTRAL REMOTE SENSING IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp417-424, November-2018, Available at : http://www.jetir.org/papers/JETIR1811150.pdf