000			01959nam a22001937a 4500
003			OSt
005			20220107122843.0
008			180719b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aAnju Francis (92316001) _914541
245			_aChange monitoring of agricultural lands using temporal hyperspectral datasets via feature extraction and classification techniques
300			_aMSC GIS 2016-2018
500			_a Hyperspectral remote sensing, also known as imaging spectroscopy is one of the most significant breakthrough in remote sensing which Began in 1980’s. It is emerged as a technology for studying earth surface materials by spectrally and spatially. In the field of agriculture hyperspectral remote sensing offers study of species diversity, environmental stress, physiological features such as photosynthetic activity, plant productivity, canopy biochemistry, biomass and plant transpiration. Also for evaluation of vegetation stress, nutrient stress, moisture stress and crop growth models. In this study, Hyperion image datasets covering the area of Kokrajhar, Assam in L1 R format. The Hyperion images are preprocessed by the Hyperion tools provided as the extension to ENVI software. Atmospheric corrections are performed by QUAC and FLAASH. Feature extraction was performed to reduce the high dimensionality data into lower dimension via PCA transformation. Classification was done using three techniques, Spectral Angle Mapper, Maximum Likelihood Classification and Support Vector Machine based classification. In this three classification results, the SVM classification after PCA transformation gave more accurate results. From the classified results of SVM, the area for each land classes were estimated.
502			_bMSC GIS _c2016-2018 _dINT _eMr. Radhakrishnan T
650			_aHYPERSPECTRAL REMOTE SENSING _914542
650			_aHYPERION IMAGE _914543
650			_aSUPPORT VECTOR MACHINE _914544
942			_2ddc _cPR
999			_c6164 _d6164