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CLASSIFICATION OF LISS IV IMAGERY USING DECISION TREE METHODS
Crop Vegetation Indices Texture Decision Tree Classification LISS IV
2016/12/1
Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to ...
CORRECTIONS FOR ATMOSPHERIC AND ADJACENCY EFFECTS ON HIGH RESOLUTION SENSOR DATA-A CASE STUDY USING IRS-P6 LISS-IV DATA
IRS-P6 Aerosol High Resolution Reflectance Classification
2015/12/28
Path radiance due to aerosols plays an important role over satellite derived reflectance due to higher aerosol optical depth and their seasonal variations. In addition, satellite data from high-resolu...
SEMI-SUPERVISED CLASSIFICATION OF LAND COVER BASED ON SPECTRAL REFLECTANCE DATA EXTRACTED FROM LISS IV IMAGE
Semi-supervised classification Hyperspectral Reflectance
2015/8/28
A methodology is proposed for extracting information on land cover based on hyperspectral reflectance
data derived from satellite image, without supervising with ground truth. The reflectance percent...
INVESTIGATING THE CAPABILITY OF IRS-P6-LISS IV SATELLITE IMAGE FOR PISTACHIO FORESTS DENSITY MAPPING (CASE STUDY: NORTHEAST OF IRAN)
IRS-P6-LISS IV Ground Truth Pistachio Forests Forest Density Iran
2014/4/30
In order to investigate the capability of satellite images for Pistachio forests density mapping, IRS-P6-LISS IV data were analyzed in an area of 500 ha in Iran. After geometric correction, suitable t...