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MULTISPECTRAL CLASSIFICATION OF IRS-P6 IMAGES USING TEXTURAL FEATURES TRAINED SUPERVISED NEURAL NETWORKS
Jul 30th, 2007 by admin
MULTISPECTRAL CLASSIFICATION OF IRS-P6 IMAGES USING TEXTURAL FEATURES TRAINED SUPERVISED NEURAL NETWORKS
ABSTRACT
Supervised classification plays an important role in remote sensing image interpretation, in which the image pixels are classified to various predefined categories based on the spectral reflectance values in different bands. In reality some classes have very close spectral reflectance values and therefore they overlap in the feature space. This produces spectral confusion among the classes and results in inaccurate classified images. To remove such spectral ambiguity we require extra spectral and spatial knowledge. So additions of textural features are described here to reduce spectral ambiguity. A detailed comparison among three land cover image classification methods (namely Co-occurrence matrix based classifier using ANN, Gabor-ANN Classifier and Wavelet-ANN classifier) of remotely sensed images is presented in this paper. The testing and accuracy assessment has been carried out on IRS –P6 LISS IV satellite image of Allahabad area. The ground truth data used for training and accuracy assessment were collected using Lieca GS5 GPS receiver.
1. Introduction
Satellite images are used for a variety of natural resources management and environmental monitoring tasks. Supervised classification plays an important role in remote sensing image interpretation [1]. The objective here is to recognize and assign particular ranges of pixel values in spectral bands to the classes of interest, which can be used for decision making for effective management of natural resources.
(Note: This Paper was presented in ICSIP Please Download the paper for proper formatting, images and symbols)
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