Author : K. Radhika 1
Date of Publication :7th March 2017
Abstract: Remote sensing is the art of obtaining information regarding an object or area using machine or device which is not physically contacted with the area. Geology, urban planning, Forest and land cover/land use are the several applications of remote sensing. Remote sensing is majorly utilized for generation of classification map. Latest methods used for classification of pixels in multispectral satellite images consists supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines ,k-NN and decision trees (Random Forest). SVM may be one-class or multi-class SVM. KNN is simple technique. In case of Random Forest, many decision trees are grown by it for classification. The input vector needs to run through every decision tree in the forest to classify a new object. The forest chooses the classification having the most votes. Random Forest provides a robust algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing multispectral satellite images. KNN is simple technique in high-dimensional feature space. In case of Random Forest, many decision trees are grown by it for classification. The input vector needs to run through every decision tree in the forest to classify a new object. The forest chooses the classification having the most votes. Random Forest provides a robust algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing multispectral satellite images.
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