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|Title: ||Comparison of CBF, ANN and SVM classifiers for object based classification of high resolution satellite images|
|Authors: ||BUDDHIRAJU, KM|
|Issue Date: ||2010|
|Citation: ||2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM,40-43|
|Abstract: ||Image classification is an important task for many aspects of global change studies and environmental applications. This paper emphasizes on the analysis and usage of different advanced image classification techniques like Cloud Basis Functions (CBFs) Neural Networks, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for object based classification to get better accuracy. For comparison, adaptive Gaussian filtered images were classified using ANN and post-processed using relaxation labeling process (RLP). The results are demonstrated using high spatial resolution remotely sensed images.|
|Appears in Collections:||Proceedings papers|
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