dc.description.abstract |
Satellite imagery has provided the top view for solving many engineering, agriculture, water resources, disaster response, and environmental monitoring problems. The top view requires the classification of real-world objects from satellite images. Broadly two image classification approaches are used: pixel-based and object-based. The pixel-based classification approach primarily works on spectral characteristics and ignores spatial features, whereas object-based classification operates on both spectral and spatial features. The current work examines object-based and pixel-based methods for high resolution satellite images of novel Cartosat − 2E and Cartosat-3 satellites using machine learning classifiers (Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), k-Nearest Neighbour (kNN) and Bayes). The study identified that decision tree classifier outperformed other classifiers under object-based approach with kappa coefficient higher than 0.90. On the other hand, for pixel-based approach kNN and SVM classifiers outperformed the other classifiers for Cartosat – 2 and Cartosat – 3 images. However, for Linear Imaging Self Scanning (LISS)-4 image Bayes, SVM and kNN performed relatively better than RF and DT. The model parameters of the machine learning classifiers may be altered or fine-tuned to increase predictive performance for object-based and pixel-based image classification techniques. |
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