Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients

Abstract

This paper proposes a gradient-based data fusion and classification approach for Synthetic Aperture Radar (SAR) and optical image. This method is used to intuitively reflect the boundaries and edges of land cover classes present in the dataset. For the fusion of SAR and optical images, Sentinel 1A and Sentinel 2B data covering Central State Farm in Hissar (India) was used. The major agricultural crops grown in this area include paddy, maize, cotton, and pulses during kharif (summer) and wheat, sugarcane, mustard, gram, and peas during rabi (winter) seasons. The gradient method using a Sobel operator and color components for three directions (i.e., x, y, and z) are used for image fusion. To judge the quality of fused image, several fusion metrics are calculated. After obtaining the resultant fused image, gradient based classification methods, including Stochastic Gradient Descent Classifier, Stochastic Gradient Boosting Classifier, and Extreme Gradient Boosting Classifier, are used for the final classification. The classification accuracy is represented using overall classification accuracy and kappa value. A comparison of classification results indicates a better performance by the Extreme Gradient Boosting Classifier.