Evaluation of different approaches to the fusion of Sentinel -1 SAR data and Resourcesat 2 LISS III optical data for use in crop classification

ABSTRACT

This study evaluates various combinations of data fusion techniques at Pixel, Feature, and Decision level for crop classification using Sentinel-1 Synthetic Aperture Radar (SAR) data and Resourcesat-2 LISS (Linear Imaging Self Scanning) III, optical data for Yadgir District of Karnataka, India. For Pixel level data fusion, techniques such as brovey transformation (BT), principal component analysis (PCA), multiplicative transformation (MLT), and wavelet with IHS (intensity-hue-saturation) were used. Results were compared between different fusion techniques visually, statistically (using universal image quality index), and through image classification (Rule-based and Maximum likelihood) for major crops (Rice, Cotton, and Pigeon pea) in the area. The estimated crop area for all three major crops was compared with the Government statistics. Among the four pixel-level fusion techniques used, the wavelet method performed best in retaining the image quality. However, the study showed that using the feature-level fusion technique, maximum accuracy was obtained for Rice crop. In contrast, the decision-level fusion improved the efficiency for other crops (Cotton and Pigeon pea).

Acknowledgments

This research work was part of the Ph.D. work of the first author at Nirma University, Gujarat, India. She is thankful to the university and Mahalanobis National Crop Forecast Centre for providing the satellite, ground truth data, and facility to work. The authors thank the Indian Space Research Organisation (ISRO) and the European Space Agency (ESA) for the satellite data.