Synthetic Aperture Radar Remote Sensing for Crop Classification

This Study proposes the approach for crop classification using the Grey Level Co-occurrence Matrix feature of Synthetic Aperture Radar (SAR) images. The method utilizes the SAR Images acquired by Sentinel 1A SAR Data and extract textural features using GLCM. In this study, we investigate the potential of Grey Level Co-occurrence Matrix (GLCM)-based texture information for horticulture crop classification with SAR images in Kharif and cloud weather condition. A study on Synthetic Aperture Radar (SAR) satellite imagery was conducted in Chhattisgarh with the objective to evaluate the potential of different texture parameters among crop. The SAR data were pre-processed for textural analysis having entire angle and equal distance quantization. The results were categorized among different parameters showing significant variation for horticulture crops for Contrast, Dissimilarity, Homogeneity, ASM, Energy, Entropy and GLCM Mean. The statistical analysis was done for fruit crop along with major kharif crop of study area. The results shows that mean backscatter value was lowest for banana (99.12 dB) and highest for Mango (198.26 dB) regarding contrast textural property in VH Channel whereas mean backscatter value in VH Channel w.r.t to energy was maximum for banana (0.60 dB) followed by papaya (0.49 dB) and guava (0.45 dB) and least for mango (0.44 dB). The mean backscatter value for GLCM mean textural property in VH channel was shown maximum by banana (51.24 dB) followed by papaya (41.96 dB) and mango (32.98 dB). These results indicate the usefulness of texture information for classification of SAR images, particularly when acquisition of optical images is difficult in Kharif and cloud weather condition for crop classification. Thus GLCM feature of SAR Data proven to be significant for the classification of horticulture crops.