Synthetic Aperture Radar Remote Sensing for Crop Classification
نویسندگان
چکیده
This Study proposes the approach for crop classification using Grey Level Co-occurrence Matrix feature of Synthetic Aperture Radar (SAR) images. The method utilizes SAR Images acquired by Sentinel 1A Data and extract textural features GLCM. In this study, we investigate potential (GLCM)-based texture information horticulture with images in Kharif cloud weather condition. A study on satellite imagery was conducted Chhattisgarh objective to evaluate different parameters among crop. data were pre-processed analysis having entire angle equal distance quantization. results categorized showing significant variation crops Contrast, Dissimilarity, Homogeneity, ASM, Energy, Entropy GLCM Mean. statistical done fruit along major kharif area. shows that mean backscatter value lowest banana (99.12 dB) highest Mango (198.26 regarding contrast property VH Channel whereas w.r.t energy maximum (0.60 followed papaya (0.49 guava (0.45 least mango (0.44 dB). channel shown (51.24 (41.96 (32.98 These indicate usefulness images, particularly when acquisition optical is difficult condition classification. Thus proven be crops.
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ژورنال
عنوان ژورنال: International Journal of Plant and Soil Science
سال: 2023
ISSN: ['2320-7035']
DOI: https://doi.org/10.9734/ijpss/2023/v35i122961