H.5. Image Processing and Computer Vision
Fateme Namazi; Mehdi Ezoji; Ebadat Ghanbari Parmehr
Abstract
Paddy fields in the north of Iran are highly fragmented, leading to challenges in accurately mapping them using remote sensing techniques. Cloudy weather often degrades image quality or renders images unusable, further complicating monitoring efforts. This paper presents a novel paddy rice mapping method ...
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Paddy fields in the north of Iran are highly fragmented, leading to challenges in accurately mapping them using remote sensing techniques. Cloudy weather often degrades image quality or renders images unusable, further complicating monitoring efforts. This paper presents a novel paddy rice mapping method based on phenology, addressing these challenges. The method utilizes time series data from Sentinel-1 and 2 satellites to derive a rice phenology curve. This curve is constructed using the cross ratio (CR) index from Sentinel-1, and the normalized difference vegetation index (NDVI) and land surface water index (LSWI) from Sentinel-2. Unlike existing methods, which often rely on analyzing single-point indices at specific times, this approach examines the entire time series behavior of each pixel. This robust strategy significantly mitigates the impact of cloud cover on classification accuracy. The time series behavior of each pixel is then correlated with this rice phenology curve. The maximum correlation, typically achieved around the 50-day period in the middle of the cultivation season, helps identify potential rice fields. A Support Vector Machine (SVM) classifier with a Radial Basis Function (RBF) kernel is then employed, utilizing the maximum correlation values from all three indices to classify pixels as rice paddy or other land cover types. The implementation results validate the accuracy of this method, achieving an overall accuracy of 99%. All processes were carried out on the Google Earth Engine (GEE) platform.
