Pixel Level Feature Extraction and Machine Learning Classification for Water Body Extraction

Surface Water Bodies (SWB) are a renewable water source crucial for maintaining ecosystems and the water cycle. The declining rate of SWB increases owing to the overutilization of these resources, especially for agriculture. A timely and accurate Surface Water Body Extraction (SWBE) is necessary for water resource conservation and planning. Recently, Deep Learning (DL), a subset of Machine Learning (ML) algorithm, got remarkable attention in SWBE. It learns inherent features directly from the images at the expense of time and data. But, the ML algorithms such as K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGB) use optimal hand-crafted features to produce better results with fewer data and time. In this paper, SWBE is performed through two steps: (1) Use of spectral indices and Gabor filters for obtaining Pixel Level Feature (PLF) maps from the multispectral image; (2) Prediction of water and non-water pixels based on PLF maps using the KNN, DT, RF, SVM, and XGB classifiers. The proposed framework has experimented with Resoucesat-2 imagery over major reservoirs in Tamil Nadu and India. The results show that the proposed PLF + XGB outperforms in accuracy, recall, F1-score, kappa, False Negative Rate, Mathews Correlation Coefficient, and mean Intersection over Union with the metric value of 0.995, 0.990, 0.983, 0.979, 0.009, 0.979, and 0.969 with other existing and proposed models. Also, the surface water extent of Bhavani Sagar and Sathanur reservoirs is predicted for 4 years (2016-2019) and the causes of surface water dynamics were analyzed.