Fog-inspired water resource analysis in urban areas from satellite images

Abstract

In the context of climate change, the extraction of accurate information on natural resources becomes necessary and is considered one of the most challenging tasks in the field of remote sensing. The identification of water resources has achieved considerable attention in the field of remote sensing to deal with the problem of water scarcity. In the proposed study, a novel Multi-layered Data Integration Technique (MDIT) is proposed for the identification of water resources from satellite imagery. To evaluate the patterns, Deep Convolutional Restrictive Model (DCRM) is proposed to extract deep hierarchical features from the satellite images. Furthermore, the DCRM model is calculating the relationship between the features to evaluate the meaningful patterns. Moreover, Spatial Inferred Features (SIF) and Deep Sparse Auto-encoder (DSA) modules are utilized in MDIT to improve the inferences between the spatial features and to calculate the non-direct relationship between the extracted features. To evaluate the performance, the prediction efficiency of the proposed solution is compared with different state-of-the-art conventional and deep learning approaches such as Normalized Difference Water Index (NDWI), Residual Neural Network (ResNet), Visual Geometry Group (VGG), DeepLab V3, Densely Connected Convolutional Network (DenseNet), and Semantic Segmentation Network (SegNet). The proposed solution outperformed all the state-of-the-art approaches by achieving a higher precision of 0.945% for the extraction of water resources from low-resolution satellite imagery.