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.

在气候变化的背景下，提取自然资源的准确信息变得必要，被认为是遥感领域最具挑战性的任务之一。水资源的识别在遥感领域以解决水资源短缺问题得到了相当大的关注。在拟议的研究中，提出了一种新颖的多层数据集成技术 (MDIT)，用于从卫星图像中识别水资源。为了评估模式，提出了深度卷积限制模型（DCRM）来从卫星图像中提取深层层次特征。此外，DCRM 模型正在计算特征之间的关系以评估有意义的模式。而且，在 MDIT 中利用空间推断特征 (SIF) 和深度稀疏自动编码器 (DSA) 模块来改进空间特征之间的推断并计算提取特征之间的非直接关系。为了评估性能，将所提出的解决方案的预测效率与不同的最先进的传统和深度学习方法进行比较，例如归一化差分水指数 (NDWI)、残差神经网络 (ResNet)、视觉几何组 (VGG) )、DeepLab V3、密集连接卷积网络 (DenseNet) 和语义分割网络 (SegNet)。所提出的解决方案在从低分辨率卫星图像中提取水资源方面实现了 0.945% 的更高精度，从而优于所有最先进的方法。