Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery

Abstract

High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.

Fine-Grained Tidal Flat Waterbody Extraction Method (FYOLOv3) for High-Resolution Remote Sensing Images

“…Liang et al [19] introduced dense connection structure in the full convolution network to reduce the shallow feature loss, get more detailed information from the remote sensing images, and achieve better water extraction. Song et al [20] used the self-learning ability of deep learning to construct a modified Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Yu et al [21] presented a novel deep learning framework for waterbody extraction from Landsat images considering both its spectral and spatial information, which is a hybrid of CNN and logistic regression classifier.…”

Deep learning for processing and analysis of remote sensing big data: a technical review

“…Waterbodies are common targets in RS images. Various deep learning frameworks have been developed for extracting waterbodies Feng, Sui, Huang, Xu, & An, 2018;Isikdogan, Bovik, & Passalacqua, 2017;Li et al, 2019a;Song et al, 2020;Wang, Li, & Zeng et al, 2020b). It is worth mentioning that waterbodies consist of lakes, rivers, and sea.…”

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

“…Intelligent computing and automatic analysis is the premise of RS-STBD for data mining, information extraction and knowledge transformation from RS observation data. The processing of RS data has experienced three developments stage from qualitative RS to quantitative RS [29], and then to intelligent RS [30]. Generally, qualitative model and conceptual model are used to realize the qualitative analysis of RS.…”