Abstract
Water detection from Synthetic Aperture Radar (SAR) images has been widely utilized in various applications. However, it remains an open challenge due to the high similarity between water and shadow in SAR images. To address this challenge, a new end-to-end framework based on deep learning has been proposed to automatically classify water and shadow areas in SAR images. This end-to-end framework is mainly composed of three parts, namely, Multi-scale Spatial Feature (MSF) extraction, Multi-Level Selective Attention Network (MLSAN) and the Improvement Strategy (IS). Firstly, the dataset is input to MSF for multi-scale low-level feature extraction via three different methods. Then, these low-level features are fed into the MLSAN network, which contains the Encoder and Decoder. The Encoder aims to generate different levels of features using residual network of 101 layers. The Decoder extracts geospatial contextual information and fuses the multi-level features to generate high-level features that are further optimized by the IS. Finally, the classification is implemented with the Softmax function. We name the proposed framework as MSF-MLSAN, which is trained and tested using millimeter wave SAR datasets. The classification accuracy reaches 0.8382 and 0.9278 for water and shadow, respectively; while the overall Intersection over Union (IoU) is 0.9076. MSF-MLSAN demonstrates the success of integrating SAR domain knowledge and state-of-the-art deep learning techniques.
Highlights
Detecting water bodies from Synthetic Aperture Radar (SAR) images has been a very active research field [1]
We propose a new end-to-end framework called Multi-scale Spatial Feature Multi-Level Selective Attention Network (MSF–MLSAN), which is built on state-of-the-art deep learning techniques, SAR domain knowledge and geospatial context analytics, to implement water and shadow classification
The Multi-scale Spatial Features (MSF) integrates prior SAR image features with deep learning techniques for multi-level feature extraction
Summary
Detecting water bodies from Synthetic Aperture Radar (SAR) images has been a very active research field [1]. Detection results are widely applied to reduce errors in SAR phase unwrapping, monitor flooding and assess damage, track water storage changes over various periods, investigate the area increase and decrease of wetlands and delineate shoreline movement [2,3,4]. Have proposed a semi-automated classification algorithm of water areas using Radarsat-1 SAR imagery, in which the average accuracy was merely around 70%. Martinis et al [6] compared advantages and disadvantages of four semi-automatic/automatic water detection algorithms and these algorithms achieved similar overall accuracy (i.e., ~85%) in the evaluation experiments. Other semi-automatic/automatic water detection approaches have been explored but their performance is unsatisfying [7,8,9]
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