Flood Detection in Dual-Polarization SAR Images Based on Multi-Scale Deeplab Model

Abstract

The proliferation of massive polarimetric Synthetic Aperture Radar (SAR) data helps promote the development of SAR image interpretation. Due to the advantages of powerful feature extraction capability and strong adaptability for different tasks, deep learning has been adopted in the work of SAR image interpretation and has achieved good results. However, most deep learning methods only employ single-polarization SAR images and ignore the water features embedded in multi-polarization SAR images. To fully exploit the dual-polarization SAR data and multi-scale features of SAR images, an effective flood detection method for SAR images is proposed in this paper. In the proposed flood detection method, a powerful Multi-Scale Deeplab (MS-Deeplab) model is constructed based on the dual-channel MobileNetV2 backbone and the classic DeeplabV3+ architecture to improve the ability of water feature extraction in SAR images. Firstly, the dual-channel feature extraction backbone based on the lightweight MobileNetV2 separately trains the dual-polarization SAR images, and the obtained training parameters are merged with the linear weighting to fuse dual-polarization water features. Given the multi-scale space information in SAR images, then, a multi-scale feature fusion module is introduced to effectively utilize multi-layer features and contextual information, which enhances the representation of water features. Finally, a joint loss function is constructed based on cross-entropy and a dice coefficient to deal with the imbalanced categorical distribution in the training dataset. The experimental results on the time series of Sentinel-1A SAR images show that the proposed method for flood detection has a strong ability to locate water boundaries and tiny water bodies in complex scenes. In terms of quantitative assessment, MS-Deeplab can achieve a better performance compared with other mainstream semantic segmentation models, including PSPNet, Unet and the original DeeplabV3+ model, with a 3.27% intersection over union (IoU) and 1.69% pixel accuracy (PA) improvement than the original DeeplabV3+ model.

Convolutional Neural Network-Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data

“…Earlier in this paper, we introduced the work of Nemni et al [4], Li et al [18], Zhao et al [30], Wu et al [46], Katiyar et al [47] and Kang et al [23] using CNN-based deep learning for the extraction of flood pixels from SAR images over different locations and environmental conditions. Comparing the results presented in these studies to our study, we found that our scores are either similar or superior to the scores presented.…”

“…Similarly, Kang et al [23] showed that the overall accuracy of FCN16 can reach 99%, which is comparable to our method. In another SAR-data-driven flood detection, Wu et al [46] achieved an average IoU score of 86.33% and PA score of 95.75%. Furthermore, the overall accuracy score in [46] appeared to be similar to that of Zhao et al [30], who achieved 95.95% for water and non-water classes, thus scoring about 3% lower than our best overall accuracy.…”

“…The study proposed a CNN-based MS-DeepLab model and other models, (i.e., PSPNet, U-Net and DeeLabV3) with the MobileNetV2 convolutional backbone. The results showed that the MS-DeepLab model successfully improved the feature extraction ability, especially for boundary detection, and was the best performing model in the study [46]. Furthermore, Zhao et al [30] used a CNN deep learning-based model for flood detection in Xinxiang, China.…”

“…The methodology was found to speed up the development of flood maps by 80%, as well as obtain strong performance metrics over the selected locations with varying environmental conditions [4]. Furthermore, Wu et al [46] used dual-polarised SAR images based on multi-scale DeepLab model for flood detection. The study proposed a CNN-based MS-DeepLab model and other models, (i.e., PSPNet, U-Net and DeeLabV3) with the MobileNetV2 convolutional backbone.…”

Method for inland water body extraction fused atrous spatial pyramid pooling

“…PSPNet network couples the semantic features of different areas via the pyramid pooling module and the pyramid scene parsing module, which elevates the segmentation precision of water body edges [5] . Deeplab series networks aggregate detailed information in the shallow layer and semantic information in the deep layer by using asymmetric structure [6] . SegFormer network realizes the semantic expression of higher-order water bodies by cutting off the positional encoding, utilizing MLP (Multilayer Perceptron) for feature extraction, and enlarging the receptive field [7] .…”