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Multisource remote sensing images have rich features and high interpretability and are widely employed in many applications. However, highly unbalanced category distributions and complex backgrounds have created some difficulties in the application of remote sensing image semantic segmentation tasks, such as low accuracy of small target segmentation and inaccurate edge extraction. To solve these problems, in this paper, a feature map segmentation reconstruction module and an attention upsampling module are proposed. In the encoder part, the input feature map is equally segmented, and the segmented feature map is enlarged to effectively improve the small target feature information expression ability in the model. In the decoder part, the key segmentation and location information of shallow features are obtained using the global view. The deep semantic information and shallow spatial location information are fully combined to achieve a more refined upsampling operation. In addition, the attention mechanism of the spatial and channel squeeze and excitation block (scSE) is applied to pay more attention to important features and to suppress irrelevant background and redundant information. To verify the effectiveness of the proposed method, the WHU-OPT-SAR dataset and six state-of-the-art algorithms are utilized in comparative experiments. The experimental results show that our model has demonstrated the best performance and low computational complexity. With only approximately half the floating-point operation count and the number of model parameters of the MCANet model, which is specially designed for the dataset, our model surpasses MCANet by 1.52% and 1.53% in terms of mean intersection over union (mIoU) and F1 score, respectively. In particular, for small object regions such as roads and other categories, compared to the baseline model, the IoU and F1 score of our model are improved by 5.27% and 3.99% and by 5.68% and 5.65%, respectively. These results demonstrate the superior performance of our model in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a polarimetric homogeneity measurement and applies it to the speckle filtering of polarimetric synthetic aperture radar (PolSAR) data. First, a line-and-edge (LAE) detector that can detect both the lines and edges in one scan is developed based on the traditional edge detector. A polarimetric homogeneity measurement is then derived by combining the equivalent number of looks and the LAE maps and is used to distinguish the homogeneous and heterogeneous regions. Finally, a new adaptive-window PolSAR filtering algorithm based on the LAE detector and the polarimetric homogeneity measurement is proposed. The proposed speckle filter adjusts the filtering windows in both shape and size, based on the homogeneity and gradient information. Consequently, it uses small and nonsquare windows in heterogeneous regions to preserve the detail information and uses large and square windows in homogeneous regions to maximize the suppression of speckle noise. EMISAR and ESAR L-band PolSAR data were used to demonstrate the effectiveness of the proposed filter in speckle suppression, detail preservation, and polarimetric information preservation. [ABSTRACT FROM PUBLISHER]

Continuous and accurate acquisitions of surface water distribution are important for water resources evaluation, especially high-precision flood monitoring. During surface water extraction, optical imagery is strongly affected by clouds, while synthetic aperture radar (SAR) imagery is easily influenced by numerous physical factors; thus, the water extraction method based on single-sensor imagery cannot obtain high-precision water range under multiple scenarios. Here, we integrated the radar backscattering coefficient of ground objects into the Normalized Difference Water Index to construct a novel SAR and Optical Imagery Water Index (SOWI), and the water ranges of five study areas were extracted. We compared two previous automatic extraction methods based on single-sensor imagery and evaluated the accuracy of the extraction results. Compared with using optical and SAR imagery alone, the accuracy of all five regions was improved by up to 1–18%. The fusion-derived products resulted in user accuracies ranging 95–99% and Kappa coefficients varying by 85–97%. SOWI was then applied to monitor the 2021 heavy rainfall-induced Henan Province flood disaster, obtaining a time-series change diagram of flood inundation range. Our results verify SOWI's continuous high-precision monitoring capability to accurately identify waterbodies beneath clouds and algal blooms. By reducing random noise, the defects of SAR are improved and the roughness of water boundaries is overcome. SOWI is suitable for high-precision water extraction in myriad scenarios, and has great potential for use in flood disaster monitoring and water resources statistics. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has often been used in earthquake damage assessment due to its extreme versatility and almost all-weather, day-and-night capability. In this article, we demonstrate the potential to use only post-event, high-resolution airborne polarimetric SAR (PolSAR) imagery to estimate the damage level at the block scale. Intact buildings with large orientation angles have a similar scattering mechanism  to collapsed buildings; they are all volume-scattering dominant and reflection asymmetric, which seriously hampers the process of damage assessment. In this article, we propose a new damage assessment method combining polarimetric and spatial texture information to eliminate this deficiency. In the proposed method, the normalized circular-pol correlation coefficient is used first to identify intact buildings aligned parallel with the flight direction of the radar. The ‘homogeneity’ feature of the grey-level co-occurrence matrix (GLCM) is then introduced to distinguish building patches with large orientation angles from the severely damaged class. Furthermore, a new damage assessment index is also introduced to handle the assessment at the level of the block scale. To demonstrate the effectiveness of the proposed approach, the high-resolution airborne PolSAR imagery acquired after the earthquake that hit Yushu County, Qinghai Province of China, is investigated. By comparison with the damage validation map, the results confirm the validity of the proposed method and the advantage of further improving the assessment accuracy without external ancillary optical or SAR data. [ABSTRACT FROM PUBLISHER]

A novel segmentation algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed in this paper. The method is composed of two essential components: a merging order and a merging predicate. The similarity measured by the complex-kind Hotelling–Lawley trace (HLT) statistic is used to decide the merging order. The merging predicate is determined by the scattering characteristics and the revised Wishart distance between adjacent pixels, which can greatly improve the performance in speckle suppression and detail preservation. A postprocessing step is applied to obtain a satisfactory result after the merging operation. The decomposition and merging processes are iteratively executed until the termination criterion is met. The superiority of the proposed method was verified with experiments on two RADARSAT-2 PolSAR images and a Gaofen-3 PolSAR image, which demonstrated that the proposed method can obtain more accurate segmentation results and shows a better performance in speckle suppression and detail preservation than the other algorithms. [ABSTRACT FROM AUTHOR]