Semantic segmentation is a fundamental research in optical remote sensing image processing. Because of the complex maritime environment, the sea-land segmentation is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there were a few of works using CNN for sea-land segmentation and the results could be further improved. This paper proposes a novel deep convolution neural network named DeepUNet. Like the U-Net, its structure has a contracting path and an expansive path to get high-resolution optical output. But differently, the DeepUNet uses DownBlocks instead of convolution layers in the contracting path and uses UpBlock in the expansive path. The two novel blocks bring two new connections that are U-connection and Plus connection. They are promoted to get more precise segmentation results. To verify the network architecture, we construct a new challenging sea-land dataset and compare the DeepUNet on it with the U-Net, SegNet, and SeNet. Experimental results show that DeepUNet can improve 1–2% accuracy performance compared with other architectures, especially in high-resolution optical remote sensing imagery.

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Water-land segmentation has important applications in many aspects such as environmental protection and flood prevention. Synthetic aperture radar (SAR) is one of the important tools for watershed monitoring. However, neither the traditional SAR water-land segmentation methods nor convolutional neural network (CNN)-based algorithms have satisfactory accuracy for the boundaries of water, especially for fine rivers/lakes. Transformer can effectively extract global information and has achieved amazing success in image interpretation. In this paper, a structure-aware CNN-Transformer network with U-shaped structure is designed to solve the water segmentation problem, named Trans-SANet. And an edge refinement post-processing module is added to Trans-SANet to refine the water edges, named Trans-SANet+. First, a cascaded attentional upsampler (CAUP) is designed in the decoder to combine spatial and temporal attentional serialization to fully capture feature information and avoid feature omission. Secondly, a structure-aware loss (SA-Loss) function is designed to give sufficient attention to the overall boundary structure while avoiding the loss of small-scale features to reduce the segmentation error of the boundary, thus improving the segmentation accuracy of the fine water region. Finally, combining fully connected conditional random field (CRF) with edge extraction, a post-processing module is proposed to further optimize the results of water-land segmentation. Experiments on three SAR datasets show that Trans-SANet+ outperformes several most advanced depth segmentation networks in terms of accuracy and speed.

Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the sea-land segmentation is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for sea-land segmentation and the results could be further improved. This paper proposes a novel deep convolution neural network named DeepUNet. Like the U-Net, its structure has a contracting path and an expansive path to get high resolution output. But differently, the DeepUNet uses DownBlocks instead of convolution layers in the contracting path and uses UpBlock in the expansive path. The two novel blocks bring two new connections that are U-connection and Plus connection. They are promoted to get more precise segmentation results. To verify our network architecture, we made a new challenging sea-land dataset and compare the DeepUNet on it with the SegNet and the U-Net. Experimental results show that DeepUNet achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.