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 deep learning, medical image classification has been significantly improved. However, deep learning requires massive data with labels. While labeling the samples by human experts is expensive and time-consuming, collecting labels from crowd-sourcing suffers from the noises which may degenerate the accuracy of classifiers. Therefore, approaches that can effectively handle label noises are highly desired. Unfortunately, recent progress on handling label noise in deep learning has gone largely unnoticed by the medical image. To fill the gap, this paper proposes a noise-tolerant medical image classification framework named Co-Correcting, which significantly improves classification accuracy and obtains more accurate labels through dual-network mutual learning, label probability estimation, and curriculum label correcting. On two representative medical image datasets and the MNIST dataset, we test six latest Learning-with-Noisy-Labels methods and conduct comparative studies. The experiments show that Co-Correcting achieves the best accuracy and generalization under different noise ratios in various tasks. Our project can be found at: https://github.com/JiarunLiu/Co-Correcting .

Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. Such a design gives MSMKU a rich receptive field and is able to effectively represent multi-scale features. In addition, we designed a mixed maximum loss minimization learning strategy (MMLM) for training the proposed MSMKU. This training strategy can adaptively sort the samples by the loss function and re-weight the samples through data enhancement, thereby synchronously improving the prediction performance of all samples. Experiments show that the proposed method has obtained a state-of-the-art breakthrough result for OD and OC segmentation on the RIM-ONE-V3 and DRISHTI-GS datasets. At the same time, the proposed method achieved satisfactory glaucoma screening performance on the RIM-ONE-V3 and DRISHTI-GS datasets. On datasets with an imbalanced distribution between typical and rare sample images, the proposed method obtained a higher accuracy than existing deep learning methods.

Automated road extraction from very high-resolution (VHR) remote sensing imagery is important in many practical applications and has a long research history. Due to the diversity, narrowness, and sparsity of the road nature, extracting a full detailed road network remains a challenge, especially in the presence of interference. When applying semantic segmentation to deal with road extraction, U-Net-based architectures have achieved great progress through the use of dilated convolution or residual structure. However, the existing methods rarely focus on shape completeness and road continuity, and in fact, these are essential for road extraction. Inspirit by the multitask learning, in this letter, we present a novel road extraction architecture called gated auxiliary edge (GAE)-LinkNet with semantic segmentation as the main task and edge detection as the auxiliary task. With the proposed GatedBlocks, redundant features are filtered out and shape-relevant features stand out. Through the task loss weighing mechanism, these two tasks can work together seamlessly to make better use of the shape features. Experiments on a public road data set show that the proposed method is superior to state-of-the-art road extraction methods.

Object detection and semantic segmentation have achieved remarkable performance propelled by deep convolutional neural networks (DCNNs). However, neither of them can well parse and deal with swarms of rotating ships in remote sensing images. In this paper, we pay more attention to the instance-level segmentation task, which recognizes objects more effectively and straightly. We propose a new network architecture, called OSM-Net (Orientated Silhouette Matching Network), employing multi-scale features and instance-level masks to enable single-shot and anchor-box-free instance segmentation. To be specific, we propose a novel Orientated Polar Template Mask with Orientated Mask IoU to better match the ship silhouette. We also design a multi-scale feature propagation and fusion module to improve the precision of detection. To further improve the performance, our network adopts Res2Net and Soft-NMS. Extensive experiments on the open datasets, namely Airbus Ship, demonstrate that our method improves the average precision by 14.2 and 10.0 percentage points on Res2Net101, compared with PolarMask and YOLACT. The source code will be open source after the reviewing process.