Recently, moderate-resolution remote sensing images have been widely used in extracting coastal aquaculture areas that are usually used to obtain marine fishery resources. However, in moderate-resolution remote sensing images, the aquaculture areas destroyed by storm-tide are hard to be observed. In order to get information of coastal aquaculture areas quickly and accurately, to help the scientific management and the planning of aquaculture resources and to meet the needs of disaster emergency response, it is imperative to extract aquaculture areas based on high-resolution remote sensing images. However, as the spatial resolution increases, there are many disadvantages that traditional pixel-level classification methods bring about, for example, the sediments and some floatage on the water surface are easy to be mistaken as aquaculture areas. In view of these problems, we proposed a novel Semantic Segmentation Network, named Hybrid Dilated Convolution U-Net (HDCUNet), which combines U-Net and Hybrid Dilated Convolution (HDC) to further expend receptive field and prevent the ‘gridding’ problem simultaneously. In this paper, we chose Gaofen-2 images as experimental data to extract aquaculture areas, and the Precisions and Recalls of two types of aquaculture areas are both above 95% and the overall accuracy reaches up to 99.16%. Besides, we compared the extraction accuracy with other four methods: FCN-8s, SegNet, U-Net and Threshold Segmentation (TS) to analyse the extraction effect and verify the validity of HDCUNet.

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs.

Single Domain Generalization (SDG) is a more realistic setting than Domain Generalization (DG) and Domain Adaptation (DA). It aims to train a domain-agnostic model in the presence of a single source domain to perform well on arbitrary unseen target domains. To facilitate the practical deployment of remote sensing image segmentation in the real world, we propose a novel SDG method, termed Category Consistency on Domain Randomization (CCDR). To expand the coverage of a single source domain, CCDR implements a simple yet effective data generation module to perform domain randomization of texture and style information, since texture divergences from different geographical environments or phenological periods, and style divergences from different illumination or weather conditions, are the main causes of the domain shift in remote sensing. And in addition to emphasizing inter- and intra-class relationships within each domain via multi-domain supervised learning, CCDR further draws inspiration from the triple loss to enhance the semantic correlation across the source domain and the generated auxiliary domain, which makes the segmentation model more sensitive to class discriminative information and better adapted to unseen target domains. In comparison to other state-of-the-art SDG methods, CCDR can synthesize the more effective auxiliary domain, and perform more reliable classification on the unseen target domains without the sophisticated training pipeline and cumbersome data generation process. And the experimental results on two public remote sensing datasets demonstrate that CCDR has remarkable advantages in remote sensing image segmentation tasks. And the code is publicly available at: https://github.com/LCB1970/CCDR.

There is a large amount of out-of-distribution data (OOD) in remote sensing, which hinders high-accuracy segmentation models under the assumption of independent identical distribution (i.i.d.) from stable and reliable performance in real-world remote sensing applications. And Domain Adaptation (DA) is presented to seamlessly extend classifiers to the label-scarce target domain in the presence of the label-sufficient source domain with different data distributions. However, given that the domain shift, i.e. the distribution difference between the two domains, is more serious in remote sensing images, the current DA methods for image segmentation in Computer Vision (CV) typically perform unsatisfactorily in remote sensing, even suffering from the negative domain alignment. To this end, this paper proposes the Self-Training Adversarial Domain Adaptation (STADA) method for remote sensing image segmentation, which not only performs adversarial learning to extract domain-invariant features, but also implements Self-Training using pseudo-labels in the target domain denoised by the conditional adversarial loss for classifier adaptation. The ISPRS and WHU datasets are employed to conduct extensive experiments to investigate the effectiveness of STADA and the specific effect of its each DA component. And the experimental results demonstrate that STADA outperforms other state-of-the-art DA methods in the remote sensing image segmentation task.

Coastal aquaculture areas are some of the main areas to obtain marine fishery resources and are vulnerable to storm-tide disasters. Obtaining the information of coastal aquaculture areas quickly and accurately is important for the scientific management and planning of aquaculture resources. Recently, deep neural networks have been widely used in remote sensing to deal with many problems, such as scene classification and object detection, and there are many data sources with different spatial resolutions and different uses with the development of remote sensing technology. Thus, using deep learning networks to extract coastal aquaculture areas often encounters the following problems: (1) the difficulty in labeling; (2) the poor robustness of the model; (3) the spatial resolution of the image to be processed is inconsistent with that of the existing samples. In order to fix these problems, this paper proposes a novel semi-/weakly-supervised method, the semi-/weakly-supervised semantic segmentation network (Semi-SSN), and adopts 3 data sources: GaoFen-2 image, GaoFen-1(PMS)image, and GanFen-1(WFV)image with a 0.8 m, 2 m, and 16 m spatial resolution, respectively, and through experiments, we analyze the extraction effect of the model comprehensively. After comparing with other the-state-of-art methods and verifying on an open remote sensing dataset, we take the Fujian coastal area (mainly Sanduo) as the experimental area and employ our method to detect the effect of storm-tide disasters on coastal aquaculture areas, monitor the production, and make the distribution map of coastal aquaculture areas.

Due to more abundant data sources, more various objects of interest, and more time-consuming annotations, there is a large amount of out-of-distribution (OOD) data in the remote sensing field, on which the performance of high-accuracy image segmentation models trained under ideal experimental conditions generally degrades dramatically. Domain Adaptation (DA) consequently comes into being, which aims to learn the predictor for the label-scarce target domain of interest with the help of the label-sufficient source domain in the presence of the distribution difference, namely domain shift, between the two domains. However, the off-the-shelf DA methods for image segmentation not only struggle to cope with the more complex domain shift problems in remote sensing imagery, but also almost cannot process heterogeneous data directly without information loss. While the current heterogeneous DA methods mostly still rely on some supervision information from the target domain, which is typically inaccessible in the real world. To overcome these drawbacks, we propose the Multi-level Heterogeneous unsupervised DA method, termed MHDA, which unifies the instance-level DA based on cycle consistency, the feature-level DA based on contrastive learning, and the decision-level DA based on task consistency into a framework to more effectively handle the complex domain shift and heterogeneous data. After that, extensive DA experiments are conducted on the ISPRS dataset, the BigCity dataset constructed by ourselves, and the WHU dataset, in order to explore the effect of each module in MHDA, the necessity of heterogeneous DA, and the effectiveness of multi-level DA. And the results demonstrate that MHDA can achieve superior performance on the remote sensing image segmentation task, compared with several state-of-the-art DA methods.