Remote sensing has become a primary technology for monitoring raft aquaculture products. However, due to the complexity of the marine aquaculture environment, the boundaries of the raft aquaculture areas in remote sensing images are often blurred, which will result in ‘adhesion’ phenomenon in the raft aquaculture areas extraction. The fully convolutional network (FCN) based methods have made great progress in the field of remote sensing in recent years. In this paper, we proposed an FCN-based end-to-end raft aquaculture areas extraction model (which is called UPS-Net) to overcome the ‘adhesion’ phenomenon. The UPS-Net contains an improved U-Net and a PSE structure. The improved U-Net can simultaneously capture boundary and contextual information of raft aquaculture areas from remote sensing images. The PSE structure can adaptively fuse the boundary and contextual information to reduce the ‘adhesion’ phenomenon. We selected laver raft aquaculture areas in eastern Lianyungang in China as the research region to verify the effectiveness of our model. The experimental results show that compared with several state-of-the-art models, the proposed UPS-Net model performs better at extracting raft aquaculture areas and can significantly reduce the ‘adhesion’ phenomenon.

Sea-land segmentation of remote sensing images is of great significance to the dynamic monitoring of coastlines. However, the types of objects in the coastal zone are complex, and their spectra, textures, shapes, and distribution features are different. Therefore, sea-land segmentation for various types of coastlines is still a challenging task. In this paper, a scale-adaptive semantic segmentation network, called SANet, is proposed for sea-land segmentation of remote sensing images. SANet has made two innovations on the basis of the classic encoder-decoder structure. First, to integrate the spectral, textural and semantic features of ground objects at different scales, we designed an adaptive multi-scale feature learning module (AML) to replace the conventional serial convolution operation. The AML module mainly contains a multi-scale feature extraction unit and an adaptive feature fusion unit. The former can capture the multi-scale detailed information and contextual semantic information of objects from an early stage, while the latter can adaptively fuse feature maps of different scales. Second, we adopted the squeeze-and-excitation module to bridge the corresponding layers of the codec so that SANet can selectively emphasize the features of the weak sea-land boundaries. Experiments on a set of Gaofen-1 remote sensing images demonstrated that SANet achieved more accurate segmentation results and obtained sharper boundaries than other methods for various natural and artificial coastlines.

Semi-supervised classification methods result in higher performance for hyperspectral images, because they can utilize the relationship between unlabeled samples and labeled samples to obtain pseudo-labeled samples. However, how generating an effective training sample set is a major challenge for semi-supervised methods, In this paper, we propose a novel semi-supervised classification method based on extended label propagation (ELP) and a rolling guidance filter (RGF) called ELP-RGF, in which ELP is a new two-step process to make full use of unlabeled samples. The first step is to implement the graph-based label propagation algorithm to propagate the label information from labeled samples to the neighboring unlabeled samples. This is then followed by the second step, which uses superpixel propagation to assign the same labels to all pixels within the superpixels that are generated by the image segmentation method, so that some labels wrongly labeled by the above step can be modified. As a result, so obtained pseudo-labeled samples could be used to improve the performance of the classifier. Subsequently, an effective feature extraction method, i.e., RGF is further used to remove the noise and the small texture structures to optimize the features of the initial hyperspectral image. Finally, these produced initial labeled samples and high-confidence pseudo-labeled samples are used as a training set for support vector machine (SVM). The experimental results show that the proposed method can produce better classification performance for three widely-used real hyperspectral datasets, particularly when the number of training samples is relatively small.

Extracting laver aquaculture areas from remote sensing images (RSIs) is very important for laver aquaculture monitoring and scientific management. However, due to the large differences in spectral features of laver aquaculture areas caused by factors such as different growth stages and harvesting conditions,traditional machine learning and deep learning methods face great challenges in achieving accurate and complete extraction of raft laver aquaculture areas. In this paper, a reverse attention dual-stream network (RADNet) is proposed for the extraction of laver aquaculture areas with weak spectral responses by comprehensively considering both the aquaculture boundary and surrounding sea background information. RADNet consists of a boundary stream and a segmentation stream. Considering the weaker spectral responses of certain laver aquaculture areas, we introduce a reverse attention module (RAM) in the segmentation stream to amplify the weaker responses of inapparent laver aquaculture areas. To suppress the response of nonboundary details in the boundary stream, we design a boundary attention module (BAM), which is guided by high-level semantics from the segmentation stream. The structural information of the laver aquaculture area learned from the boundary stream will be fed back to the segmentation stream through a specially designed boundary guidance module (BGM). The study is conducted in Haizhou Bay, China and is verified using a self-labeled GF-1 multispectral dataset. The experimental results show that RADNet model performs better in extracting inapparent laver aquaculture areas compared to SOTA models.

Hyperspectral image classification methods may not achieve good performance when a limited number of training samples are provided. However, labeling sufficient samples of hyperspectral images to achieve adequate training is quite expensive and difficult. In this paper, we propose a novel sample pseudo-labeling method based on sparse representation (SRSPL) for hyperspectral image classification, in which sparse representation is used to select the purest samples to extend the training set. The proposed method consists of the following three steps. First, intrinsic image decomposition is used to obtain the reflectance components of hyperspectral images. Second, hyperspectral pixels are sparsely represented using an overcomplete dictionary composed of all training samples. Finally, information entropy is defined for the vectorized sparse representation, and then the pixels with low information entropy are selected as pseudo-labeled samples to augment the training set. The quality of the generated pseudo-labeled samples is evaluated based on classification accuracy, i.e., overall accuracy, average accuracy, and Kappa coefficient. Experimental results on four real hyperspectral data sets demonstrate excellent classification performance using the new added pseudo-labeled samples, which indicates that the generated samples are of high confidence.

The fine mapping of coastal wetlands is a major challenge due to the spectral aliasing of vegetation. In this letter, we selected Zhuhai-1 hyperspectral images (HSIs) for coastal wetland mapping and proposed a tiny-scene embedding network (TSE-Net) based on scene representation and attention mechanism. In TSE-Net, the tiny-scene representation associated with each hyperspectral pixel was extracted and used to enhance the spectral discrimination of ground objects. DenseNet was chosen as the backbone network, and the attention mechanism was introduced into the dense blocks to extract remarkable features. Experiments on the Yellow River estuary coastal wetland showed that the results of TSE-Net had a significant improvement in accuracy compared to other models, especially for the coastal wetland vegetation with confusing spectra, such as Spartina alterniflora, Suaeda salsa, Phragmites australis, and Tamarix.