SRSe-Net: Super-Resolution-Based Semantic Segmentation Network for Green Tide Extraction

Due to the phenomenon of mixed pixels in low-resolution remote sensing images, the green tide spectral features with low Enteromorpha coverage are not obvious. Super-resolution technology based on deep learning can supplement more detailed information for subsequent semantic segmentation tasks. In this paper, a novel green tide extraction method for MODIS images based on super-resolution and a deep semantic segmentation network was proposed. Inspired by the idea of transfer learning, a super-resolution model (i.e., WDSR) is first pre-trained with high spatial resolution GF1-WFV images, and then the representations learned in the GF1-WFV image domain are transferred to the MODIS image domain. The improvement of remote sensing image resolution enables us to better distinguish the green tide patches from the surrounding seawater. As a result, a deep semantic segmentation network (SRSe-Net) suitable for large-scale green tide information extraction is proposed. The SRSe-Net introduced the dense connection mechanism on the basis of U-Net and replaces the convolution operations with dense blocks, which effectively obtained the detailed green tide boundary information by strengthening the propagation and reusing features. In addition, the SRSe-Net reducs the pooling layer and adds a bridge module in the final stage of the encoder. The experimental results show that a SRSe-Net can obtain more accurate segmentation results with fewer network parameters

Semi-Supervised Classification of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering

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 Raft Aquaculture Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure

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

Reverse Attention Dual-Stream Network for Extracting Laver Aquaculture Areas From GF-1 Remote Sensing Images

Extracting laver aquaculture areas from remote sensing images 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 article, 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 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, 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. 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