Mangroves are important ecosystems and their distribution and dynamics can provide an understanding of the processes of ecological change. Meanwhile, mangroves protection is also an important element of the Maritime Silk Road (MSR) Cooperation Project. Large amounts of accessible satellite remote sensing data can provide timely and accurate information on the dynamics of mangroves, offering significant advantages in space, time, and characterization. In view of the capability of deep learning in processing massive data in recent years, we developed a new deep learning model—Capsules-Unet, which introduces the capsule concept into U-net to extract mangroves with high accuracy by learning the spatial relationship between objects in images. This model can significantly reduce the number of network parameters to improve the efficiency of data processing. This study uses Landsat data combined with Capsules-Unet to map the dynamics of mangrove changes over the 25 years (1990–2015) along the MSR. The results show that there was a loss in the mangrove area of 1,356,686 ha (about 21.5%) between 1990 and 2015, with anthropic activities such as agriculture, aquaculture, tourism, urban development, and over-development appearing to be the likely drivers of this decline. This information contributes to the understanding of ecological conditions, variability characteristics, and influencing factors along the MSR.

With the rapid development of deep convolutional neural networks (CNNs), super-resolution (SR) in hyperspectral image (HSI) has achieved good results. Current methods generally use 2-D convolution for feature extraction, but they cannot effectively extract spectral information. Although 3-D convolution can better characterize feature structure of HSI, it will lead to parameter redundancy, model complexity, and severe memory shortage. To address the above problems, we propose a new HSI SR method, named diffused CNN (DCNN). Specifically, spectral convolutions have been added into the enhanced convolutional neural (ECN) block, and a series of spectral convolutions are introduced in the residual network to learn features in the channel direction of different depths. Furthermore, histogram of oriented gradient (HOG) and local binary pattern (LBP) are used to retain the shape and texture information of the image, respectively, which can well represent the spatial structure of the object. To effectively make use of the extracted shallow and deep features, a feature fusion strategy is used to reinforce the reconstruction efficiency. Besides, an image enhancement module has been developed to diffuse the SR image into the image space. Extensive evaluations and comparisons show that our DCNN approach can not only recover the HSI data with richer details but also achieve superiority over several state-of-the-art methods.

Hyperspectral cameras capture electromagnetic information within hundreds of narrow spectral bands, producing hyperspectral images (HSIs) with the capability to accurately characterize the attribute information of objects. However, mixed noise induced by instrument and atmospheric effects hinders the interpretations and applications of the HSIs. In this paper, we propose a novel subspace representation based mixed noise removal method for hyperspectral images via Robust Subspace Estimation and weighted Group Sparsity constraint (RoSEGS). An outlier detection method is proposed to effectively detect sparse noise and replace the sparse noise with new estimates. A subspace estimation strategy, which is robust to mixed noise, is proposed. The subspace is first estimated after sparse noise detection and then optimized iteratively. In addition to the introduction of a state-of-the-art denoiser based on the plug-and-play technique to exploit self-similarity characteristics of the eigen-images, we impose a weighted group sparse regularization on the eigen-images to better promote the group sparsity of the spatial differences between the eigen-images, which further improves the denoising performance. We performed extensive experiments on two simulated and two real HSIs to fully demonstrate the effectiveness of the proposed method in comparison with seven state-of-the-art competitors.

Classification of remote sensing images is a key technology for extracting the information on ground objects using remote sensing methods. Inspired by the great success of deep learning in artificial intelligence, researchers have proposed different algorithums based on deep learning to improve the performance of classification. At present, a deep learning model represented by the Convolutional Neural Networks (CNNs) can extract the abstract feature, but it loses the spatial context of the ground objects. In order To solve the problem of lack of spatial information in CNNs, the feature of the Capsule network takes the form of vectors that convey the location transformation information from part to whole. This study propose using a Capsules-Unet model, which incorporates capsules within the U-net architecture for classification of remote sensing images. The aim of this study is to train better models by encapsulating the multi-dimensional features of the objects in the form of capsules, and to reduce parameter space by improving the dynamic routing algorithm. Experiments are conducted on ISPRS Vaihingen and Potsdam datasets. Capsules-Unet slightly outperforms all other approaches with far fewer parameters, a reduction in parameters of over 81.8% compared with U-net and over 13.8% compared with Capsule network.

The necessary prerequisite for effective data fusion is the strict registration of low-resolution hyperspectral images (LR-HSI) and high-resolution multispectral images (HR-MSI). However, registration requires a complex process that takes into account the effects of light, imaging angle, and geometric distortion of the image during acquisition. Therefore, to avoid complex registration, we focused on developing an unregistered HSI and MSI fusion method for pixel shifting, obtaining fused images with high resolution, high signal-to-noise ratio, and feature identifiability. We identified that the unregistered LR-HSI and HR-MSI in the case of pixel shift are very similar to the disparity maps in stereo vision. Inspired by this, we simulate the structure of stereo cameras to propose a stereo cross-attention network (SCANet) to achieve an accurate fusion of unregistered LR-HSI and HR-MSI. Considering the model complexity and computing efficiency, we design a simple and stackable stereo cross-fusion block (SCFBlock) based on a Transformer to simulate the process of light entering the left and right cameras by extracting the abstract features of the images. Moreover, the purpose of cross-convergence fusion self-attention (CCFSA) is to learn cross-complementary attention and collect contextual information in horizontal and vertical directions to fuse unregistered images using multi-directional cross-view information. We have conducted extensive experiments on Pavia University (PaviaU), Chikusei, and PYLake datasets. The results show that SCANet achieves superior or competitive performance in fusing unregistered LR-HSI and HR-MSI in comparison with the other competitors.