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The sparse unmixing (SU) technique is widely used in hyperspectral image (HSI) unmixing because it does not need to estimate the number of pure endmembers but directly obtains the spectra from known spectral libraries to construct the endmember matrix, which avoids the influence of endmember extraction on unmixing. However, some existing SU algorithms still have problems, such as insufficient consideration of abundance details and sensitivity to noise. In order to solve the above issues, this article proposes a graph Laplacian weighted robust SU (RSU) algorithm based on superpixels, which can better reconstruct abundance details and reduce sensitivity to noise. The coarse abundance is calculated based on the superpixel results, and then the global spatial prior weight is calculated. Then, weighted RSU is applied to each superpixel to achieve a combination of local and global cooperation to reduce sensitivity to noise. On this basis, in order to better reconstruct the abundance details, the spatial position information and spectral information between pixels within superpixels are used to construct a weighted map to represent the similarity between pixels. Finally, the alternating direction multiplier method (ADMM) is used to perform structural optimization on the superpixel scale, retaining the structural information of abundance and reducing the amount of calculation. Experiments are conducted on three simulated datasets and three real datasets, and the results show that the proposed algorithm outperforms state-of-the-art SU algorithms.

Vision transformers are widely used in hyperspectral image (HSI) classification, with their core feature extractor being self-attention. Self-attention has a wider receptive field than convolution. However, existing vision transformers for the classification of HSIs with a large number of bands generally suffer from high computational complexity and a large number of parameter requirements. In this article, we propose an ultralightweight feature-compressed multihead self-attention learning network (UFMS-LN), which mainly consists of a novel compressed feature multihead self-attention (CF-MHSA), a spatial feature enhancement-enhancing transformation reduction (SFE-ETR), and a spatial-spectral hybridization-receptive field attention convolutional operation (SH-RFAConv). By effectively compressing feature maps in spatial-spectral dimensions, CF-MHSA achieves the same feature extraction capabilities as state-of-the-art self-attention mechanisms, and its floating-point operations (FLOPs) and parameters are two orders of magnitude lower than state-of-the-art self-attention mechanisms. SH-RFAConv is designed to emphasize local features, which have the ability to extract both spatial-spectral features simultaneously and have a wider receptive field than traditional convolutional operations. Furthermore, SFE-ETR is a preprocessing module for UFMS-LN that combines global spatial feature enhancement methods with enhancing transformation reduction (ETR). Extensive experiments conducted on four benchmark HSI datasets have shown that this method achieves superior results compared to existing state-of-the-art HSI classification networks.

The single screw compressor (SSC) is widely used in air compression and refrigeration systems due to its many advantages. The meshing clearance between the screw groove and gate rotor teeth flank has a significant influence on the compressor performance. In this paper, mathematical calculation models describing the internal working process of the SSC are established in order to evaluate the thermal dynamic characteristics of the compressor under varying meshing clearance heights. The refrigerating capacity, volume efficiency and adiabatic efficiency of the SSC are calculated and discussed. Three prototypes, with different meshing clearance heights, were manufactured to study the internal influence mechanisms. The theoretical model was verified using experimental data and the calculation results strongly agreed with the experimental results. Results demonstrate that comparisons of volume efficiency and adiabatic efficiency between the measured and calculated results exhibited deviations of 3.64%–7.98% and 5.92%–9.4%, respectively. Based on the models, analysis under varying meshing clearance heights and working conditions was performed. Taking into account working performance, actual manufacturing conditions and manufacturing cost limitations, a meshing clearance height range from 0.01 mm to 0.08 mm is suggested. This study can provide important theoretical data and experimental support for the design, manufacturing and optimization of single screw compressors.

The shipping industry is pivotal in transporting approximately 90% of the world’s goods, and it is characterized by evolving trends in vessel sizes and energy-efficient designs. Continuous advancements in technology for ship management have focused on detecting and analyzing anomalous and illicit vessels. In this study, we introduce ShipGeoNet, a model designed to extract geometric features from ships captured in Sentinel-1 synthetic aperture radar (SAR) images. ShipGeoNet employs a combination of convolutional neural networks (CNNs) and nonlinear regression techniques to extract various geometric features of ships from SAR imagery. The model follows a two-step approach. First, it utilizes a modified Mask R-CNN architecture and the ViTDet model to accurately detect ships, generating high-quality object masks for precise localization. In the subsequent step, a regression model utilizes the detected ship masks to extract key geometric attributes, including length, width, and orientation. The proposed nonlinear regression techniques are specifically crafted to address the complex nonlinear deformations inherent in SAR images. Through extensive experiments on a large-scale SAR dataset, ShipGeoNet demonstrates its efficiency and accuracy in ship size extraction and matching, outperforming existing methods. Developing the ShipGeoNet model opens up possibilities for future applications in maritime surveillance, navigation, and environmental monitoring.

ABSTRACTThe combination of multi-source Geostationary Earth Orbit (GEO) satellites can realize simultaneous global observation with high frequency, which is significant to fine weather forecast and global climate change research. Here the radiance consistency and deviation characterization in the overlapping regions are analysed for three new generation GEO satellites, including the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite, the Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-17 (GOES-17) and the Advanced Geostationary Radiation Imager (AGRI) onboard FengYun satellite-4A (FY-4A). The result shows that the radiance has higher consistency for visible and near-infrared (VNIR) channels with correlation coefficient r > 0.98 than infrared (IR) channels with r > 0.90 in the overlapping regions of different GEO satellites. For FY-4A & Himawari-8, the average percentage deviation (APD) of VNIR channels is 5.54%–9.03%. The brightness temperature (BT) bias of IR channels is 1.16 K-2.50 K. For GOES-17 & Himawari-8, the APD of VNIR channels is 4.48%–6.71%, and the BT bias of IR channels is 0.96 K −2.76 K. Solar-viewing geometries significantly influence the radiance deviation in the over regions. The future work is to design a radiance fusion method and achieve cloud detection in ample space with high frequency by combining multi-source GEO satellites.

Oil film thickness (OFT) is an important indicator for estimating the amount of oil spill, and accurately quantifying the OFT is of great significance for loss assessment. In this article, hyperspectral images (HSIs) of different OFTs (0.01–3.04 mm) through a ground experiment were obtained, and the spectral characteristics were analyzed. To address the issue of poor spectral separability for different OFTs, the 1-D convolutional neural network_gate recurrent unit (1DCNN_GRU) model was developed for the quantitative inversion of OFT. It was validated through experiments on airborne Cubert-S185 HSI. The experimental results indicated that: 1) the proposed 1DCNN_GRU model effectively addressed the issue of reduced quantitative inversion accuracy resulting from poor spectral separability. The inversion results of it outperformed those of the support vector regression (SVR), convolutional neural network (CNN), and gate recurrent unit (GRU) models. Moreover, the optimal time for hyperspectral sensor to monitor OFT was at noon. 2) The proposed model using airborne hyperspectral data exhibited excellent inversion performance for OFT greater than 0.07 mm, especially with the best performance in 0.60–0.90 mm. 3) The accuracy of HSI-based OFT inversion assisted by brightness temperature (BT) data was superior to that of OFT inversion using single-source data. In particular, the proposed model had advantages in the feature level and decision level inversion of OFT in the ranges of 0.01–0.30 and 1.00–3.04 mm, respectively. This research provides technical support for the detection of OFT.

Autoencoders (AEs) are commonly utilized for acquiring low-dimensional data representations and performing data reconstruction, which makes them suitable for hyperspectral unmixing (HU). However, AE networks trained pixel by pixel and those employing localized convolutional filters disregard the global material distribution and distant interdependencies, resulting in the loss of necessary spatial feature information essential for the unmixing process. To overcome this limitation, we propose an innovative deep neural network model named U-shaped transformer network using shifted windows (UST-Net). UST-Net prioritizes spatial information in the scene that is more discriminative and significant by using multihead self-attention blocks based on shifted windows. Unlike patch-based unmixing networks, UST-Net operates on the complete image, eliminating inconsistencies associated with patches. Moreover, the downsampling and upsampling stages are used to extract hyperspectral image (HSI) feature maps at different scales. This process generates a context-rich and spatially accurate abundance map without losing local details. The experimental results of one synthetic dataset and three real datasets demonstrate that UST-Net significantly outperforms both traditional and several other advanced neural network methods. Our code is publicly available at https://github.com/UPCGIT/UST-Net.

Superpixel segmentation has emerged as a prominent approach for simultaneous extraction of spatial–spectral features in hyperspectral imagery, exhibiting considerable efficacy in this domain. Although effective in spatial spectrum feature extraction, the existing feature extraction algorithms typically perform superpixel segmentation on a single band, failing to utilize the rich spectral and spatial information available across more bands. Moreover, current superpixel feature extraction methods lack scientific guidance for determining optimal multiscale parameters, which can lead to suboptimal segmentation and increased the complexity of hyperspectral analysis. To overcome these limitations, this article presents a novel band-by-band adaptive multiscale superpixel feature extraction (BAMS-FE) method. The method comprises of two key components: a band-by-band superpixel-based feature extraction method and an adaptive optimal superpixel multiscale determination method. First, the band-by-band superpixel-based feature extraction method performs superpixel segmentation for each band of hyperspectral images (HSIs), thereby extracting joint spatial and spectral features. Second, the adaptive optimal superpixel multiscale determination method uses an unsupervised approach to determine the optimal multiscale superpixel segmentation parameters. Finally, the BAMS algorithm is obtained by combining the above two algorithms. The proposed algorithm is evaluated on five different datasets, and the results demonstrate its excellent precision and stability. With the top 99% principal components post-PCA transformation or with raw, unprocessed hyperspectral datasets, stable and satisfactory classification performance is achieved by BAMS. Additionally, we compared its performance with several other state-of-the-art algorithms and found that it outperformed them in terms of accuracy. Our code is publicly available at https://github.com/UPCGIT/BAMS-FE.