Soil salinity is challenging to measure accurately because soil is highly heterogeneous. This study first explores the feasibility of retrieving near-surface soil salinity using the GNSS transmissive signals received by an antenna shallowly buried underground. Soil salinity is measured by calculating the power attenuation of the signal in the soil received by the underground antenna with respect to the reference antenna mounted above the ground using GNSS Carrier-to-Noise Ratio (CNR) observations. Three-day observations in the Yellow River Delta, a typical saline-alkali land, are used to verify the approach. The results show that GNSS-derived soil salinity across different bands follows the overall trend measured by the laboratory. The GPS L1 and BDS B3 bands exhibit the highest retrieval accuracy, with the Root Mean Square Error (RMSE) being 0.15% and 0.25%, respectively, and achieved an average RMSE of 0.20%. The findings of this study initially show the potential of the proposed GNSS transmission mode to retrieve soil salinity and provide supportive information for the future development of new instruments.

Despite the proven effectiveness of deep learning technology in pine tree disease segmentation, acquiring a large volume of labeled data remains challenging and inefficient. Few-shot segmentation (FSS) uses a small amount of labeled data to guide the segmentation of unknown categories, further evolving into one-shot segmentation (OSS), which utilizes a single labeled sample to perform segmentation under conditions of extreme data scarcity. However, these methods are mostly applicable to natural images with clear boundaries and have not yet been applied to segmenting pine tree disease in autonomous aerial vehicle (AAV) remote sensing images. For this reason, we have designed the OSS model C2Net for the first time, which includes two main modules: 1) a prior contour awareness module (PCAM) that first generates a query image prior mask with contour response and then uses an iterative feature refinement unit (FRU) to refine features and accurately delineate the segmentation boundaries of pine tree disease and 2) an interclass relationship module (ICRM), which studies the vegetation index features of the support and query images, constructing importance weights that reflect the differences between categories, solving the visual similarity issue. Our experiments on field-collected and publicly available datasets demonstrate that C2Net excels in challenging OSS tasks, showing its ability to generalize across different sensor domains and various disease categories. Especially, on the field acquisition dataset, using just a single labeled pine tree disease image achieves an intersection over union (IoU) of 55.24% and an$F_{1}$of 71.24%.

Hyperspectral unmixing (HU) can obtain subpixel-level ground object information, which is crucial for the fine advancement of imaging spectrum processing technology. Deep learning (DL) has been widely used in HU recently because of its ability to deeply mine complex relevant features in data. Existing DL unmixing methods usually operate only in the original spatial-spectral feature domain. However, due to noise, spectral variation, and other factors, it is difficult to fully mine effective features and easy to interfere with by only relying on the original domain. To get over these obstacles, we propose an innovative stationary wavelet convolutional network (SWC-Net) for HU. Stationary wavelet transform (SWT) is introduced in SWC-Net to extend the original feature domain to feature domains with different frequencies, which promotes the multiview extraction of information. What is more, a new generative self-supervised feature learning strategy based on wavelet perspective (GSFL-W) is proposed for SWC-Net. More robust features can be obtained by GSFL-W by introducing noisy perturbations into high-frequency inputs and forcing the network to generate the original inputs. The proposed SWC-Net surpasses the advanced approaches by sufficient experiments on one simulated and three real hyperspectral datasets. The code is publicly available athttps://github.com/UPCGIT/SWC-Net.

Due to the limitations of imaging environment and hardware conditions, mixed pixels are common in hyperspectral images, which seriously affects the accuracy of land use coverage mapping. Subpixel mapping (SPM) decomposes mixed pixels to obtain the spatial distribution information of local object components inside the pixel, thereby breaking through the limitations of traditional pixel-level classification and achieving more accurate land use interpretation and refined mapping. Recently, deep convolutional neural networks have demonstrated their potential and effectiveness in SPM. However, in the SPM process, the multiscale spatial context information are not fully utilized in the process of using semantic information for network modulation, and the spatial representation at a more abstract level cannot be fully obtained. Therefore, in response to the above problems, this article proposes a multiscale semantic modulation hybrid convolutional network for SPM. The network obtains multiscale semantic information in semantics by constructing a multiscale semantic modulation module (MSSM) to modulate the backbone network and fully mine the spatial context information. Simultaneously, a hybrid convolutional module integrating, 2-D convolutional neural networks, 3D convolutional neural networks, and attention mechanisms is designed. This module captures joint spatial–spectral features while reducing model complexity and learns more abstract spatial representations to enhance the network’s performance in SPM. Experimental results show that this method outperforms the most advanced SPM methods on three public datasets and a produced wetland dataset, and the details of land cover categories are more prominent. The code and data will be released on GitHub upon acceptance:https://github.com/UPCGIT/MSMCNet

Vision transformers are widely used in hyperspectral image 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 hyperspectral images (HSIs) with a large number of bands generally suffer from high computational complexity and a large number of parameter requirements. In this paper, we propose an Ultra-lightweight Feature-compressed Multi-head Self-attention Learning Network (UFMS-LN), which mainly consists of a novel Compressed Feature Multi-Head 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 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.

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.

In this paper, the spatial and temporal coverage of satellite SST in the Arctic region is studied by using the SST data of polar orbit spaceborne microwave radiometer (Windsat, AMSR2, HY-2A RM, GMI) in 2016, and the accuracy of SST data is evaluated by using the measured data of Argo. The results show that, the space borne microwave radiometer SST retrievals coverage rate and effective coverage days in winter are lower than that in summer. When AMSR2, GMI, WindSat and HY-2A RM space borne microwave radiometer SST data are combined used, the SST coverage rate can be between 12%-15% in February, and the number of effective observation days is better than 26 days. The error of the space borne microwave radiometer SST data in the Arctic is larger than that of the global average. The accuracy of AMSR2 data is the best one, the accuracy of WindSat data is close to that of AMSR2. The RMSE of GMI SST is about 2 times larger than AMSR2, and the accuracy of HY-2A RM data is lower than that of any other space borne microwave radiometer.

Through the oil spill simulation experiment of designing outdoor small scenes, this paper finds that: (1) the thermal infrared image obtained by the two sensors shows that the diurnal variation trend of the thermal infrared brightness temperature (BT) value of different OFT is the same, but the values will be different depending on the sensitivity of the sensor and the shooting angle. (2) the larger the oil film thickness (OFT), the larger the brightness temperature difference (BTD) between oil and water in the daytime, while the relationship between OFT and BTD is not monotonous in the nighttime. When the OFT is less than 317μm, the larger the OFT, the smaller the BTD, and when OFT is greater than 317μm, the conclusion is reversed. (3) there is a strong correlation between OFT and BTD. The correlation is greatest around noon, which is most conducive to the detection of OFT.

Object identification is one of the fundamental challenges in computer vision. Dealing with tiny, fuzzy, and widely dispersed objects is challenging despite tremendous improvements. This article introduces the detector in our model as a tool for detecting tiny objects in SAR (Synthetic Aperture Radar) photos. To overcome the aforementioned issues, the suggested model makes use of shallow layer information, spatial attention, and contextual information. To avoid missing the tiny object features, a shallow semantic information extraction model that incorporates low-level semantic data into the backbone has been designed. The original Neck has been replaced by an MSCFP (Multi-scale Context Feature Pyramid) in order to enhance the exploitation of lower levels of information and provide context information. The recognition of attention locations of various sizes is made possible by the introduction of a spatial attention system. Tests using open-source SSDD (SAR Ship Detection Dataset) datasets show that our model has effective identifying capabilities.

Precise and fast extraction of sea ice leads is the foundation for polar research and ship navigation. The accuracy of traditional methods for sea ice lead extraction is limited, and the efficiency of deep learning methods is difficult to guarantee. Besides, the tedious preprocessing steps complicate the application of existing methods. In this article, we proposed a lightweight semantic segmentation model based on the U-Net framework for sea ice lead extraction, which introduced lightweight blocks and a feature branch. Lightweight blocks took the place of the convolutional layers in U-Net to reduce the network parameters and increase operational speed. With the input of the contrast feature of horizontal-vertical (HV) polarization, the feature branch was used to improve the extraction precision and robustness. Besides, the combination of the lightweight blocks, feature branch and U-Net framework was beneficial to resist preprocessing. In the experiments, the performance of the proposed method on non-preprocessed Sentinel-1 dataset was better than that of the classical semantic segmentation method on preprocessed Sentinel-1 dataset in floating-point operations (FLOPs), parameters, frames per second (FPS), and accuracy evaluation. The results indicate that the proposed network is effective and lightweight for sea ice lead extraction from non-preprocessed data.

Due to the low resolution of hyperspectral images, the problem of mixed pixels is common, and hyperspectral unmixing is a crucial technology to solve the problem of mixed pixels. Among them, nonnegative matrix factorization (NMF) is widely used because it can simultaneously perform endmember and abundance estimations. As a variant of NMF, the archetype analysis (AA) is to find the most representative sample in the dataset, which has strong interpretability compared with NMF. However, traditional AA-based unmixing methods consider only one spectral curve to represent one class, ignoring endmember variability. To solve this problem, a manifold regularized sparse AA unmixing method considering endmember variability is proposed. In this paper, various spectra were included for each class to fully account for variability. In addition, considering the sparsity of abundance, L2,1regularization is used to impose sparse constraints on abundance, which ensures the sparseness of abundance. Furthermore, a manifold regularization constraint is introduced to use the underlying manifold structure of the data in unmixing, the construction of which is done by superpixel segmentation. The close relationship between the original image and the abundance is preserved. Experimental results on both synthetic and real hyperspectral datasets illustrate that the proposed method is superior to several multi-endmember extraction algorithms, AA-based algorithms, and advanced sparse NMF-based algorithms.

Autoencoders are widely utilized in hyperspectral unmixing as an unsupervised end-to-end learning model. In particular, convolutional autoencoder networks are popular for processing multidimensional hyperspectral features. Nonetheless, the traditional convolutional Autoencoder network’s receptive field is constrained in the unmixing task, and establishing the connection between the local spatial neighborhood and the local spectrum fails to improve unmixing performance significantly. To address these limitations, a bilateral global attention network based on both spatial and spectral information is proposed. It enables the network to obtain respective feature dependencies in the two dimensions and achieve optimal fusion of both features. The network comprises two information extraction branches. The spatial information extraction branch uses the Swin Transformer block to acquire the global spatial attention of the overall image, while the spectral information extraction branch designates a simplified spectral channel attention mechanism to gain spectral attention weight maps. The network’s efficacy is demonstrated through a comparative study using a synthetic dataset and two real datasets. The code of this work is available at https://github.com/UPCGIT/SSABN.

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 complexity of hyperspectral analysis. To overcome these limitations, this paper presents a novel band-by-band adaptive multiscale superpixel feature extraction method (BAMS-FE). The method comprises of two key components: a band-by-band superpixel-based feature extraction method and an adaptive optimal superpixel multiscale determination method. Firstly, the band-by-band superpixel-based feature extraction method performs superpixel segmentation for each band of hyperspectral images, thereby extracting joint spatial and spectral features. Secondly, 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 will be publicly available at https://github.com/UPCGIT/BAMS-FE.

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 paper, 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 1DConvolutional 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 SVR, CNN, and 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 0.60-0.90mm. (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 0.01-0.30mm and 1.00-3.04mm, 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. 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 multi-head 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 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.

Hyperspectral unmixing (HU) is widely used to process mixed pixels as an essential technology. Among them, the nonnegative matrix factorization (NMF)-based approach is one typical of the blind unmixing techniques, which can achieve endmembers and abundances simultaneously. Considering the physical meaning of the extracted endmembers, the archetypal analysis (AA) method constructs a new matrix decomposition structure with stronger interpretability than NMF. However, AA ignores the significant sparse property of abundance in unmixing. Therefore, we propose the L1sparsity-constrained AA algorithm for HU. To solve the new optimization problem, we explore a new optimization method for optimizing abundance. The alternating direction method of multipliers (ADMM) is used to increase the strong convexity and convergence of the problem. Then the fast gradient method (FGM) instead of traditional gradient descent is used to speed up algorithm convergence. The experimental results in both the synthesized and real datasets show that the proposed method outperforms several sparse NMF-based and AA-based methods.

Drought is one of the agricultural natural disasters, which seriously threatens the natural ecological environment and world's food security. The FY-3B satellite has the ability of drought monitoring, but its spatial resolution is low, which isn't suitable for small and medium scale drought monitoring. In view of this, this paper calculates the High resolution Soil Moisture Drought Index (HSMDI) in the study area through the effective fusion of MODIS optical data which is small and medium scale and FY-3B soil moisture data of large scale. The results were verified by the measured meteorological data and simulated soil moisture data, which showed that HSMDI had a good consistency with precipitation and soil moisture (P < 0.05). The spatial and temporal characteristics of drought described by HSMDI were consistent with the actual drought situation in the study area, indicating that the method can effectively achieve small and medium scale drought monitoring.

Yellow Sea and Bohai Sea are the most frequent sea fog regions in China. The research on sea fog detection methods is of great significance to sea safety and human production activities. In this paper, the MODIS images are used for sea fog detection experiment from 2016 to 2018. The sea fog detection threshold algorithm is established based on MODIS 1, 2, 3, 5, 17, 26 and 32 bands. The temporal and spatial characteristics of sea fog are analyzed in the Yellow Sea and Bohai Sea.

In this paper, the spectral and texture features of Sargassum first are analyzed through calculating four measures of GLCM and sampling spectrum from typical pixels of Sargassum blooms with high-resolution satellite data. The four-dimensional spectral bands of the image, the first principal component and NVDI are used as the spectral features of the image, and four measures of GLCM are used as the texture features of the image. And then the Sargassum is extracted using SVM by constructing spectral-texture eigenvectors. The experiment achieves superior results compared with the conventional NDVI threshold method.

In this paper the quality of Jason-3 data was evaluated based on Jason-2 GDR data in the tandem stages. The percentage of data loss and data edition and the daily mean changes of the main physical parameters are calculated for each cycle. The sea surface height discrepancy at the intersection point and the sea level anomaly along the track are analyzed, and the system deviation between Jason-2 and Jason-3 are calibrated. Jason-3 and Jason-2 data have uniform change trend and spatial distribution in significant wave heights(SWH), mean backscattering coefficient, ionosphere delay correction mean values and mean wet tropospheric delay differences between microwave radiometer observations and ECMWF model, and there are systematic deviations between Jason-3 and Jason-2. The standard deviations of the sea surface height bias at the self-intersection of the Jason-3 and Jason-2 are 4.98cm and 4.94cm, respectively. It can be indicated that the accuracy of Jason-3 is comparable to that of Jason-2 with the systematic deviation of 2.93cm.

The sea level rise rate of the China Sea and its vicinity is 4.16 mm/a based on the altimeter data of TOPEX/Poseidon, Jason-1, Jason-2 and Jason-3 satellites. Then the sea level time series are denoised by Morlet wavelet transform. And the multi-time scale characteristics of sea level time series are analyzed by wavelet transform. The results show that the strength and distribution of various periods and the breaking point can be given by the wavelet analysis. The sea level change in China Sea and its vicinity is dominated by annual signals with signal periods of 1.5a and 4a.

Poor visibility by sea fog is a navigational hazard for maritime transport. Due to the lack of observation stations on the sea, monitoring large-scale sea fog and assessing navigation risk in foggy season are challenges. Thus, the paper introduced MODIS data for monitoring sea fog and proposed an approach of assessing navigation risk along fairway during foggy season. Sea fog is firstly identified from MODIS images using supervised classifier and then navigation risk along fairway is quantitatively evaluated by means of spatial analysis. Using GIS based Multi-criteria Decision Analysis (MCDA) model, a risk index layer is respectively constructed for various marine environmental factors, which include sea fog, wind, wave and etc. and these risk index layers eventually are integrated into navigation risk assessment layer through the order weighted average (OWA) operator.

The ocean gravity field has always been an important part of marine scientific research. Combining ship survey data and satellite data to obtain high-resolution and high-precision gravity data attracts many researchers. In this paper, we present two fusion methods of gravity anomaly data, which are data fusion based on the analysis of error trend surface and the data fusion method based on the combination of net function and fractal interpolation. The experimental results show that the data fusion based on the analysis of error trend surface can effectively improve the effect of data fusion. The data fusion method based on the combination of net function and fractal interpolation is not obvious for accuracy improvement, but it shows great potential in terms of detail.