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The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution. The selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit, which will improve the timeliness of oil spill emergency monitoring. At the same time, the combination of spectral and spatial features can improve the accuracy of oil spill monitoring. Two ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions, for which the multiscale superpixel level group clustering framework (MSGCF) was used to select spectral feature bands with strong separability. In addition, the double-branch dual-attention (DBDA) model was applied to identify crude oil and its emulsions. Compared with the recognition results based on original hyperspectral images, using the feature bands determined by MSGCF improved the recognition accuracy, and greatly shortened the running time. Moreover, the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined, and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in 2010. This study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration, laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit. [ABSTRACT FROM AUTHOR]

Seagrass beds play a vital ecological role by regulating climate, maintaining biodiversity, and sequestering carbon. Achieving accurate classification of seagrass beds across both time and space is of significant importance for efficient monitoring. This paper addresses the challenges of seagrass bed classification in remote sensing imagery and the difficulty of achieving temporal and spatial transferability of classification model. Leveraging a long-term Landsat satellite image time series spanning from 1976 to 2022 in the Yellow River Delta, we enhance the DeepLab V3+ network and propose a cross-temporal-spatial domain seagrass bed transfer learning classification approach. We utilize selectively chosen images from a few years in partial regions for training, introduce freeze-training and-unfreeze-training during the model training phase, and then transfer the model to achieve cross-temporal and cross-temporal-spatial domain classification on images from other years and regions. Additionally, we discuss the factors influencing the classification results. The research results indicate that: (1)The method we proposed has shown good results, achieving classification accuracy of 83.17% and 80.99% for seagrass beds in both cross-temporal and cross-temporal-spatial domains. (2) The classification results indicate that this method achieves an average classification accuracy higher by 16.18% compared to networks such as UNet, PSPNet, HRNet, UNet++, SegFormer, etc. under cross-temporal conditions and higher by 17.56% under cross-spatiotemporal conditions for seagrass bed classification. (3) Based on the classification results, it is observed that from 1973 to 1986, the overall area of seagrass beds in the Yellow River Delta had an initial growth trend, with the largest seagrass bed area recorded in 1986, reaching 4281.289 hectares. Subsequently, the seagrass beds showed an overall decreasing trend, and by 2019 they had almost completely disappeared. The cross-temporal-spatial domain seagrass bed transfer learning classification method proposed in this paper can provide valuable support for the monitoring, restoration, management, and utilization of seagrass ecosystems. [ABSTRACT FROM AUTHOR]

Timely and accurate acquisition of winter wheat planting areas is crucial for food security. In this study, Sentinel-1 and Sentinel-2 time-series data are integrated at the feature level to enhance the accuracy of winter wheat extraction. However, existing feature-level fusion models suffer from insufficient feature extraction and lack of feature completeness, thereby overlooking the complementarity and correlation between these two modalities. A Multimodal Fusion Convolutional Neural Network (MMF-CNN) model is proposed to address the issues above. Firstly, the images of Sentinel-1 and Sentinel-2 are processed to obtain the NDVI and backscatter characteristics of the winter wheat time series life cycle. A single feature and a combination of two features are then imported into each end of the model. The model adds a feature fusion module, which can fully extract the feature information. At the same time, the original features are retained in the process of multiscale feature fusion, which avoids the loss of the original information. Finally, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Visual Geometry Group (VGG) are selected as the comparison models for comparative experiments, and the classification results of remote sensing images are obtained. These results demonstrate that the joint utilization of SAR and optical data yields the highest classification accuracy, with an F1 score of 97.42% for winter wheat. The overall accuracy (OA) of the proposed MMF-CNN method in this study is 96.87%, representing a 1.86% improvement compared to the Conv1D-CNN model. This improvement signifies adaptive feature learning at different hierarchical levels. Comparing the accuracy with other mainstream methods, the OA improves by 1.75%–4.37%, reveals finer ground details, and demonstrates faster performance. This study can provide methodological references for crop extraction studies based on multi-source data and time series analysis. [ABSTRACT FROM AUTHOR]

In the hyperspectral image (HSI) classification tasks, various deep learning models have achieved remarkable success. However, most deep learning models are compute-intensive, requiring significant computing power, time, and other resources. It becomes a challenge to pursue better results while saving computational resources. Therefore, a novel dual-input ultralight multi-head self-attention learning network (DUMS-LN) is proposed for HSI classification. The proposed DUMS-LN consists of three main core modules, namely the high-dimensional reduced module (HDRM), lightweight multi-head self-attention (LMHSA) module, and linearized hierarchical conversion module (LHCM). HDRM is used as a pre-processing module with efficient data compression and combines spatial and spectral information extraction from the raw data to provide cleaner and more comprehensive feature data for subsequent processing. In addition, the core computational module of DUMS-LN is the LMHSA module, which is lightweight but possesses better data processing capability than the traditional multi-head self-attention module. Finally, the LHCM divides the model into two phases, reducing the dimensionality of the feature data phase by phase so that the LMHSA module can perform feature extraction at different levels. Experiments on four benchmark HSI datasets show that the proposed DUMS-LN outperforms the comparison HSI classification algorithms regarding speed and classification accuracy. [ABSTRACT FROM AUTHOR]

China's first dynamic environment satellite constellation includes the HY-2B, HY-2C, and HY-2D satellites. In this study, the along track SLA, SWH, and SSWS of this satellite constellation were evaluated. SLA parameters are evaluated using self-crossing and dual-crossing methods. The SSWS and SWH data were evaluated by comparing with NDBC buoy and other available satellites' data. The evaluation revealed that the standard deviation of the SLA from the HY-2B/C/D satellites' single mission crossovers was 3.29 cm, 3.51 cm, and 3.72 cm, respectively. In addition, at the dual-crossovers of the Jason-3 satellite and the HY-2B satellite, the HY-2B satellite, and the HY-2C/D satellites, the standard deviation was determined to be 3.40 cm, 3.48 cm, and 4.25 cm, respectively. The accuracy of the SWH products of the HY-2B/C/D satellite radar altimeters was observed to be 0.23 m, 0.25 m, and 0.26 m, respectively. The accuracy of the SSWS data of the HY-2B/C/D satellite radar altimeters was observed to be 1.48 m/s, 1.59 m/s, and 1.35 m/s, respectively. In addition, this study also analyzed and compared the observation efficiency of the dynamic environment satellite constellation with the following six satellites: Sentinel-3(A, B), Jason-3, Sentinel-6A, Saral, and Cryosat-2. Observation efficiency refers to selection of any point on the globe to find a minimum radius of at least one observation point within a circle in a 14-day period. The analysis results demonstrated that observation efficiency of China's first dynamic environment satellite constellation was comparable to that of the six satellites. [ABSTRACT FROM AUTHOR]

The unavoidable nature of Ulva prolifera mixed pixel in low-resolution remote sensing images would result in rough boundary of U. prolifera patches, omission of tiny patches, and overestimation of coverage area. The decomposition of U. prolifera mixed pixel addresses the issue of coverage area overestimation, and the remaining problems can be alleviated by subpixel mapping (SPM). Due to the drift and dissipation of U. prolifera, a suitable SPM method is the single image-based unsupervised method. However, the method has difficulties in detail reconstruction, insufficient learning of spectral information, and SPM error introduced by abundance deviation. Therefore, we proposed a multiple-feature decision fusion SPM (MFDFSPM) method. It involves three branches to obtain the spatial, abundance, and spectral features of U. prolifera while considers multi-feature information using the fusion strategy. Experiments on the Geostationary Ocean Color Imager images in the Yellow Sea of China indicate that the MFDFSPM overperforms several typical U. prolifera SPM methods in higher accuracy and stronger robustness in both SPM and abundance calculation, which produced subpixel map with more detailed spatial information and less noise. [ABSTRACT FROM AUTHOR]

Chlorophyll a concentration and suspended matter concentration, as typical water quality parameters related to spectral characteristics, are essential for characterizing the degree of eutrophication in water bodies. They have become crucial indicators for water quality assessment of inland water bodies. The support vector regression model (SVR) is suitable for small samples, has excellent generalization ability, and has high prediction accuracy. Still, it has the problem of difficult selection of model parameters and quickly falling into local extremes. To solve this problem, a hybrid Differential Evolution-Grey Wolf Optimizer (DE-GWO) algorithm is introduced into the parameter selection process of the support vector regression model, and an improved SVR algorithm (DE-GWO-SVR) is proposed for the remote sensing inversion of chlorophyll a concentration and suspended sediment concentration in water bodies. In this paper, the spectral reflectance of the water surface and the chlorophyll a and broken matter concentration values were obtained by field measurements in the Tangdao Bay waters of Qingdao, Shandong Province. The inverse model between the concentration values of the two water quality parameters and the corresponding sensitive factors was established by first determining the sensitive factors based on the response of the spectral reflectance to the two water quality parameters and introducing the DE-GWO optimization algorithm into the parameter selection process of the SVR model. Finally, the accuracy of the model was verified using Sentinel II satellite remote sensing spectral data, and then the inverse accuracy of the two water quality parameters was obtained. The mean relative error (MRE) of the chlorophyll a prediction model built by the DE-GWO algorithm optimizing the SVR is 25.1%, and the mean relative error (MRE) of the suspended matter prediction model is 32.5%. The inversion results were all better than the other models (linear regression, SVR, and GWO-SVR model). When the best model, built from the measured water surface spectral data, was applied to the Sentinel II satellite data, the improved SVR model outperformed the other models in terms of mean relative error. The experimental results confirm that the DE-GWO-SVR algorithm is an effective method for remote sensing inversion of chlorophyll a and suspended matter concentrations in water bodies, which can provide a reference for remote sensing inversion of chlorophyll a and suspended matter concentrations in Chinese offshore waters and subsequent scientific management of waters. [ABSTRACT FROM AUTHOR]

Oil spills on the sea surface have caused serious harm to the marine ecological environment and coastal environment. Oil film thickness (OFT) is an important parameter for estimating oil spills amount, and accurate quantification of OFT is of great significance for rapid response and risk assessment of oil spills. In recent years, thermal infrared remote sensing has been gradually applied to quantify the OFT. In this paper, the outdoor oil spill simulation experiments were designed, and the bright temperature (BT) data of different OFTs were obtained for 24 consecutive hours in summer and autumn. On the basis of the correlation analysis of OFT and bright temperature difference (BTD) between oil and water, the traditional regression fitting model, classical machine learning model, ensemble learning model, and deep learning model were applied to the inversion of OFT. At the same time, inversion results of the four models were compared and analyzed. In addition, the best OFT inversion time using thermal infrared was studied based on 24-h thermal infrared data. Additionally, the inversion results were compared with the measured results; the optimal OFT range detectable using thermal infrared was explored. The experimental results show that: (1) Compared with ensemble learning model, traditional regression fitting model, and classical machine learning model, Convolutional Neural Network (CNN) has the advantages of high stability while maintaining high-precision inversion, and can be used as the preferred model for oil film thickness inversion; (2) The optimal time for OFT detection is around 10:00 to 13:00 of the day, and is not affected by seasonal changes; (3) During the day, thermal infrared has good detection ability for OFT greater than 0.4 mm, and weak detection ability for thinner oil films; (4) At night, thermal infrared has certain detection ability for relatively thick oil film, but the accuracy is lower than that in the daytime. [ABSTRACT FROM AUTHOR]

Marine oil spills can cause serious damage to marine ecosystems and biological species, and the pollution is difficult to repair in the short term. Accurate oil type identification and oil thickness quantification are of great significance for marine oil spill emergency response and damage assessment. In recent years, hyperspectral remote sensing technology has become an effective means to monitor marine oil spills. The spectral and spatial features of oil spill images at different levels are different. To accurately identify oil spill types and quantify oil film thickness, and perform better extraction of spectral and spatial features, a multilevel spatial and spectral feature extraction network is proposed in this study. First, the graph convolutional neural network and graph attentional neural network models were used to extract spectral and spatial features in non-Euclidean space, respectively, and then the designed modules based on 2D expansion convolution, depth convolution, and point convolution were applied to extract feature information in Euclidean space; after that, a multilevel feature fusion method was developed to fuse the obtained spatial and spectral features in Euclidean space in a complementary way to obtain multilevel features. Finally, the multilevel features were fused at the feature level to obtain the oil spill information. The experimental results show that compared with CGCNN, SSRN, and A2S2KResNet algorithms, the accuracy of oil type identification and oil film thickness classification of the proposed method in this paper is improved by 12.82%, 0.06%, and 0.08% and 2.23%, 0.69%, and 0.47%, respectively, which proves that the method in this paper can effectively extract oil spill information and identify different oil spill types and different oil film thicknesses. [ABSTRACT FROM AUTHOR]

As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computing resources and has good application prospects. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) has caused severe damage to ecosystems worldwide. Monitoring PWD is urgent due to its rapid spread. Unsupervised methods are more suitable for the monitoring needs of PWD, as they have the advantages of being fast and not limited by samples. We propose an unsupervised method with decision fusion that combines adaptive threshold and Lab spatial clustering. The method avoids the sample problem, and fuses the strengths of different algorithms. First, the modified ExG-ExR index is proposed for adaptive threshold segmentation to obtain an initial result. Then, k-means and Fuzzy C-means in Lab color space are established for an iterative calculation to achieve two initial results. The final result is obtained from the three initial extraction results by the majority voting rule. Experimental results on unmanned aerial vehicle images in the Laoshan area of Qingdao show that this method has high accuracy and strong robustness, with the average accuracy and F1-score reaching 91.35% and 0.8373, respectively. The method can help provide helpful information for effective control and tactical management of PWD. [ABSTRACT FROM AUTHOR]

As an important remote sensing technology, satellite altimetry provides a large amount of observations of sea surface height over the global ocean. In coastal areas, the accuracy of satellite altimetry data decreases greatly due to issues arise in the vicinity of land, related to poorer geophysical corrections and artifacts in the altimeter reflected signals linked to the presence of land within the instrument footprint. To improve the application of HY-2B altimetry data in coastal areas, this study proposes a coastal waveform retracking strategy for HY-2B altimetry mission, which depends on the effective trailing edge and the leading edge, which are less affected by coastal 'contamination', to retrieve accurate waveform information. The HY-2B pass 323 and pass 196 data are reprocessed, and the accuracy of the reprocessing results in the range of 0–40 km offshore is validated against the tide gauge data and compared with the HY-2B standard SGDR data. According to the analysis conclusion, the accuracy of the reprocessed data is higher than that of the SGDR data and has good performance within 15 km offshore. For the pass 323, the mean value of correlation coefficient and RMS of the reprocessed data against the corresponding tide gauge data are 0.893 and 45.1 cm, respectively, in the range within 0–15 km offshore, and are 0.86 and 33.6 cm, respectively, in the range beyond 15 km offshore. For the pass 196, the mean value of correlation coefficient and RMS of the reprocessed data against the corresponding tide gauge data in the range within 0–12 km offshore are 0.84 and 33.0 cm, respectively, and in the range within 0–5 km offshore to the island are 0.90 and 29.3 cm, respectively, and in the range beyond 5 km offshore to the island are 0.92 and 36.2 cm, respectively, which are all better than the corresponding values of the SGDR data, especially in the range closed to the land. The results indicate that the proposed coastal waveform retracking strategy for HY-2B altimetry greatly improves the quality of HY-2B altimetry data in coastal areas. [ABSTRACT FROM AUTHOR]

Mixed pixels are the main reason for the low accuracy of traditional remote sensing applications. Hyperspectral image unmixing can explore the sub-pixel information of mixed pixels, which is an effective measure to solve the problems of low precision. Current unmixing methods only consider the correlation of local or global pixels and do not fully utilize images information. Based on the above problems, we proposed a novel sparse unmixing model named combining low-rank constrain for similar superpixels and total variation sparse unmixing (CLRSS-TV) in this current research paper. The adjacent similar pixels are clustered first into superpixels, then the superpixels with the same semantic information are merged into a collection. The spatial information of the image is fully considered by twice clustering. Weighted low-rank constraint is imposed on each collection to consider the spectral correlation of pixels. Moreover, the TV regularization and l2,1 norm are utilized to improve the smoothness and row sparsity, respectively. Experimental results reveal that the proposed method is not only competitive with the four state-of-the-art algorithms in simulated and actual datasets, but also has good anti-noise ability and universality. However, this paper still fails to overcome the problem of parameters selection in sparse unmixing, so the future work study intends to adopt the intelligent optimization algorithm to obtain the optimal parameters. [ABSTRACT FROM AUTHOR]

Efficient methodologies for vegetation-type mapping are significant for wetland's management practices and monitoring. Nowadays, dynamic time warping (DTW) based on remote sensing time series has been successfully applied to vegetation classification. However, most of the previous related studies only focused on Normalized Difference Vegetation Index (NDVI) time series while ignoring multiple features in each period image. In order to further improve the accuracy of wetland vegetation classification, Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series was employed in this research. This method extends the traditional DTW algorithm based on single-dimensional features to multi-dimensional features and solves the problem of calculating similarity distance between multi-dimensional feature time series. Vegetation classification experiments were carried out in the Yellow River Delta (YRD). Compared with different classification methods, the results show that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) has achieved better classification accuracy; the overall accuracy is more than 90%, and kappa is more than 0.9. [ABSTRACT FROM AUTHOR]

With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained. [ABSTRACT FROM AUTHOR]