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With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and producing training samples, the limited training sample is a common problem that researchers often face. Furthermore, efficient algorithms are necessary to excavate the spatial and spectral information from these images, and then, make full use of this information with limited training samples. To solve this problem, a novel two-branch deep learning network model is proposed for extracting hyperspectral remote sensing features in this paper. In this model, one branch focuses on extracting spectral features using multi-scale convolution and a normalization-based attention module, while the other branch captures spatial features through small-scale dilation convolution and Euclidean Similarity Attention. Subsequently, pooling and layering techniques are employed to further extract abstract features after feature fusion. In the experiments conducted on two public datasets, namely, IP and UP, as well as our own labeled dataset, namely, YRE, the proposed DMAN achieves the best classification results, with overall accuracies of 96.74%, 97.4%, and 98.08%, respectively. Compared to the sub-optimal state-of-the-art methods, the overall accuracies are improved by 1.05, 0.42, and 0.51 percentage points, respectively. The advantage of this network structure is particularly evident in unbalanced sample environments. Additionally, we introduce a new strategy based on the RPNet, which utilizes a small number of principal components for feature classification after dimensionality reduction. The results demonstrate its effectiveness in uncovering compressed feature information, with an overall accuracy improvement of 0.68 percentage points. Consequently, our model helps mitigate the impact of data scarcity on model performance, thereby contributing positively to the advancement of hyperspectral remote sensing technology in practical applications. [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]

An outdoor experiment was conducted to measure the thickness of oil films (0~3000 μm) using an airborne hyperspectral imager and thermal infrared imager, and the spectral response and thermal response of oil films of different thicknesses were analyzed. The classic support vector regression (SVR) model was used to retrieve the oil film thickness. On this basis, the suitable range for retrieving oil film thickness using hyperspectral and thermal infrared remote sensing was explored, and the decision-level fusion algorithm was developed to fuse the retrieval capabilities of hyperspectral and thermal infrared remote sensing for oil film thickness. The following conclusions can be drawn: (1) Based on airborne hyperspectral data and thermal infrared data, the retrieval accuracy of oil films of different thicknesses reached 154.31 μm and 116.59 μm, respectively. (2) The S185 hyperspectral data were beneficial for retrieving thicknesses greater than or equal to 400 μm, and the H20T thermal infrared data were beneficial for retrieving thicknesses greater than 500 μm. (3) The result of the decision-level fusion model based on a fuzzy membership degree was superior to those obtained using a single sensor (hyperspectral or thermal infrared), indicating that it can better integrate the retrieval results of hyperspectral and thermal infrared remote sensing for oil film thickness. Furthermore, the feasibility of using hyperspectral and thermal infrared remote sensing to detect water-in-oil emulsions of different thicknesses was investigated through spectral response and thermal response analysis. [ABSTRACT FROM AUTHOR]

Marine oil spills cause great pollution to the marine environment and require development of efficient cleaning plans. Accurate identification of the oil type involved in the spill is of great significance for rapid and effective treatment. Hyperspectral remote sensing plays an important role in oil spill detection and oil type identification. We designed an outdoor oil spill experiment to simulate an oil spill in a marine environment. Five common oil types were selected as the experimental starting materials: crude oil, fuel oil, diesel oil, gasoline, and palm oil. Hyperspectral data of the five oils were collected from different solar times by Analytical Spectral Devices (ASD) FieldSpec4. The relationship between the spectral absorption baseline height of the different oil types and solar time is investigated. The characteristic analysis method of spectral standard deviation was used to obtain characteristic bands of the different oil types. Using both full spectrum and selected characteristic bands, oil type identification experiments were performed using the Support Vector Machine (SVM) model, respectively. The results show that oil type identification using selected characteristic bands is 3.70% more accurate compared with that using the full spectrum, reaching 83.33%. [ABSTRACT FROM AUTHOR]

Wang, J.B.; Ren, G.B.; Lin, Z.Y.; Wang, A.D.; Hu, Y.B.; Li, X.M.; Wu, P.Q.; Ma, Y., and Zhang, J., 2020. Estimation and analysis of nitrogen contents in the yellow river estuary wetland using Gaofen-1 remote sensing data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 1-10. Coconut Creek (Florida), ISSN 0749-0208. Nitrogen storage is a key indicator of vegetation growth, and the spatial distribution of nitrogen storage provides important information about the ecological status of wetland ecosystems. In this study, the aboveground vegetation nitrogen content (AVNC) in the Yellow River estuary wetland is estimated based on GaoFen-1 wide field of view (GF-1 WFV) image. Based on the spatial distribution of vegetation and the fractional vegetation coverage (FVC), the characteristics of AVNC are analyzed. On the basis of this study, the best model for estimating AVNC based on vegetation indices is the sample rate index (SRI), whose determination coefficient (R2) is 0.72 and root mean square error (RMSE) is 1.5 g/m2. Per the results of this model, the total AVNC of the study area is obtained, which is 620.6 tons. The total ANVC for freshwater Phragmites australis (FPA), Tamarix chinesis shrub (TCS), Spartina alterniflora (SA), Suaeda salsa (SS), and tidal flat Phragmites australis (TFPA) communities are 296.2 t, 115.3 t, 97.1 t, 86.3 t and 25.7 t, respectively. The average AVNC per unit area for SA, FPA, TCS, TFPA, and SS communities are 4.2 g/m2, 4.8 g/m2, 3.1 g/m2, 1.7 g/m2 and 1.4 g/m2, respectively. The correlation between FVC and AVNC is also analyzed, from which, the spatial distribution of AVNC and FVC is found to be in good agreement. Results of this study thus provide a valuable reference for determining AVNC in coastal wetland ecosystems with implications for ecological health and other ecological characteristics. [ABSTRACT FROM AUTHOR]