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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]

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. [ABSTRACT FROM AUTHOR]

Autoencoders (AEs) are widely utilized in hyperspectral unmixing (HU) as an unsupervised learning model. In particular, convolutional AE networks are popular for processing multidimensional hyperspectral features. Nonetheless, the traditional convolutional AE 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. [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]

The Sargassum bloom has severely impacted the ecological environment of the East China Sea and the Yellow Sea, causing significant economic losses. In recent years, deep learning has seen extensive development due to its outstanding feature extraction capabilities. However, the deep learning process typically involves a large number of parameters and computations. To address this issue, this paper proposes a lightweight deep learning network based on the U-Net framework, called SLWE-NET, which uses lightweight modules to replace the feature extraction modules in U-Net. In this experiment, SLWE-Net performed the best in both extraction accuracy and model lightweight. Compared to the formal U-Net, the number of parameters decreased by 65.83 %, the model size reduced from 94.97 MB to 32.51 MB, and the mIoU increased to 93.81 %. Therefore, the method proposed in this paper is beneficial for Sargassum extraction and provides a basis for operational monitoring. • The SLWE-Net model is an effective model for Sargassum detection through satellite images. • Using VoVNet as the feature extraction module of the model can better extract the target and achieve higher accuracy. • In terms of Sargassum extraction accuracy, SLWE-Net outperforms traditional spectral vegetation indices. • SLWE-Net has achieved notable advancements in model lightweight by decreasing parameter count and computation complexity. [ABSTRACT FROM AUTHOR]

In the oil industry, oil spills occur due to offshore rig explosions, ship collisions, and other reasons. It is crucial to accurately and rapidly identify oil spills to protect marine ecosystems. Synthetic aperture radar (SAR) can all-weather and all-time work and provide a wealth of polarization information for identification of oil spills based on semantic segmentation model. However, the performance of classifiers in the semantic segmentation model has become a significant challenge to improving recognition ability. To solve this problem, an improved semantic segmentation model named DRSNet was proposed, which uses ResNet-50 as the backbone in DeepLabv3+ and support vector machines (SVM) as the classifier. The experiment was conducted using ten polarimetric features from SAR images and results demonstrate that the DRSNet performs best compared to other semantic segmentation models. Current work provides a valuable tool to enhance maritime emergency management capabilities. • Ten key polarimetric features are used for oil spill detection. • ResNet-50 as the backbone in encoder of DeepLabv3+, can get more feature map extraction. • SVM as the classifier can trained by the more sufficiently feature map to identify oil spill pixels. [ABSTRACT FROM AUTHOR]

The current limited spaceborne hardware resources and the diversity of ship target scales in SAR images have led to the requirement of on-orbit real-time detection of ship targets in spaceborne synthetic aperture radar (SAR) images. In this paper, we propose a lightweight ship detection network based on the YOLOv4-LITE model. In order to facilitate the network migration to the satellite, the method uses MobileNetv2 as the backbone feature extraction network of the model. To solve the problem of ship target scale diversity in SAR images, an improved receptive field block (RFB) structure is introduced, enhancing the feature extraction ability of the network, and improving the accuracy of multi-scale ship target detection. A sliding window block method is designed to detect the whole SAR image, which can solve the problem of image input. Experiments on the SAR ship dataset SSDD show that the detection speed of the improved lightweight network could reach up to 47.16 FPS, with the mean average precision (mAP) of 95.03%, and the model size is only 49.34 M, which demonstrates that the proposed network can accurately and quickly detect ship targets. The proposed network model can provide a reference for constructing a spaceborne real-time lightweight ship detection network, which can balance the detection accuracy and speed of the network. [ABSTRACT FROM AUTHOR]

Oil spill pollution at sea causes significant damage to marine ecosystems. Quad-polarimetric Synthetic Aperture Radar (SAR) has become an essential technology since it can provide polarization features for marine oil spill detection. Using deep learning models based on polarimetric features, oil spill detection can be achieved. However, there is insufficient feature extraction due to model depth, small reception field lend due to loss of target information, and fixed hyperparameter for models. The effect of oil spill detection is still incomplete or misclassified. To solve the above problems, we propose an improved deep learning model named BO-DRNet. The model can obtain a more sufficiently and fuller feature by ResNet-18 as the backbone in encoder of DeepLabv3+, and Bayesian Optimization (BO) was used to optimize the model's hyperparameters. Experiments were conducted based on ten prominent polarimetric features were extracted from three quad-polarimetric SAR images obtained by RADARSAT-2. Experimental results show that compared with other deep learning models, BO-DRNet performs best with a mean accuracy of 74.69% and a mean dice of 0.8551. This paper provides a valuable tool to manage upcoming disasters effectively. [ABSTRACT FROM AUTHOR]