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.

Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.

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.

This study is conducted in accordance with a systematic literature review (SLR) protocol. SLR is tasked with finding publications, publishers, deep learning types, enhanced and adapted deep learning techniques, impacts, proactive approaches, key parameters, and applications in the field of remote sensing. It is also expected to identify current research directions, gaps, and unsolved issues in order to provide understanding and recommendations for future studies. The data is collected from important research papers published in recognized journals between the years 2015 and 2021, however, conference/seminar proceedings and other online resources are excluded to minimize unnecessary complications. Based on previously established exclusion, inclusion, and quality parameter criteria, a total of 122 primary studies are considered. The literature review overcomes a number of significant problems, including key variables taken into account by researchers in the remote sensing (RS) domain, various deep learning (DL) solutions proposed for RS analysis, various proactive strategies recommended in the literature to reduce risks linked to the RS domain, and various DL applications reported in the remote sensing domain. The results show that there is still a lack of structured information that enables DL to be employed for crucial applications in the field of remote sensing, despite substantial research and development of numerous DL algorithms. Furthermore, it is evident that DL approaches in the remote sensing domain have not been thoroughly exploited, thus demanding further research. The findings suggest that deep learning techniques need further investigations and the development of an authentic mechanism is essential for accurate results retrieved from remote sensing data. The proposed study would let scientists examine previous investigations into deep learning methods, which can then be utilized as support for further investigations.

With a gradually rising global average sea level, it is of great significance to predict changes in sea level. However, sea level variations often exhibit both linear and nonlinear characteristics, complicating the prediction of sea level changes with a single model. The seasonal autoregressive integrated moving average (SARIMA) model can fully consider the linear characteristics of time series, but its nonlinear prediction ability is poor; the long short-term memory (LSTM) model can compensate for this shortcoming. To predict complex sea level changes, we propose a strategy to combine the SARIMA and LSTM models to increase the sea level prediction accuracy. In our method, sea level anomaly (SLA) time series are decomposed into the trend and seasonal term and random term; then, the SARIMA model is used to predict the trend and seasonal term of sea level variations, while the random term is predicted by LSTM. Sea surface height data from 1993 to 2018 are used in an experiment. Compared with other models, the performance of the SARIMA+LSTM model is superior in predicting sea level changes with a minimum root mean square error of 1.155 cm and a maximum determination coefficient (R2) of 0.89 during the testing period. Furthermore, the predicted results are in close agreement with the SLA data, which indicates that the SARIMA+LSTM model could be successfully used for the estimation of sea level variability.