Ship detection and classification pose critical challenges in maritime surveillance and monitoring. On the one hand, they find applications in civilian domains, like fishery management, illegal immigrant monitoring, and marine rescue. On the other hand, ships are important targets in maritime combat. Ship detection and classification help to obtain timely maritime military intelligence that holds immense importance in seizing control of the maritime battlefield and ensuring the success of military operations [1], [2]. Remote sensing technology, facilitated by sensors mounted on platforms like satellites and airplanes, enables long-distance noncontact imaging of ground targets. It offers high timeliness, wide coverage, and low-cost accessibility and is not constrained by geographic conditions. Consequently, it has emerged as the primary method for monitoring vessels [4], [5].

The South China Sea is a major shipping hub between the West Pacific and Indian Oceans. In this region, the demand for energy is enormous, both for residents’ daily lives and for economic development. Wave energy and wind energy are two major clean and low-cost ocean sources of renewable energy. The reasonable development and utilization of these energy sources can provide a stable energy supply for coastal cities and remote islands of China. Before wave energy and wind energy development, however, we must assess the potential of each of these sources. Based on high-resolution and high-accuracy wave field data and wind field data obtained by ERA-Interim reanalysis for the recent 38-year period from 1979–2016, the joint development potential of wave energy and wind energy was assessed in detail for offshore and nearshore areas in the South China Sea. Based on potential installed capacity, the results revealed three promising areas for the joint development of nearshore wave energy and wind energy, including the Taiwan Strait, Luzon Strait and the sea southeast of the Indo-China Peninsula. For these three dominant areas (key stations), the directionality of wave energy and wind energy propagation were good in various seasons; the dominant wave conditions and the dominant wind conditions were the same, which is advantageous for the joint development of wave and wind energy. Existing well-known wave energy converters (WECs) are not suitable for wave energy development in the areas of interest. Therefore, we must consider the distributions of wave conditions and develop more suitable WECs for these areas. The economic and environmental benefits of the joint development of wave and wind energy are high in these promising areas. The results described in this paper can provide references for the joint development of wave and wind energy in the South China Sea.

Bistatic and multi-static high-frequency surface wave radar (HFSWR) is becoming a prospective development trend for sea surface surveillance due to its potential in extending the coverage area, improving the detection accuracy, etc. In this paper, the vessel detection and tracking performance of a newly developed bistatic compact HFSWR system whose transmitting and receiving antennas are not co-located was investigated. Firstly, the representation of the target range and Doppler velocity concerning a bistatic HFSWR was derived and compared with that of a monostatic system. Next, taking the characteristics of target kinematic parameters into account, a target tracking method applicable to a bistatic HFSWR is proposed. The simultaneous target tracking results from both monostatic and bistatic HFSWR field data are presented and compared. The experimental results demonstrate the good performance in target tracking of the bistatic HFSWR and also show that an HFSWR system combining monostatic and bistatic modes has the potential to enhance the target track continuity and improve the detection accuracy.

Since the Industrial Revolution, methane has become the second most important greenhouse gas component after CO₂ and the second most important culprit of global warming, leading to serious climate change problems such as droughts, fires, floods, and glacial melting. While most of the methane in the atmosphere comes from emissions from energy activities such as petroleum refining, storage tanks are an important source of methane emissions during the extraction and processing of crude oil and natural gas. Therefore, the use of high-resolution remote sensing image data for oil and gas production sites to achieve efficient and accurate statistics for storage tanks is important to promote the strategic goals of “carbon neutrality and carbon peaking”. Compared with traditional statistical methods for studying oil storage tanks, deep learning-based target detection algorithms are more powerful for multi-scale targets and complex background conditions. In this paper, five deep learning detection algorithms, Faster RCNN, YOLOv5, YOLOv7, RetinaNet and SSD, were selected to conduct experiments on 3568 remote sensing images from five different datasets. The results show that the average accuracy of the Faster RCNN, YOLOv5, YOLOv7 and SSD algorithms is above 0.84, and the F1 scores of YOLOv5, YOLOv7 and SSD algorithms are above 0.80, among which the highest detection accuracy is shown by the SSD algorithm at 0.897 with a high F1 score, while the lowest average accuracy is shown by RetinaNet at only 0.639. The training results of the five algorithms were validated on three images containing differently sized oil storage tanks in complex backgrounds, and the validation results obtained were better, providing more accurate references for practical detection applications in remote sensing of oil storage tank targets in the future.