Purpose This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed. Design/methodology/approach First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions. Findings Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future. Originality/value This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.
The underwater hyperspectral imager enables the detection and identification of targets on the seafloor by collecting high-resolution spectral images. The distance between the hyperspectral imager and the targets cannot be consistent in real operation by factors such as motion and fluctuating terrain, resulting in unfocused images and negative effects on the identification. In this paper, we developed a novel integrated underwater hyperspectral imaging system for deep sea surveys and proposed an autofocus strategy based on liquid lens focusing transfer. The calibration tests provided a clear focus result for hyperspectral transects and a global spectral resolution of less than 7 nm in spectral range from 400 to 800 nm. The prototype was used to obtain spectrum and image information of manganese nodules and four other rocks in a laboratory environment. The classification of the five kinds of minerals was successfully realized by using a support vector machine. We tested the UHI prototype in the deep sea and observed a Psychropotidae specimen on the sediment from the in situ hyperspectral images. The results show that the prototype developed here can accurately and stably obtain hyperspectral data and has potential applications for in situ deep-sea exploration.
The ocean connects all continents and is an important space for human activities. Ship detection with electro-optical images has shown great potential due to the abundant imaging spectrum and, hence, strongly supports human activities in the ocean. A suitable imaging spectrum can obtain effective images in complex marine environments, which is the premise of ship detection. This paper provides an overview of ship detection methods with electro-optical images in marine environments. Ship detection methods with sea–sky backgrounds include traditional and deep learning methods. Traditional ship detection methods comprise the following steps: preprocessing, sea–sky line (SSL) detection, region of interest (ROI) extraction, and identification. The use of deep learning is promising in ship detection; however, it requires a large amount of labeled data to build a robust model, and its targeted optimization for ship detection in marine environments is not sufficient.
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