Hybrid model decision-based remote sensing image berthing ship detection method

Abstract

The invention discloses a hybrid model decision-based remote sensing image berthing ship detection method, and aims at realizing the accurate detection of ships in ports by adoption of a hierarchical algorithm framework. In a candidate area screening stage, rapid water separation is carried out on input high-resolution large-size port images, and candidate areas are rapidly screened on the basis of an all-around bi-dimensional cross scanning method. In a candidate area discrimination state, a method for carrying reliable discrimination on the candidate areas on the basis of a hybrid decision template is proposed. The method disclosed by the invention comprises the following steps of: firstly carrying out training to obtain three decision sub-models according to key positions and overall features of ships and a context of the surrounding environment; and carrying out candidate area discrimination on judgement results of the sub-models on the basis of the hybrid model decision template. Compared with the traditional method, the method disclosed by the invention has the advantages of effectively overcoming the adverse effects caused by the factors such as wide ship varieties, different berthing postures and partial ship body sheltering, and obtaining detection results with relatively high accuracy through relatively short time.

Description

technical field [0001] The invention relates to a method for detecting ships calling at a port in a remote sensing image based on mixed model decision-making. Background technique [0002] In recent years, with the development of high-resolution optical remote sensing platforms, ship detection based on high-resolution remote sensing images has become a research hotspot in marine applications of satellite remote sensing data. Due to its advantages of wide detection range and high timeliness, it has been applied in many important marine remote sensing fields, such as port dynamic monitoring, maritime management, ship rescue, and detection of smuggling activities. [0003] Its detection objects are mainly divided into two types: ocean-going ships and port ships. At present, a large number of existing ship detection methods are mainly researched on out-of-port and ocean-going ships, and these methods have achieved good results and applications. For port ship detection, since t...

AI Technical Summary

Problems solved by technology

However, compared with the detection of ocean-going and anchored ships (non-docked) in ports, there are fewer researches on the detection of moored ships in ports; the main difficulty is that the grayscale, texture, and artificial structure of ships are very similar to those of docks. , and the ship and the wharf are usually closely connected, making it difficult to effectively extract the ship area

Some existing ship detection methods in ports realize rapid port location based on known geographic information, so as to obtain the location of berthing ships, but prior geographic information positioning is only suitable for limited application scenarios, and the generalization is poor.

In addition, some methods achieve segmentation based on primary image features such as texture and grayscale. Such methods are susceptible to interference from shadows in the environment, complex artificial structures in the wharf, and different outfitting of the hull, and often cannot extract complete boundary contours, making it difficult to achieve docking The problem of effective segmentation of ships and docks is poor in robustness

Images