A Ship Detection Method Based on Gist Feature Learning in Complicated Sea Surface Remote Sensing Images

Abstract

The invention discloses a method for detecting complex sea-surface remote sensing image ships based on Gist characteristic study, comprising the following steps: step 1, collecting the remote sensing image data of complex sea-surfaces which have different time phases, different sensors and different sizes; step 2, implementing the block preprocess for the complex sea-surface remote sensing image to obtain a sample image section and a detection image section; step 3, drawing the distinguishing features and the Gist features of the sample image section and the detection image section; step 4, training the sample image section according to the distinguishing features and the Gist features obtained in the step 3 to obtain a training mode; step 5, adopting an SVM (Support Vector Machine) classifier to judge whether the detection image section has ships according to the training mode obtained in the step 4; and step 6, finding the single ship of the detection image section based on an improved itti visual attention mode. The method for detecting the complex sea-surface remote sensing image ships based on Gist characteristic study ensures that the ships do not fail to examine, reduces the false alarm rate, effectively treats the complex sea-surface remote sensing images under the disturbed conditions of sea clutters, cloud and mist, and has low computation complexity and strong pertinence.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a ship detection method based on Gist biological visual feature learning for complex sea surface remote sensing images. Background technique [0002] Ship target detection is a traditional task of coastal countries in the world, and it has a wide range of applications in ship search and rescue, fishing boat monitoring, illegal immigration, territorial defense, anti-drugs, monitoring and management of illegal dumping of oil by ships, etc. . With the development of remote sensing imaging technology, it is possible to use remote sensing images to detect ship targets, and its research objects include the detection of ships themselves and ship wakes. Under complex marine environment conditions, satellite remote sensing images present chaotic fish scales, large reflective areas, and irregularly moving rich textured waves, etc. Small and medium-sized ship target...

AI Technical Summary

Problems solved by technology

When the image contains strong sea clutter, good detection results are often not obtained; the algorithm based on the distribution model first needs to make an assumption about the background clutter distribution, which requires certain prior knowledge, but in fact the general situation The background clutter does not strictly obey a certain distribution

Secondly, this type of algorithm needs to count each pixel in the image, so the amount of calculation is large, and it increases with the increase of the sliding window size; the algorithm based on the fractal model believes that the fractal dimension of natural scenery and ship targets has A certain difference is detected according to the difference

However, in actual images, affected by background complexity, random noise, and imaging quality, it is difficult to distinguish natural scenes from artificial targets with a single scale or constant fractal dimension; the target detection algorithm based on feature domains, when the gray distribution of the background is relatively When it is complex, it is greatly affected by noise. At this time, the influence of noise on the feature map will be increased, resulting in mis-segmentation

In addition, in the process of feature conversion, there will be some influence on the outline of the target itself. Using this method to segment the target, the shape information will be lost

[0004] It can be seen that various algorithms are still limited by many conditions, such as the interference of the image background, and the target is affected by weather and illumination changes. Especially for low- and medium-resolution remote sensing data, the ship target appears on the image as For small targets, the probability of missing alarms and false alarms in the monitoring process is high

At the same time, under normal circumstances, visible light images will be disturbed by clouds, oil pollution, sea waves, etc., and it is difficult to establish a background model. Obvious, and therefore not easy to separate, especially for black polar ships

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