High resolution remote sensing image airport detection method based on conditional random field model

Abstract

The invention relates to a high resolution remote sensing image airport detection method based on a conditional random field model. The method comprises the implementation procedures of firstly extracting the dense SIFT characteristics of high resolution remote sensing images, gaining the sparse codes of the dense SIFT characteristics on an over-complete dictionary, then establishing a 4-connection graph in the 4-neighborhood range of each sparse code, establishing a conditional random field model on the 4-connection graph, obtaining the parameters of the conditional random field model by learning a Max-margin algorithm, reasoning out the probability value of each sparse code belonging to an airport target through an LBP algorithm, thereby obtaining the probability graph of the airport target, and finally obtaining airport detection results by carrying out threshold segmentation on the probability graph of the airport target. According to the method, airport detection is carried out in the high resolution remote sensing images, and the method has the advantages of being high in accuracy and low in false alarm rate, and has quite high application value.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a high-resolution remote sensing image airport detection method based on a conditional random field model. Background technique [0002] With the rapid development of satellite and sensor technology, high-resolution remote sensing images provide very rich information for target detection such as airports. Effective use of this information can improve the performance of airport detection. Most existing algorithms realize airport detection by using geometric information such as the linear features of airport runways, but pure geometric information cannot effectively distinguish airports from highways, rivers, and artificial buildings. Chinese patent application number 201110166001.X describes a "selective attention mechanism-based airport target detection and recognition method for remote sensing images", using SIFT features to characterize airpo...

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Problems solved by technology

Most existing algorithms realize airport detection by using geometric information such as the linear features of airport runways, but pure geometric information cannot effectively distinguish airports from highways, rivers, artificial buildings, etc.

Chinese patent application number 201110166001.X describes a "selective attention mechanism-based airport target detection and recognition method for remote sensing images", which uses SIFT features to characterize airports to achieve airport detection, but the background of high-resolution remote sensing images is complex. Only extract SIFT local features for identification, which is easy to cause false detection

The Chinese Patent Application No. 201210282568.8 describes a "method for detecting airports in infrared remote sensing images based on sparse coding and visual saliency", which uses sparse coding guided by visual saliency to characterize airports. In low-resolution remote sensing images Good detection results have been achieved, but it is still not suitable for airport detection in high-resolution remote sensing images

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