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Method for detecting and identifying airport target by using remote sensing image based on selective visual attention mechanism

A visual attention and random field technology, applied in the field of remote sensing image processing, can solve the problems of inaccurate airport positioning and reduce computational complexity, etc., and achieve the effect of solving inaccurate positioning, reducing computational complexity, and fast speed

Inactive Publication Date: 2011-10-12
FUDAN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

Compared with the traditional airport detection and identification method, the present invention does not need to analyze the original image pixel by pixel, which reduces the computational complexity, solves the problem of inaccurate airport positioning, and effectively reduces the surrounding complex background for detection. process disturbance

Method used

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  • Method for detecting and identifying airport target by using remote sensing image based on selective visual attention mechanism
  • Method for detecting and identifying airport target by using remote sensing image based on selective visual attention mechanism
  • Method for detecting and identifying airport target by using remote sensing image based on selective visual attention mechanism

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Effect test

experiment example 1

[0053] Experimental example 1. Some recognition results

[0054] In the method of the present invention, there are four main processes from the input to the saliency map, the candidate area, and the final recognition area, and each process has a corresponding result map, such as figure 1 shown. In this example, the parameters are taken as and . From figure 1 In (b), it can be seen that the airport area has a very high salience and is identified as the first candidate area. Although the existence of rivers around the airport makes some areas along the river also determined as candidate areas, the identification of SIFT features , which can well distinguish these candidate areas from the airport area. We show some other recognition results as figure 2 .

experiment example 2

[0055] Experimental example 2. Method comparison: recognition rate, recognition false alarm rate and running time

[0056] The method of the present invention is compared with the method of document [3] and document [5], and part result is as follows image 3 shown. Document [3] is a representative of the algorithm based on edge extraction. It performs edge detection on the input image, then removes small and curved edges and performs Hough transformation to obtain long straight lines, and substitutes the texture of the area near the straight line into the support vector machine. (Support Vector Machine, SVM) recognition. The method in literature [5] is a representative of the region segmentation algorithm. It first performs region segmentation on the image to be recognized; then extracts SIFT feature points from the entire image and clusters them according to the density of SIFT feature points; next, each The position of each group corresponds to the result of area segmenta...

experiment example 3

[0061] Experimental example 3. ROC curve performance test

[0062] ROC curve (receiver operating characteristic curve curve) [19] It is often used to measure the detection efficiency. Its abscissa is the detection false positive rate (False Positive Rate, FPR), and the ordinate is the detection accuracy rate (True Positive Rate, TPR). The detection accuracy rate here is different from the previous recognition rate. It is aimed at the problem of whether there is a target, and does not care whether the position of the target is correct. The detection false alarm rate is also relative to the detection accuracy rate. Draw the FPR and TPR under different judgment standards into a curve, the larger the area under the curve, the higher the detection efficiency. We draw the ROC curves of the three methods according to their respective discriminant criteria as follows: Figure 4 shown. From Figure 4 It can be seen that the ROC area of ​​the method of the present invention is sign...

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Abstract

The invention belongs to the technical field of remote sensing image target detection and identification, and in particular relates to a method for quickly detecting and identifying an airport target by using a remote sensing image based on a selective visual attention mechanism. By the method, the saliency of the original remote sensing image is analyzed by an improved attention selection model,namely graph-based visual saliency (GBVS), to acquire a saliency area, and the aim of identifying the airport target is fulfilled according to a scale-invariant feature transform (SIFT) characteristic on the area by combining a hierachical discriminant regression (HDR) tree. The method can effectively overcome the defect that the image is analyzed pixel by pixel in the conventional airport detection method. Compared with the conventional other airport detection methods, the method is characterized by high speed, high identification rate and low false alarm rate, has high noise robustness, andgreat significance and high value in practical application, and is very suitable for real-time detection under complex backgrounds in the fields of military use and civilian use.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a method for quickly detecting and identifying airport targets in remote sensing images. Background technique [0002] The detection and recognition of typical targets in remote sensing images is a hotspot in the current automatic target detection and recognition research. As a specific target, airports are of great significance in both military and civilian fields, and have received more and more attention. However, the regional background of the airport is often very complex, which brings difficulties to its detection and identification. [0003] Commonly used airport detection and identification methods are roughly divided into two categories [1] : Edge extraction based methods and region segmentation based methods. The former is based on the parallel long straight line characteristics of the airport runway, its edge is extracted from the...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/54G06K9/66
Inventor 王鑫王斌张立明
Owner FUDAN UNIV
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