本文選題：遙感圖像 + 目標檢測��； 參考：《西南交通大學》2014年碩士論文

【摘要】：隨著遙感技術的發(fā)展,遙感圖像由于覆蓋范圍廣、信息量大、觀測周期短等特點受到了廣泛的關注和研究,遙感圖像目標檢測作為遙感圖像解析的一個重要研究方向,其對于資源調查、災害監(jiān)測、資源勘探以及軍事目標的識別判讀等都具有重要的意義。由于遙感圖像數(shù)據(jù)量巨大,依靠傳統(tǒng)人工判讀的方式從中提取特定目標的信息難以適應遙感技術的發(fā)展趨勢,如何從這些數(shù)據(jù)中快速、準確的提取所需要的信息成為當今遙感圖像解析的重點與難點。本文以光學遙感圖像的目標檢測算法為研究對象,主要圍繞如何提高目標檢測算法效率與目標中心的預測精度進行研究。 本文在綜述遙感圖像目標檢測算法的基礎上對其歸納和總結,闡述算法的基本原理和檢測過程,并對經典的目標檢測算法進行仿真實驗和分析。文中首先闡述了特征提取對目標檢測的重要性,然后重點介紹了目標檢測中常用的幾種特征以及基于這些特征的飛機目標檢測算法,并對算法進行仿真實驗,分析算法中存在的不足之處。 其次針對顯著圖應用在遙感圖像中缺少客觀評價標準的問題,本文將顯著圖的量化評價標準引入到遙感圖像中。文中仿真對比分析了常用的三種顯著圖算法,并結合遙感圖像以及遙感圖像中飛機目標的特性,從主觀和客觀評價方面對顯著圖的突顯效果進行評價。在綜合考慮突顯精度和顯著區(qū)域突顯完整程度的情況下,Itti算法對于遙感圖像中飛機小目標的突顯效果優(yōu)于GBVS算法和SR算法。通過對顯著圖與遙感圖像模板匹配相結合的算法進行仿真實驗,驗證了基于顯著圖算法的目標檢測可以舍棄百分之七十以上的背景區(qū)域,縮小目標搜索空間是提高目標檢測算法效率的一種有效途徑。 最后,基于隨機森林目標檢測算法在霍夫投票階段是平均分配投票權重的,針對平均權重對目標位置預測不夠準確的問題,本文將原算法基于隨機森林葉子節(jié)點信息的平均權重改為基于樣本目標特征字典的指數(shù)分布投票權重。在訓練階段將樣本的目標像素特征向量存儲起來作為樣本字典,檢測階段對于隨機森林預測為樣本的像素,計算該像素點特征與字典中特征之間的歐式距離,根據(jù)該距離計算對應的指數(shù)函數(shù)分配權重,并對目標中心進行投票。通過仿真實驗分析了改進投票權重前后霍夫圖像的特點,并驗證這種改進能夠提高目標檢測算法的性能。
[Abstract]:With the development of remote sensing technology, remote sensing image has received extensive attention and research because of its wide coverage, large amount of information and short observation period. Target detection of remote sensing image is an important research direction in remote sensing image analysis. It is of great significance for resource investigation, disaster monitoring, resource exploration and military target recognition. Because of the huge amount of remote sensing image data, it is difficult to adapt to the development trend of remote sensing technology by relying on the traditional manual interpretation method to extract the information of specific target. Accurate extraction of information is becoming the focus and difficulty of remote sensing image analysis. In this paper, the object detection algorithm of optical remote sensing image is studied, which focuses on how to improve the efficiency of the target detection algorithm and the prediction accuracy of the target center. Based on the summarization of remote sensing image target detection algorithm, this paper summarizes its basic principle and detection process, and carries on the simulation experiment and analysis to the classical target detection algorithm. In this paper, the importance of feature extraction to target detection is first expounded, and then several commonly used features in target detection and aircraft target detection algorithms based on these features are introduced, and the simulation experiments are carried out. The shortcomings of the algorithm are analyzed. Secondly, aiming at the lack of objective evaluation criteria for salient maps in remote sensing images, this paper introduces the quantitative evaluation criteria of salient maps into remote sensing images. In this paper, three salient image algorithms are compared and analyzed, and the salient effect of salient map is evaluated from subjective and objective aspects by combining the characteristics of remote sensing images and aircraft targets in remote sensing images. Considering the accuracy of salience and the degree of salience integrity, Itti algorithm is superior to GBVS algorithm and SR algorithm in highlighting small targets in remote sensing images. Through the simulation experiment on the algorithm of combining salient map and template matching of remote sensing image, it is proved that the target detection based on salient map algorithm can discard more than 70% background area. Reducing target search space is an effective way to improve the efficiency of target detection algorithm. Finally, based on the stochastic forest target detection algorithm, the voting weight is distributed equally in the Hough voting stage, so the average weight is not accurate enough to predict the target position. In this paper, the average weight of the original algorithm based on random forest leaf node information is changed to an exponential distribution voting weight based on sample target feature dictionary. In the training stage, the target pixel feature vector of the sample is stored as a sample dictionary, and the Euclidean distance between the pixel feature and the dictionary feature is calculated in the detection stage. According to the distance, the corresponding exponential function is calculated to distribute the weight, and the target center is voted. The characteristics of Hough image before and after the improved voting weight are analyzed by simulation experiments, and it is verified that the improved algorithm can improve the performance of the target detection algorithm.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751

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本文編號：2103127