本文關鍵詞： 高光譜圖像 目標檢測 高階統計量 稀疏算法　出處：《西安電子科技大學》2014年碩士論文　論文類型：學位論文

【摘要】：近幾年，由于科學技術的迅猛發(fā)展，人們對“感知”提出了更高要求并得到了有效的延伸，同時，對事物的認識能力也得到了不斷的提高。過去幾十年正是成像光譜發(fā)展突飛猛進的階段，高光譜圖像的分析和處理成為當前國內外遙感圖像處理領域的研究熱點之一。高光譜圖像的突出特點是光譜分辨率高，可獲得觀測對象的幾十個或幾百個光譜波段的圖像信息,而成像光譜系統獲得的連續(xù)波段寬度一般都小于10nm。高光譜圖像是一種三維數據，，成像光譜儀為每個像素點提供一條近似連續(xù)的光譜曲線，而所有像素的相同波段對應一個二維圖像。 高光譜遙感圖像目標檢測技術是高光譜遙感理論以及實踐應用的核心環(huán)節(jié)。所謂高光譜圖像目標檢測，即利用已知的目標光譜信息在高光譜圖像中對感興趣的目標進行檢測、確認的技術。高光譜圖像目標檢測技術在軍事和民用領域中都有重要的應用價值。在軍事領域可用于對飛機、坦克等軍事目標進行檢測、定位，也可對偽裝的軍事目標進行檢測。在民用領域可應用于公共安全、環(huán)境監(jiān)控等領域。 本文在深入研究經典高光譜圖像目標檢測方法的基礎上，提出了兩個新的高光譜目標檢測框架。 由于目前存在的高光譜圖像目標檢測算法，大多是基于統計模型的檢測方法，利用了二階統計量進行目標檢測。然而，現實中的目標往往服從的是非高斯分布。根據ICA的理論基礎，針對非高斯分布目標的檢測問題應使用高階統計量進行檢測。本文中提出兩種采用高階統計量的檢測方法，多種目標材料檢測器（MultipleMaterials Detector，MMD）和基于擬牛頓法多種目標材料檢測器（Quasi-Newtonbased Multiple Materials Detector，QNMMD）。文章中從理論和實驗結果均說明，相對于現有的基于二階統計量的檢測方法，基于高階統計量的檢測方法有更好的檢測效果。 在本文中，利用高光譜圖像的稀疏模型，提出了兩種檢測方法。第一種是基于凸松弛法高光譜圖像目標探測器（Convex Relaxation Based Target Detector,CRBTD）。這個算法中的創(chuàng)新點在于提出了一個連續(xù)的凸函數近似l0范數。利用這個方法，可以將很難求解的NP-hard優(yōu)化問題轉化為容易求解的凸優(yōu)化問題，并且可以找到更準確的稀疏解。在實驗中，相比于目前存在的基于稀疏模型的高光譜目標檢測算法，CRBTD具有更好的檢測結果。第二種提出的算法是，基于k-mean聚類重建光譜庫的高光譜圖像目標檢測算法。在此算法中，通過對高光譜圖像進行k-mean聚類、目標光譜剔除并整合的處理，實現了光譜庫的自動構造。在真實高光譜數據的實驗結果中可以看到，基于自動光譜庫構造的稀疏檢測算法，在檢測效果上優(yōu)于傳統的統計算法。
[Abstract]:In recent years, due to the rapid development of science and technology, people have put forward higher requirements for "perception" and have been effectively extended at the same time. The ability to understand things has also been continuously improved. The past few decades is the stage of the rapid development of imaging spectrum. The analysis and processing of hyperspectral image has become one of the research hotspots in the field of remote sensing image processing at home and abroad. The outstanding feature of hyperspectral image is high spectral resolution. The image information of dozens or hundreds of spectral bands can be obtained, and the width of continuous band obtained by imaging spectral system is generally less than 10nm.Hyperspectral image is a kind of three-dimensional data. The imaging spectrometer provides an approximate continuous spectral curve for each pixel, and the same band of all pixels corresponds to a two-dimensional image. The object detection technology of hyperspectral remote sensing image is the core link of hyperspectral remote sensing theory and practical application, so called hyperspectral image target detection. That is, using the known target spectral information to detect the interested targets in hyperspectral images. Hyperspectral image target detection technology has important application value in both military and civil fields. It can be used to detect and locate military targets such as aircraft tanks and so on in the military field. It can also detect camouflaged military targets. It can be used in public safety, environmental monitoring and other fields. Based on the study of classical hyperspectral image target detection methods, two new hyperspectral target detection frameworks are proposed in this paper. Because of the existing hyperspectral image target detection algorithms, mostly based on the statistical model of detection methods, using second-order statistics for target detection. In reality, goals tend to conform to the distribution of non-#china_person0#. According to the theoretical basis of ICA. High order statistics should be used to detect non-#china_person0# distributed targets. In this paper, two detection methods using high order statistics are proposed. Multiple Materials Detector with multiple target material detectors. MMD) and quasi-Newton-based Multiple Materials Detector based on quasi Newton method. In this paper, the theoretical and experimental results show that the detection method based on higher order statistics is more effective than the existing detection methods based on second-order statistics. In this paper, the sparse model of hyperspectral images is used. Two detection methods are proposed. The first is based on convex relaxation hyperspectral image target detector (. Convex Relaxation Based Target Detector. The innovation of this algorithm is to propose a continuous convex function approximating l _ 0 norm. The NP-hard optimization problem which is difficult to solve can be transformed into a convex optimization problem which is easy to solve, and a more accurate sparse solution can be found. Compared with the existing hyperspectral target detection algorithm based on sparse model, CRBTD has better detection results. The target detection algorithm of hyperspectral image based on k-mean clustering reconstructing spectral database. In this algorithm, the target spectrum is eliminated and integrated by k-mean clustering of hyperspectral image. The experimental results of the real hyperspectral data show that the sparse detection algorithm based on the automatic spectral database is better than the traditional statistical algorithm.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751

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