本文選題：高光譜 + 目標(biāo)探測��； 參考：《中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)》2017年博士論文

【摘要】：高光譜目標(biāo)探測可對目標(biāo)及背景進行精準(zhǔn)的識別,在揭露低可探測目標(biāo)方面獨具優(yōu)勢和潛力,已成為一個目標(biāo)探測領(lǐng)域的前沿研究方向。陰影是遙感圖像中普遍存在的一類現(xiàn)象,陰影區(qū)域反射光能量偏低,在高光譜圖像中相應(yīng)區(qū)域的光譜信號本身較弱,信噪比相對于非陰影區(qū)顯著降低,使得判斷圖像中陰影內(nèi)有無目標(biāo)成為一個常見而棘手的問題。然而,當(dāng)前針對陰影內(nèi)目標(biāo)的高光譜目標(biāo)探測的研究仍處于發(fā)展階段,相關(guān)陰影內(nèi)目標(biāo)探測的影響因素研究還存在不足,也缺乏陰影內(nèi)目標(biāo)探測的有效算法和可行技術(shù)方案。因此,本論文從陰影對目標(biāo)光譜影響機理的研究出發(fā),利用地面高光譜成像數(shù)據(jù),探索研究陰影區(qū)域目標(biāo)探測的影響因素,分析對陰影內(nèi)目標(biāo)探測最優(yōu)適應(yīng)性算法,針對迷彩偽裝目標(biāo)探測改進多目標(biāo)高光譜探測算法,并基于此構(gòu)建陰影區(qū)目標(biāo)探測策略,準(zhǔn)確提取陰影范圍并提升陰影區(qū)目標(biāo)探測精度。研究不僅可為高光譜遙感目標(biāo)探測能力分析提供技術(shù)和數(shù)據(jù)支撐,更能為地面目標(biāo)反高光譜偵察能力評估提供理論和數(shù)據(jù)依據(jù),對解決高光譜遙感陰影區(qū)軍事目標(biāo)探測問題具有重要意義。本文所得到的主要結(jié)論包括:(1)四個典型監(jiān)督探測算法中ACE算法對不同探測背景、不同光照條件下的目標(biāo)探測效果最為穩(wěn)定,其后依次是CEM算法和OSP算法。相對而言,SAM算法對背景和光照條件均敏感,表現(xiàn)為適應(yīng)性最差;(2)對于陰影區(qū)目標(biāo)探測,異常探測算法難以達到理想的探測效果;經(jīng)典的RXD算法對光照區(qū)目標(biāo)探測效果尚可,無法直接探測陰影內(nèi)目標(biāo);LPTD算法更適于探測強反射目標(biāo),對光譜存在顯著起伏特征的目標(biāo)探測效果較差;(3)分別利用區(qū)域生長分割法和基于最大類間方差(Otsu)閾值分割法實現(xiàn)了高光譜圖像陰影檢測,結(jié)果表明區(qū)域生長法檢測結(jié)果更優(yōu);其次將圖像分割成光照區(qū)、半影區(qū)和陰影區(qū),對半影區(qū)和陰影區(qū)分別進行陰影去除和信息恢復(fù),使得恢復(fù)后的圖像色調(diào)均勻,因此將圖像細(xì)分為光照區(qū)、半影區(qū)和陰影區(qū)能顯著提升恢復(fù)圖像的質(zhì)量。(4)引進矩匹配法的思想對圖像進行了陰影去除,在一定程度上恢復(fù)了陰影區(qū)的光譜信息,使得原本被陰影壓制的目標(biāo)光譜特征得以發(fā)掘。經(jīng)過對陰影區(qū)光譜恢復(fù)后的圖像進行經(jīng)典高光譜目標(biāo)探測算法實驗,結(jié)果表明陰影去除后,所有算法探測精度均有不同程度的提升,其中已知目標(biāo)未知背景的ACE和CEM算法的探測效率提升效果明顯;SAM算法也取得了一定的提升效果,但是由于該算法的探測值(包括目標(biāo)和背景的探測值)均分布在探測結(jié)果灰度圖的高灰度值范圍,目標(biāo)與背景的分離效果不是很好;改進的MTCEM算法取得了0.9956的探測精度。(5)針對迷彩偽裝目標(biāo)的多本征光譜特點,選用MTCEM算法進行目標(biāo)探測,與CEM算法相比,目標(biāo)探測精度明顯增強。針對MTCEM算法在目標(biāo)對背景影響方面固有的缺陷,基于數(shù)學(xué)形態(tài)學(xué)提出一種改進的MTCEM算法。實驗結(jié)果表明,改進的MTCEM算法較改進前的探測精度明顯提升,遵循陰影檢測、去除、改進MTCEM探測的技術(shù)路線,可以實現(xiàn)同時探測陰影區(qū)內(nèi)多類目標(biāo)的優(yōu)異探測效果。(6)對陰影條件下目標(biāo)探測的空間尺度和光譜尺度進行了分析,結(jié)果表明,草地背景下目標(biāo)與背景有一定的相似性,要取得一定的探測效果目標(biāo)需要占據(jù)87%以上的像元豐度;路面背景下目標(biāo)與背景的差異性較大,目標(biāo)僅需占據(jù)37%以上的像元豐度便能被探測;草地背景下當(dāng)光譜分辨率低于50nm時目標(biāo)與背景的反射峰特征消失,探測效果下降嚴(yán)重,目標(biāo)不能被探測;路面背景下當(dāng)光譜分辨率低于50nm時目標(biāo)的反射峰特征消失,雖然目標(biāo)與背景差異性較大但是探測精度仍然出現(xiàn)了嚴(yán)重下降趨勢。實驗結(jié)果表明當(dāng)完整保留目標(biāo)與背景的反射峰、吸收谷時目標(biāo)探測的精度最高。
[Abstract]:Hyperspectral target detection can accurately identify the target and background. It has a unique advantage and potential in exposing low detectable targets. It has become a frontier research direction in the field of target detection. Shadow is a common phenomenon in remote sensing images. The reflected light energy of the shadow region is low and the corresponding region of the hyperspectral image is light. The spectral signal itself is weak and the signal to noise ratio is significantly reduced relative to the non shaded region. It makes it a common and difficult problem to judge whether there is a target in the shadow in the image. However, the current research on the detection of hyperspectral targets for the object in the shadow is still in the developing stage, and the research on the influence factors of the target detection in the shadow is still inadequate. Therefore, based on the study of the influence mechanism of the shadow on the target spectrum, this paper makes use of the ground hyperspectral imaging data to explore the influence factors of the target detection in the shadow region, and analyze the optimal adaptive algorithm for the target detection in the shadow, and aim at the camouflage target. The detection algorithm of multi-target hyperspectral detection is improved, and the shadow area target detection strategy is constructed to accurately extract the shadow range and improve the precision of the target detection in the shadow region. The study not only provides technology and data support for the analysis of the ability of hyperspectral remote sensing target detection, but also provides a theory for the evaluation of the anti high spectral reconnaissance capability of the ground target. The main conclusions of this paper are as follows: (1) the ACE algorithm in four typical supervised detection algorithms is the most stable for different detection backgrounds, and the target detection results under different illumination conditions are most stable, followed by the CEM algorithm and the OSP algorithm. The SAM algorithm is sensitive to the background and illumination conditions and shows the worst adaptability. (2) for the shadow region target detection, the anomaly detection algorithm is difficult to achieve the ideal detection effect. The classical RXD algorithm can not detect the target in the shadow directly, and the LPTD algorithm is more suitable for detecting the strong reflection target. The target detection results with significant undulating features are poor. (3) the region growth segmentation method and the maximum inter class variance (Otsu) threshold segmentation method are used to detect the hyperspectral image shadow. The results show that the region growth method has better results. Secondly, the image is divided into light area, penumbra region and shadow region, and the shadow region and shadow are distinguished. Do not remove the shadow and restore the information, make the image after the restoration even, so the image is subdivided into light area, the shadow region and the shadow region can significantly improve the quality of the restoration image. (4) the idea of introducing the moment matching method to remove the shadow of the image, to a certain extent, restore the spectral information of the shadow area, so that the original is negative. The spectral features of the shadow suppression are excavated. The classical hyperspectral target detection algorithm experiments are carried out after the restoration of the shadow region. The results show that the detection accuracy of all algorithms has been improved in varying degrees after the shadow removal, and the efficiency of the ACE and CEM algorithm known as the unknown background of the target is obvious; SAM is calculated. The method has also achieved a certain enhancement effect, but because the detection value of the algorithm (including the detection value of the target and background) is distributed in the high gray scale of the gray scale of the detection results, the separation effect between the target and the background is not very good; the improved MTCEM algorithm has obtained 0.9956 detection precision. (5) the multiple characteristic spectrum for the camouflage target. The MTCEM algorithm is used to detect the target. Compared with the CEM algorithm, the target detection precision is obviously enhanced. In view of the inherent defects of the MTCEM algorithm in the background, an improved MTCEM algorithm is proposed based on the mathematical morphology. The experimental results show that the improved MTCEM algorithm improves the detection accuracy obviously before the improvement and follows the Yin. Image detection, removal, and improvement of the technical route of MTCEM detection can be used to detect the excellent detection results of multiple targets in the shadow area. (6) the spatial scale and spectral scale of the target detection under the shadow condition are analyzed. The results show that the target and the back scene are similar in the background of the grassland, and a certain detection effect should be obtained. The standard needs to occupy more than 87% of the pixel abundance; the target and the background are very different in the background. The target only needs to occupy more than 37% of the pixel abundance and can be detected. In the background, when the spectral resolution is lower than 50nm, the characteristics of the target and the background are disappeared, the detection effect is serious and the target can not be detected; under the background of the road surface, When the spectral resolution is lower than 50nm, the characteristic of the reflection peak of the target is disappearing. Although the difference between the target and the background is larger, the detection precision still has a serious decline. The experimental results show that the target detection accuracy is the highest when the target and the background are fully retained and the valley is absorbed.
【學(xué)位授予單位】：中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP751

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本文編號：2022613