發(fā)布時間：2018-09-07 13:33

【摘要】：遙感影像空間分辨率的提高為城市發(fā)展的監(jiān)測與規(guī)劃提供了大量地面細(xì)節(jié)信息,使得城市遙感變化檢測研究成為當(dāng)前遙感研究領(lǐng)域的熱點(diǎn)之一。然而,數(shù)據(jù)信息量的急劇增加,也為遙感影像變化檢測技術(shù)的發(fā)展提出了新的問題與挑戰(zhàn)：首先,豐富的地物細(xì)節(jié)信息使得單一地物對象由多個空間相鄰的像元組成,單個像元的光譜變化不足以反映其所在地物的變化情況；其次,影像空間分辨率提高、光譜分辨率受限,導(dǎo)致同類地物光譜差異變大、不同地物光譜相互重疊,“同物異譜、異物同譜”的現(xiàn)象普遍存在；再次,多時相高分辨率影像成像條件的差異,導(dǎo)致同一地物在不同時相的影像中呈現(xiàn)出光譜、空間特征的差異,僅僅通過影像預(yù)處理很難徹底消除這些差異,例如：針對海拔高度較高的地物,多時相成像角度的差異將嚴(yán)重影響變化檢測效果；最后,成倍增長的多時相影像數(shù)據(jù)量使得對算法自動性的要求更高。 本文在現(xiàn)有變化檢測技術(shù)的基礎(chǔ)上,針對高分辨率遙感影像中的地物對象變化情況,提出了幾種新的面向?qū)ο蟮亩嗵卣髯兓瘷z測模型。它們分別著眼于改善面向?qū)ο蟮淖兓瘷z測方法中的對象“勻質(zhì)性”問題,提高自動搜索全局最優(yōu)的變化檢測結(jié)果的能力,解決多源影像光譜分辨率差異、多時相影像復(fù)合分割誤差的問題,改進(jìn)多時相影像房屋變化顯著性度量方式,以及提高針對多時相成像角度差異帶來的房屋“偽變化”的容錯性變化檢測能力等等。通過利用QuickBird、 IKONOS等高分辨率遙感衛(wèi)星影像數(shù)據(jù)進(jìn)行實(shí)驗(yàn),驗(yàn)證了各種檢測模型的有效性。 為了引入本文提出的變化檢測模型,我們首先總結(jié)了傳統(tǒng)遙感影像變化檢測方法的基本思路,詳細(xì)介紹了影像預(yù)處理、變化信息提取、閾值選擇與精度評價(jià)這四項(xiàng)關(guān)鍵技術(shù)。其中,在變化信息提取的部分,詳細(xì)介紹了現(xiàn)有的基于像元光譜信息的變化分析方法中的代數(shù)運(yùn)算法、影像變換法,以及顧及影像空間信息的面向?qū)ο蟮姆椒ê突谏窠?jīng)網(wǎng)絡(luò)的方法,并運(yùn)用一組共用多時相QuickBird影像數(shù)據(jù)對這些方法進(jìn)行了實(shí)驗(yàn)驗(yàn)證與分析。結(jié)果證明：傳統(tǒng)的基于像元光譜信息的變化檢測算法由于沒有考慮影像空間上下文信息,已無法滿足高分辨率影像變化分析的需要；而現(xiàn)有的顧及影像空間信息的變化檢測算法雖然實(shí)現(xiàn)了對高分辨率影像空間信息的利用,但仍存在一些問題,包括面向?qū)ο蟮姆椒ㄖ械膶ο蟆皠蛸|(zhì)性”問題、復(fù)合影像分割失真的問題以及多時相影像成像角度差異對變化檢測結(jié)果的影響間題等等。由此引出了本文針對這些問題的具體解決方案： 首先,針對高分辨率遙感影像面向?qū)ο蟮淖兓治龅膬纱箨P(guān)鍵問題——閾值選擇對自動獲取全局最優(yōu)解的影響以及面向?qū)ο蠓椒ǖ膶ο蟆熬祷眴栴},本文提出了兩種新的檢測模型,在面向?qū)ο蟮乃枷胂?分別利用遺傳算法(Genetic Algorithm,GA)自動搜索全局最優(yōu)解的機(jī)制,以及針對多時相影像對象內(nèi)部像元光譜特征的K-S (Kolmogorov-Smirnov)統(tǒng)計(jì)檢驗(yàn),有效解決了上述問題。根據(jù)兩組多時相QuickBird影像數(shù)據(jù)的實(shí)驗(yàn)證明,基于GA的方法能夠通過循環(huán)迭代中的遺傳操作自動搜索全局最優(yōu)的地物對象變化檢測結(jié)果,避免了閾值選擇對算法自動性和最優(yōu)解選擇效果的影響：而基于K-S檢驗(yàn)的方法有效保留并考察了多時相影像對象內(nèi)部的像元光譜統(tǒng)計(jì)差異,解決了傳統(tǒng)面向?qū)ο蟮姆椒ㄖ械膶ο蟆熬祷眴栴},從不同角度提高了高分辨率遙感影像面向?qū)ο蟮淖兓瘷z測的有效性。 其次,通過分析總結(jié)多源高分辨率影像變化分析與同源影像的區(qū)別與聯(lián)系,總結(jié)了當(dāng)前多源影像變化分析的難點(diǎn),即多源影像的光譜分辨率差異問題。針對這一問題,我們提出了一種新的解決方案,根據(jù)變化區(qū)域與其所在對象的空間關(guān)系,定義多時相影像對象的相似性特征,提取相似性較小的影像對象并將其視為變化的影像區(qū)域。同時,該方法通過多時相影像分割映射的方式解決了影像復(fù)合分割誤差的問題,并針對不同基準(zhǔn)影像與不同空間尺度下的多類變化檢測結(jié)果進(jìn)行了影像區(qū)域融合的后處理。根據(jù)兩組獲取自QuickBird與IKONOS衛(wèi)星傳感器的多源多時相影像數(shù)據(jù)的實(shí)驗(yàn)驗(yàn)證與分析,證明了該方法能有效檢測多源高分辨率影像的變化區(qū)域。 最后,為了針對性地監(jiān)測反應(yīng)城市發(fā)展的房屋目標(biāo)變化情況,我們總結(jié)了高分辨率影像房屋變化分析現(xiàn)存的兩大問題：變化顯著性度量方式與多時相成像角度差異的影響。首先,針對變化顯著性度量方式的問題,提出了基于脈沖耦合神經(jīng)網(wǎng)絡(luò)(Pulse-Couplec Neural Network, PCNN)的房屋變化檢測方法,通過神經(jīng)網(wǎng)絡(luò)的構(gòu)建,充分考慮各時相房屋特征影像的對象空間上下文信息,并使用多種相關(guān)性度量方式,全面考察房屋對象的變化顯著性程度,并據(jù)此判斷房屋對象的變化情況。通過兩組多時相QuickBird影像的實(shí)驗(yàn)驗(yàn)證,證明了該方法能有效提取高分辨率影像的房屋變化區(qū)域。其次,為了盡可能地降低多時相成像角度差異對房屋變化檢測結(jié)果的影響,房屋容錯性變化檢測方法通過對多時相房屋特征點(diǎn)的局部影像匹配,容錯性地識別出不同時相中空間幾何分布特征存在差異的同一房屋對象,并將其從真實(shí)變化的房屋區(qū)域中剔除。通過多組QuickBird或IKONOS影像實(shí)驗(yàn)證明,該方法能有效降低多時相成像角度的差異導(dǎo)致的對房屋“偽變化”的誤檢,明顯提高了房屋變化檢測的精度。
[Abstract]:The improvement of remote sensing image spatial resolution provides a lot of ground detail information for urban development monitoring and planning, which makes the research of urban remote sensing change detection become one of the hot topics in the field of remote sensing. Firstly, the abundant detail information makes a single object consist of several adjacent pixels, and the spectral change of a single pixel is not enough to reflect the change of the object. Secondly, the spatial resolution of the image is improved, and the spectral resolution is limited, which results in the spectral difference of the same object becomes larger, and the spectra of different objects overlap each other. Thirdly, the different imaging conditions of multi-temporal and high-resolution images lead to the differences of spectral and spatial characteristics of the same object in different temporal images. It is difficult to completely eliminate these differences only through image pre-processing, such as: for high-altitude objects, more. The difference of temporal imaging angle will seriously affect the effect of change detection. Finally, the multiplication of multi-temporal image data makes the algorithm more automatic.
Based on the existing change detection techniques, several new object-oriented multi-feature change detection models are proposed for the change of objects in high-resolution remote sensing images. These models focus on improving the homogeneity of objects in object-oriented change detection methods to improve the global optimization of automatic search. The ability of change detection, the resolution difference of multi-source image, the error of multi-temporal image composite segmentation, the improvement of multi-temporal image house change saliency measurement method, and the improvement of the ability of fault-tolerant change detection for multi-temporal image angle difference caused by house "pseudo-change" and so on. Experiments on IKONOS and other high resolution remote sensing satellite images verify the effectiveness of various detection models.
In order to introduce the change detection model proposed in this paper, we first summarized the basic ideas of traditional remote sensing image change detection methods, and introduced four key technologies in detail: image preprocessing, change information extraction, threshold selection and accuracy evaluation. Algebraic operation, image transformation, object-oriented method considering image spatial information and neural network-based method are used in information change analysis. A group of common multi-temporal QuickBird image data are used to validate and analyze these methods. The results show that the traditional method based on pixel spectral information is effective. Change detection algorithms can not meet the needs of high-resolution image change analysis because they do not take into account the spatial context information of the image; while the existing change detection algorithms which take into account the spatial information of the image realize the use of high-resolution image spatial information, there are still some problems, including the object-oriented method of the pairing. Such as "homogeneity" problem, composite image segmentation distortion problem and multi-temporal image angle differences on the impact of change detection results and so on.
Firstly, aiming at the two key problems of object-oriented change analysis of high-resolution remote sensing images, the influence of threshold selection on automatic global optimal solution and the problem of object-oriented "mean" of object, two new detection models are proposed in this paper. Under the object-oriented idea, genetic algorithm (Genetic A) is used respectively. The mechanism of lgorithm (GA) searching global optimal solution automatically and the K-S (Kolmogorov-Smirnov) statistical test for the spectral characteristics of pixels in multi-temporal image objects have effectively solved the above problems. Experiments on two sets of multi-temporal QuickBird image data show that the GA-based method can be automatically operated by the genetic operation in the iterative cycle. Searching for globally optimal change detection results avoids the influence of threshold selection on algorithm automaticity and optimal solution selection effect. The K-S test-based method effectively preserves and inspects the statistical difference of pixel spectrum within multi-temporal image objects, and solves the problem of object "mean" in traditional object-oriented methods. It improves the effectiveness of object oriented change detection of high resolution remote sensing images from different perspectives.
Secondly, by analyzing and summarizing the differences and relations between multi-source high-resolution image change analysis and homologous image, the difficulty of multi-source image change analysis is summarized, that is, the spectral resolution difference of multi-source image. Meanwhile, this method solves the problem of image composite segmentation error by means of multi-temporal image segmentation and mapping, and detects multi-class changes for different reference images and different spatial scales. According to the experimental verification and analysis of two groups of multi-source and multi-temporal image data obtained from QuickBird and IKONOS satellite sensors, it is proved that this method can effectively detect the change area of multi-source and high-resolution images.
Finally, in order to monitor and reflect the changes of housing targets in urban development, we summarize two existing problems in high-resolution image housing change analysis: the impact of change saliency measurement and multi-temporal imaging angle differences. Firstly, to solve the problem of change saliency measurement, a new method based on pulse coupling God is proposed. Through the method of house change detection based on the Pulse-Couplec Neural Network (PCNN), and through the construction of the neural network, the object space context information of the house feature images at different time phases is fully considered, and a variety of correlation measures are used to comprehensively inspect the change significance of the house object and judge the change of the house object. The experimental results of two sets of multi-temporal QuickBird images show that the proposed method can effectively extract the change area of houses from high-resolution images. Secondly, in order to minimize the influence of the difference of multi-temporal imaging angles on the detection results of house changes, the method of house fault-tolerant change detection is based on the local shadows of multi-temporal house feature points. By image matching, the same building object with different spatial geometric distribution characteristics in different time phases can be identified faultlessly and removed from the real changing housing area. The accuracy of house change detection is obviously improved.
【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TP751;P237

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