視覺注意機制下面向對象高分辨率遙感影像建筑物提取
發(fā)布時間：2019-02-15 10:00
【摘要】：隨著航空航天技術的發(fā)展,人們獲取的遙感影像數(shù)據(jù)的空間分辨率不斷提高,同時獲取難度大幅降低。在遙感數(shù)據(jù)日益普及的今天,利用影像處理與分析技術對高空間分辨率遙感影像中的感興趣目標進行自動提取已經成為目前遙感領域研究的熱點之一。建筑物作為與人類生活密切相關的主要人工地物,是城市發(fā)展的重要標志。目前,通過對高分辨率遙感影像進行建筑物自動提取已經成為建筑物信息獲取的重要手段,而建筑物信息可用于進一步研究城市的擴張與發(fā)展、城市土地利用現(xiàn)狀與變化、城市規(guī)劃、城市熱島效應、人口估計與預測、災害監(jiān)測預警與評估等。 論文以基于信息理論的視覺注意機制和面向對象的影像分析方法為基礎,采用自底向上的遙感影像低層特征提取和自頂向下的先驗知識指導相結合的方式,提出了一種視覺注意機制下面向對象的高分辨率遙感影像建筑物提取框架。論文的主要研究工作包括以下幾個方面： (1)遙感影像色彩一致性處理方法研究。 遙感影像中的不均勻光照現(xiàn)象是一種影像降質現(xiàn)象,會在一定程度上增加影像解譯的難度。為了消除不均勻光照現(xiàn)象的影響,提高遙感影像建筑物提取的精度,論文對遙感影像色彩一致性處理方法展開了研究。針對傳統(tǒng)的色彩一致性處理算法會導致遙感影像中地物顏色失真這一問題,論文提出了一種顧及色調保持的HSV彩色空間小波域增強處理算法。算法首先將輸入影像變換到HSV彩色空間,然后對影像的亮度分量和飽和度分量進行小波分解,對小波系數(shù)采取低頻分量抑制、次高頻分量保持、高頻分量增強的處理。在此基礎上,針對薄云導致的光照不均勻現(xiàn)象,論文分析了薄云覆蓋下的遙感影像成像機理,提出了對薄云覆蓋影像的亮度分量和飽和度分量進行針對性處理的勻光算法。 (2)面向對象的高分辨率遙感影像多尺度分割方法研究。 面向對象的多尺度分割是面向對象的影像分析方法的前提和核心。如何描述遙感影像的低層特征,并結合多特征對影像進行面向對象的多尺度分割是現(xiàn)今面向對象分割算法的研究熱點。論文首先分析了傳統(tǒng)的影像分割方法以及面向對象的多尺度分割方法。然后,在比較了影像紋理特征描述方法的基礎上,論文提出了一種基于時頻分析的遙感影像紋理特征描述方法,并基于該方法構造了影像對象的紋理異質度的度量方法,采用自底向上的區(qū)域合并作為多尺度分割策略,提出了一種結合紋理特征的面向對象遙感影像多尺度分割算法。最后,論文從影像的邊緣特征出發(fā),提出了一種遙感影像的邊緣強度描述方法以及邊緣合并代價準則,并與異質度準則相結合,提出了結合光譜、形狀、紋理和邊緣特征的面向對象的多尺度分割算法。 (3)視覺注意機制下面向對象的建筑區(qū)提取方法研究。 建筑區(qū)提取可以作為建筑物提取中粗提取的過程,也可以將其提取結果作為建筑物的輔助場景信息。論文首先分析了高分辨率遙感影像中建筑區(qū)所特有的紋理表現(xiàn),基于論文提出的多尺度多方向的紋理描述方法并針對建筑區(qū)的紋理特性進行建筑區(qū)紋理增強。然后以建筑區(qū)紋理特征作為主要低層特征,采用基于信息理論的視覺注意模型,模擬人類視覺系統(tǒng)感知環(huán)境的過程,提出了一種視覺注意機制下的建筑區(qū)顯著指數(shù)(Built-up Areas Saliency Index,BASI)。最后基于面向對象的影像分析方法,對遙感影像進行顧及建筑區(qū)紋理特征的面向對象多尺度分割,再利用建筑區(qū)顯著指數(shù)對高分辨率遙感影像進行建筑區(qū)提取。 (4)面向對象的高分辨率遙感影像陰影提取方法研究。 高分辨率遙感影像中的建筑物通常伴隨有建筑陰影存在,這些陰影信息可作為高分辨率遙感影像建筑物提取的輔助判別依據(jù)。論文從陰影在高分辨率遙感影像中的特點出發(fā),分析了陰影在亮度、色調、以及各光譜波段中的特性,并以此構建陰影對象特征知識庫。然后采用面向對象的影像分析方法,在HSV彩色空間中對影像進行多尺度分割,最后根據(jù)特征知識庫形成判別準則提取陰影。 (5)視覺注意機制下面向對象的建筑物提取方法研究。 在高分辨率遙感影像中,建筑物往往具有高亮度、強邊緣、紋理明顯等特征。論文結合自頂向下的先驗知識與自底向上的遙感影像低層特征,提出了一種顧及建筑物特性的遙感影像特征空間變換方法,并結合論文提出的紋理特征和邊緣特征描述方法,構成了高分辨率遙感影像建筑物提取框架的主要特征。然后通過構造建筑物光譜特征指數(shù),提出了結合光譜和紋理特征的建筑物顯著指數(shù)(Building Saliency Index, BSI)。通過對預處理后的高分辨率遙感影像進行多特征結合的面向對象多尺度分割,然后計算高分辨率遙感影像的建筑區(qū)顯著指數(shù)BASI和建筑物顯著指數(shù)BSI,并將陰影信息和建筑區(qū)信息作為輔助判別依據(jù),在影像的多尺度對象集合中提取建筑物對象,最后對建筑物對象進行形態(tài)學平滑處理,實現(xiàn)了高分辨率遙感影像的建筑物提取。
[Abstract]:With the development of the aeronautics and astronautics technology, the spatial resolution of the acquired remote sensing image data is increasing, and the acquisition difficulty is greatly reduced. With the increasing popularity of remote sensing data, the automatic extraction of the object of interest in the high-space-resolution remote sensing image by using the image processing and analysis technology has become one of the hot spots in the field of remote sensing. As the main artificial ground feature which is closely related to human life, the building is an important symbol of urban development. At present, the automatic extraction of the high-resolution remote sensing image has become an important means of building information acquisition, and the building information can be used to further study the expansion and development of the city, the current situation and the change of the urban land use, the urban planning, the urban heat island effect, Population estimates and projections, disaster monitoring and early warning and assessment. On the basis of the visual attention mechanism based on the information theory and the object-oriented image analysis method, the paper adopts the combination of the low-level feature extraction of the remote sensing image and the top-down prior knowledge guidance. In this paper, an object-oriented high-resolution remote sensing image building extraction under the visual attention mechanism is presented. The main research work of the paper includes the following Aspect: (1) The color consistency of remote sensing image The non-uniform illumination in remote sensing image is an image degradation phenomenon, which can increase to a certain extent. In order to eliminate the influence of the non-uniform illumination, the accuracy of the extraction of the remote sensing image is improved, and the color consistency of the remote sensing image In order to solve the problem of the color distortion in the remote sensing image, the paper presents a small HSV color space, which takes into account the color tone, which can cause the color distortion of the ground objects in the remote sensing image. The method comprises the following steps of: firstly, transforming an input image into an HSV color space, and then carrying out small wave decomposition on the luminance component and the saturation component of the image, In this paper, the image forming mechanism of remote sensing image under the cover of thin cloud is analyzed, and the luminance component and the saturation component of the thin cloud cover image are put forward. a uniform light algorithm for sexual processing. (2) object-oriented high-resolution remote sensing Research on multi-scale image segmentation. The object-oriented multi-scale segmentation is oriented to The premise and the core of the image analysis method are as follows: how to describe the low-level features of the remote sensing image and the object-oriented multi-scale segmentation of the image in combination with the multi-feature is the present In this paper, the research focus of the object-oriented segmentation algorithm is discussed. The traditional image segmentation method is first analyzed. Then, on the basis of comparing the feature description of the image, the paper presents a method for describing the texture of the remote sensing image based on the time-frequency analysis, and based on the method, the paper presents a method to describe the texture of the remote sensing image. A method for measuring the texture heterogeneity of an object, using the region merging from the bottom up as the multi-scale segmentation strategy, presents a kind of surface combining the texture features. A multi-scale segmentation algorithm for remote sensing images of objects is presented. At last, from the edge of the image, a method for describing the edge strength of a remote sensing image and an edge combination cost criterion are presented, and the combined spectrum, shape, texture and edge are proposed. An object-oriented multi-scale segmentation algorithm for sign; (3) visual attention The method for extracting the object-oriented building area under the mechanism is as follows: the extraction of the building area can be used as the process of rough extraction in the extraction of the building, This paper first analyzes the texture performance of the building area in the high-resolution remote sensing image, and the multi-scale multi-direction texture description method based on the paper. A visual attention model based on information theory and a visual attention model based on information theory are used to simulate the perception environment of human vision system. Finally, based on the object-oriented image analysis method, the object-oriented multi-scale segmentation of the texture features of the building area is taken into account for the remote sensing image, and then the building area is used. the high-resolution remote sensing image is extracted by the saliency index. 4) The object-oriented high-resolution remote sensing image shadow extraction method is studied in this paper. The image information can be used as an auxiliary judgment basis for high-resolution remote sensing image building extraction. The paper analyzes the characteristics of the shadow in the high-resolution remote sensing image, and analyzes the brightness and the color tone of the shadow. and building a knowledge base of the shadow object by using the object-oriented image analysis method. line multi-scale segmentation, and finally, a discrimination criterion is formed according to the feature knowledge base to extract the cathode, Shadow. (5) The method of object-oriented building extraction under the mechanism of visual attention. In the high-resolution remote sensing image, the buildings often have the characteristics of high brightness, strong edge and texture. Combining the top-down prior knowledge and the lower-level feature of the bottom-up remote sensing image, a remote sensing system that takes into account the characteristics of buildings is proposed. image feature space transform method and its texture feature in combination with that present pap The method of edge characterization constitutes the main feature of the extraction frame of high-resolution remote sensing image. Then, by constructing the spectral characteristic index of the building, a building with spectral and texture features is proposed. Object-oriented multi-scale segmentation based on multi-feature combining of pre-processed high-resolution remote sensing images, and then a significant index of the building area of high-resolution remote sensing images (BASI) and finally, the building object is extracted in the multi-scale object set of the image,
【學位授予單位】：武漢大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TP751
本文編號：2423221
[Abstract]:With the development of the aeronautics and astronautics technology, the spatial resolution of the acquired remote sensing image data is increasing, and the acquisition difficulty is greatly reduced. With the increasing popularity of remote sensing data, the automatic extraction of the object of interest in the high-space-resolution remote sensing image by using the image processing and analysis technology has become one of the hot spots in the field of remote sensing. As the main artificial ground feature which is closely related to human life, the building is an important symbol of urban development. At present, the automatic extraction of the high-resolution remote sensing image has become an important means of building information acquisition, and the building information can be used to further study the expansion and development of the city, the current situation and the change of the urban land use, the urban planning, the urban heat island effect, Population estimates and projections, disaster monitoring and early warning and assessment. On the basis of the visual attention mechanism based on the information theory and the object-oriented image analysis method, the paper adopts the combination of the low-level feature extraction of the remote sensing image and the top-down prior knowledge guidance. In this paper, an object-oriented high-resolution remote sensing image building extraction under the visual attention mechanism is presented. The main research work of the paper includes the following Aspect: (1) The color consistency of remote sensing image The non-uniform illumination in remote sensing image is an image degradation phenomenon, which can increase to a certain extent. In order to eliminate the influence of the non-uniform illumination, the accuracy of the extraction of the remote sensing image is improved, and the color consistency of the remote sensing image In order to solve the problem of the color distortion in the remote sensing image, the paper presents a small HSV color space, which takes into account the color tone, which can cause the color distortion of the ground objects in the remote sensing image. The method comprises the following steps of: firstly, transforming an input image into an HSV color space, and then carrying out small wave decomposition on the luminance component and the saturation component of the image, In this paper, the image forming mechanism of remote sensing image under the cover of thin cloud is analyzed, and the luminance component and the saturation component of the thin cloud cover image are put forward. a uniform light algorithm for sexual processing. (2) object-oriented high-resolution remote sensing Research on multi-scale image segmentation. The object-oriented multi-scale segmentation is oriented to The premise and the core of the image analysis method are as follows: how to describe the low-level features of the remote sensing image and the object-oriented multi-scale segmentation of the image in combination with the multi-feature is the present In this paper, the research focus of the object-oriented segmentation algorithm is discussed. The traditional image segmentation method is first analyzed. Then, on the basis of comparing the feature description of the image, the paper presents a method for describing the texture of the remote sensing image based on the time-frequency analysis, and based on the method, the paper presents a method to describe the texture of the remote sensing image. A method for measuring the texture heterogeneity of an object, using the region merging from the bottom up as the multi-scale segmentation strategy, presents a kind of surface combining the texture features. A multi-scale segmentation algorithm for remote sensing images of objects is presented. At last, from the edge of the image, a method for describing the edge strength of a remote sensing image and an edge combination cost criterion are presented, and the combined spectrum, shape, texture and edge are proposed. An object-oriented multi-scale segmentation algorithm for sign; (3) visual attention The method for extracting the object-oriented building area under the mechanism is as follows: the extraction of the building area can be used as the process of rough extraction in the extraction of the building, This paper first analyzes the texture performance of the building area in the high-resolution remote sensing image, and the multi-scale multi-direction texture description method based on the paper. A visual attention model based on information theory and a visual attention model based on information theory are used to simulate the perception environment of human vision system. Finally, based on the object-oriented image analysis method, the object-oriented multi-scale segmentation of the texture features of the building area is taken into account for the remote sensing image, and then the building area is used. the high-resolution remote sensing image is extracted by the saliency index. 4) The object-oriented high-resolution remote sensing image shadow extraction method is studied in this paper. The image information can be used as an auxiliary judgment basis for high-resolution remote sensing image building extraction. The paper analyzes the characteristics of the shadow in the high-resolution remote sensing image, and analyzes the brightness and the color tone of the shadow. and building a knowledge base of the shadow object by using the object-oriented image analysis method. line multi-scale segmentation, and finally, a discrimination criterion is formed according to the feature knowledge base to extract the cathode, Shadow. (5) The method of object-oriented building extraction under the mechanism of visual attention. In the high-resolution remote sensing image, the buildings often have the characteristics of high brightness, strong edge and texture. Combining the top-down prior knowledge and the lower-level feature of the bottom-up remote sensing image, a remote sensing system that takes into account the characteristics of buildings is proposed. image feature space transform method and its texture feature in combination with that present pap The method of edge characterization constitutes the main feature of the extraction frame of high-resolution remote sensing image. Then, by constructing the spectral characteristic index of the building, a building with spectral and texture features is proposed. Object-oriented multi-scale segmentation based on multi-feature combining of pre-processed high-resolution remote sensing images, and then a significant index of the building area of high-resolution remote sensing images (BASI) and finally, the building object is extracted in the multi-scale object set of the image,
【學位授予單位】：武漢大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TP751
【引證文獻】
相關博士學位論文 前2條
1 康一飛;光學遙感衛(wèi)星影像云檢測方法及應用[D];武漢大學;2018年
2 李政;基于無人機高分影像的空心村建筑物信息獲取關鍵技術研究[D];西南交通大學;2018年
相關碩士學位論文 前3條
1 黨濤;基于多層次分割分類模型及其特征空間優(yōu)化的高分辨率遙感影像城市建筑物提取研究[D];蘭州大學;2018年
2 張靜敏;基于超級像素的居民地自動提取研究[D];東華理工大學;2017年
3 朱姝;面向對象的高分遙感影像建筑物提取方法研究[D];成都理工大學;2017年
，本文編號：2423221
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