高分辨率遙感影像耕地多尺度提取
發(fā)布時間：2019-02-10 18:44
【摘要】：摘要：耕地是人類生存發(fā)展過程中不可或缺的寶貴資源,實時、動態(tài)、精確地掌握耕地信息是人民生活保障、國家進步發(fā)展的重要基礎。高分辨率遙感影像的出現(xiàn)為大范圍、精確、高效地提取耕地信息提供了可能,然而高分辨率遙感影像中耕地復雜多樣難以從某單一尺度精確提取,一直以來這都是遙感界的一個研究難題。因此,本文將在高分辨率遙感影像耕地多尺度分析上做了一點較為有實用意義的研究與探索�？傮w概括來說,本文開展如下幾個部分的研究工作： 首先,對分水嶺分割算法進行簡要的介紹,分析了該算法的優(yōu)劣性；對遙感中尺度的含義、內容、方法進行了闡述；對遙感影像的光譜、形狀、紋理特征的提取方法及其實用性進行分析。 其次,將物理學中場論方法引入到遙感影像分割中,提出一種多層次場聚類的分割方法。通過分水嶺分割得到地物對象,通過這些對象的分布關系構建聚類場,然后通過逐層聚類的方法進行合并,達到較優(yōu)的分割效果。在含有大片耕地的高分辨率遙感影像上進行實驗驗證,取得了較好的分割結果。 然后,丘陵地區(qū)地勢高低起伏、耕地形狀大小不一,傳統(tǒng)人工測量方法獲取耕地信息費時費力,而自動化的方法提取丘陵耕地的效果又不十分令人滿意的情況。針對這個難點,本文提出一種丘陵耕地多尺度提取方法。在利用各向異性多尺度構建方法生成多尺度梯度影像的基礎之上,通過分水嶺算法獲得不同尺度的耕地分割結果,之后利用GS評價準則從多尺度分割結果中選取最優(yōu)的耕地邊界識別結果。對比實驗表明本文方法在丘陵耕地的自動化提取中有一定的優(yōu)勢。 最后,城郊耕地是城市居民生活物資的保障,城市化進程的加快使其日益受到被侵占的威脅。高分辨率遙感影像中,城郊地區(qū)地物類別多樣、分布不均且光譜混雜,該區(qū)域耕地提取難度較大。為此,本文將建筑物區(qū)域進行剔除,在此基礎之上針對耕地占主導地位的地物進行多尺度分析得到較好的耕地提取結果。該算法主要有如下三個部分：利用改進的Harris角點特征對城郊建筑物進行提取,大致得到建筑區(qū)與非建筑區(qū)的劃分；通過多尺度方法對非建筑區(qū)耕地為主導的地物分析,得到耕地最佳分割結果；通過設定規(guī)則提取形狀規(guī)整的耕地與建筑區(qū)中非耕地,使用支持向量機進行分類得到耕地結果。
[Abstract]:Absrtact: cultivated land is an indispensable and precious resource in the process of human survival and development. It is an important foundation for people's living guarantee and national progress to grasp the information of cultivated land in real time, dynamic and accurate. The appearance of high-resolution remote sensing images makes it possible to extract cultivated land information accurately and efficiently. However, it is difficult to accurately extract cultivated land from a single scale in high-resolution remote sensing images. This has always been a research problem in remote sensing. Therefore, this paper will do a bit of practical research and exploration on the multi-scale analysis of cultivated land in high resolution remote sensing images. In general, the following parts of the research work are carried out in this paper: firstly, the watershed segmentation algorithm is introduced briefly, and the advantages and disadvantages of the algorithm are analyzed, the meaning, content and method of remote sensing mesoscale are expounded. The extraction method and practicability of spectral, shape and texture features of remote sensing image are analyzed. Secondly, the physical field theory method is introduced into remote sensing image segmentation, and a multi-level field clustering segmentation method is proposed. The ground objects are obtained by watershed segmentation, and the clustering field is constructed by the distribution of these objects, and then the clustering method is combined to achieve a better segmentation effect. The experimental results of high resolution remote sensing images with large area of cultivated land are verified and good segmentation results are obtained. Then, the terrain in hilly area is up and down, the shape of cultivated land is different, the traditional manual measurement method takes time and effort to obtain the information of cultivated land, and the effect of automatic method to extract cultivated land in hilly area is not very satisfactory. In view of this difficulty, this paper presents a multi-scale extraction method for hilly cultivated land. On the basis of using anisotropic multi-scale construction method to generate multi-scale gradient image, the watershed algorithm is used to obtain the results of farmland segmentation with different scales. Then the optimal cultivated land boundary recognition results are selected from the multi-scale segmentation results by using the GS evaluation criterion. The comparative experiments show that this method has some advantages in automatic extraction of hilly farmland. Finally, suburban cultivated land is the guarantee of living materials for urban residents, and the acceleration of urbanization makes it increasingly threatened by encroachment. In the high-resolution remote sensing image, there are many kinds of ground objects in the suburban area, which are unevenly distributed and the spectrum is mixed, so it is difficult to extract cultivated land in this region. For this reason, this paper culls the building area, and on the basis of this, carries on the multi-scale analysis to the cultivated land which occupies the dominant position, obtains the better cultivated land extraction result. The algorithm consists of the following three parts: the improved Harris corner feature is used to extract the suburban buildings, and the division between the building area and the non-building area is obtained roughly; Through multi-scale analysis of cultivated land in non-construction areas, the optimal segmentation results of cultivated land are obtained, and the cultivated land results are obtained by using support vector machine (SVM) to extract cultivated land with regular shape and non-cultivated land in construction area.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751
本文編號：2419405
[Abstract]:Absrtact: cultivated land is an indispensable and precious resource in the process of human survival and development. It is an important foundation for people's living guarantee and national progress to grasp the information of cultivated land in real time, dynamic and accurate. The appearance of high-resolution remote sensing images makes it possible to extract cultivated land information accurately and efficiently. However, it is difficult to accurately extract cultivated land from a single scale in high-resolution remote sensing images. This has always been a research problem in remote sensing. Therefore, this paper will do a bit of practical research and exploration on the multi-scale analysis of cultivated land in high resolution remote sensing images. In general, the following parts of the research work are carried out in this paper: firstly, the watershed segmentation algorithm is introduced briefly, and the advantages and disadvantages of the algorithm are analyzed, the meaning, content and method of remote sensing mesoscale are expounded. The extraction method and practicability of spectral, shape and texture features of remote sensing image are analyzed. Secondly, the physical field theory method is introduced into remote sensing image segmentation, and a multi-level field clustering segmentation method is proposed. The ground objects are obtained by watershed segmentation, and the clustering field is constructed by the distribution of these objects, and then the clustering method is combined to achieve a better segmentation effect. The experimental results of high resolution remote sensing images with large area of cultivated land are verified and good segmentation results are obtained. Then, the terrain in hilly area is up and down, the shape of cultivated land is different, the traditional manual measurement method takes time and effort to obtain the information of cultivated land, and the effect of automatic method to extract cultivated land in hilly area is not very satisfactory. In view of this difficulty, this paper presents a multi-scale extraction method for hilly cultivated land. On the basis of using anisotropic multi-scale construction method to generate multi-scale gradient image, the watershed algorithm is used to obtain the results of farmland segmentation with different scales. Then the optimal cultivated land boundary recognition results are selected from the multi-scale segmentation results by using the GS evaluation criterion. The comparative experiments show that this method has some advantages in automatic extraction of hilly farmland. Finally, suburban cultivated land is the guarantee of living materials for urban residents, and the acceleration of urbanization makes it increasingly threatened by encroachment. In the high-resolution remote sensing image, there are many kinds of ground objects in the suburban area, which are unevenly distributed and the spectrum is mixed, so it is difficult to extract cultivated land in this region. For this reason, this paper culls the building area, and on the basis of this, carries on the multi-scale analysis to the cultivated land which occupies the dominant position, obtains the better cultivated land extraction result. The algorithm consists of the following three parts: the improved Harris corner feature is used to extract the suburban buildings, and the division between the building area and the non-building area is obtained roughly; Through multi-scale analysis of cultivated land in non-construction areas, the optimal segmentation results of cultivated land are obtained, and the cultivated land results are obtained by using support vector machine (SVM) to extract cultivated land with regular shape and non-cultivated land in construction area.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751
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