本文選題：局部方差方法 + 變異函數(shù)方法��； 參考：《中國(guó)科學(xué)院研究生院（東北地理與農(nóng)業(yè)生態(tài)研究所）》2014年博士論文

【摘要】：空間格局是陸地生態(tài)系統(tǒng)的一個(gè)重要幾何特征，它控制著地表的生態(tài)過(guò)程和功能，所以，空間格局分析被廣泛應(yīng)用于地學(xué)領(lǐng)域的各相關(guān)研究中。作為對(duì)地觀測(cè)的重要工具，遙感技術(shù)提供了大量的影像數(shù)據(jù)；在遙感影像的各種應(yīng)用中，遙感影像的空間格局識(shí)別是其重要任務(wù)之一；特別是高空間分辨率遙感影像的出現(xiàn)，因其可提供更細(xì)致的空間數(shù)據(jù)，極大地提升了空間格局信息的提取水平。許多方法被開(kāi)發(fā)或者引入，以用于遙感影像空間格局的探測(cè)分析，其中變異函數(shù)方法和局部方差方法是兩種最常用的方法。這兩種方法是通過(guò)直接建立地物尺寸大小與影像數(shù)據(jù)空間分辨率之間的關(guān)系，以達(dá)到探測(cè)遙感影像中地物空間格局信息。 空間格局探測(cè)機(jī)理研究有助于加深對(duì)各種探測(cè)方法的實(shí)際應(yīng)用能力的理解；然而，有關(guān)變異函數(shù)和局部方差方法兩種空間格局探測(cè)方法的機(jī)理研究比較少；并且在這兩種方法機(jī)理研究中，對(duì)于地物空間格局類型與相關(guān)參數(shù)的選擇都比較有局限性。針對(duì)上述問(wèn)題，本文在模擬具有不同格局類型、簡(jiǎn)單離散規(guī)則分布的一維和二維影像系列基礎(chǔ)上，通過(guò)不斷改變兩種方法的相關(guān)參數(shù)，以研究其探測(cè)空間格局的能力；然后，再通過(guò)模擬不同類型及形狀的離散隨機(jī)分布的一維和二維影像，以研究?jī)煞N方法探測(cè)隨機(jī)影像空間格局的能力；最后，通過(guò)不同類型的真實(shí)遙感影像，進(jìn)一步研究?jī)煞N方法探測(cè)實(shí)際遙感影像空間格局的能力。通過(guò)對(duì)變異函數(shù)方法和局部方差方法探測(cè)機(jī)理和探測(cè)能力的系統(tǒng)和深入研究，得到以下幾個(gè)方面的研究結(jié)論： （1）地物大小探測(cè) 對(duì)于局部方差方法，在探測(cè)規(guī)則格局地物大小時(shí)，地物大小與局部方差曲線峰值點(diǎn)位置不存在一一對(duì)應(yīng)關(guān)系；ALV曲線峰值點(diǎn)位置和峰值點(diǎn)個(gè)數(shù)受到地物變化周期類型的影響。在探測(cè)隨機(jī)格局與真實(shí)影像地物大小時(shí)，通過(guò)局部方差曲線的峰值點(diǎn)位置難以判斷地物具體大小，也不能給出地物大小的可能范圍。 對(duì)于變異函數(shù)方法，在探測(cè)規(guī)則格局地物大小時(shí)，使用變異函數(shù)曲線的拐點(diǎn)能夠探測(cè)出規(guī)則影像中的地物與背景大小，并且探測(cè)結(jié)果不受地物變化周期類型的影響；但是，變異函數(shù)曲線的拐點(diǎn)既可能代表地物大小也可能表示背景大小，需要引入影像均值大小來(lái)準(zhǔn)確判斷。在探測(cè)隨機(jī)格局與真實(shí)影像地物大小時(shí)，通過(guò)變異函數(shù)曲線的拐點(diǎn)難以判斷出地物大小。 （2）地物周期探測(cè) 對(duì)于局部方差方法，在探測(cè)規(guī)則格局地物周期時(shí)，通過(guò)局部方差曲線“關(guān)鍵谷值點(diǎn)”位置能夠準(zhǔn)確探測(cè)出影像中規(guī)則地物變化周期大小。 對(duì)于變異函數(shù)方法，在探測(cè)規(guī)則格局地物周期時(shí)，通過(guò)變異函數(shù)曲線的“谷值點(diǎn)”位置能夠準(zhǔn)確探測(cè)出影像中規(guī)則地物變化周期大小。 (3)規(guī)則格局地物大小準(zhǔn)確探測(cè) 對(duì)于規(guī)則空間格局的遙感影像，在利用局部方差曲線“關(guān)鍵谷值點(diǎn)”位置探測(cè)地物變化周期的基礎(chǔ)上，提出了改進(jìn)的局部方差統(tǒng)計(jì)指數(shù)模型方法，該方法可以準(zhǔn)確探測(cè)出遙感影像規(guī)則空間格局中的地物大小信息，并克服了傳統(tǒng)局部方差方法難以探測(cè)遙感影像地物大小的局限。 （4）窗口大小影響 對(duì)于局部方差方法，在探測(cè)規(guī)則地物格局時(shí)，計(jì)算窗口大小對(duì)局部方差曲線峰值點(diǎn)的位置和個(gè)數(shù)有顯著影響，不論地物變化周期大小為何種類型；但是，，計(jì)算窗口大小對(duì)局部方差曲線“關(guān)鍵谷值點(diǎn)”的位置沒(méi)有影響。在探測(cè)隨機(jī)地物格局和真實(shí)影像格局時(shí)，隨著計(jì)算窗口的不斷變大，局部方差曲線峰值點(diǎn)的位置不斷向左移動(dòng)。 對(duì)于變異函數(shù)方法，在探測(cè)規(guī)則地物格局時(shí)，計(jì)算窗口大小對(duì)變異函數(shù)曲線拐點(diǎn)的位置和個(gè)數(shù)也有顯著影響，隨著計(jì)算窗口的不斷變大，變異函數(shù)曲線從有拐點(diǎn)變化到無(wú)拐點(diǎn)。在探測(cè)隨機(jī)地物格局和真實(shí)影像格局時(shí)，隨著計(jì)算窗口的不斷變大，變異函數(shù)曲線的拐點(diǎn)位置不斷向右移動(dòng)。 （5）影像幅寬影響 對(duì)于局部方差方法，在探測(cè)規(guī)則地物格局時(shí)，影像幅寬最少要為2×W×P（W為計(jì)算窗口大小，P為地物變化周期大�。﹤�(gè)像素時(shí)，局部方差曲線的峰值點(diǎn)和“關(guān)鍵谷值點(diǎn)”才能同時(shí)出現(xiàn)。 對(duì)于變異函數(shù)方法，利用滯后距離間隔為1個(gè)像素大小探測(cè)規(guī)則地物格局時(shí)，影像幅寬最少要為地物變化周期大小的兩倍時(shí)，才能準(zhǔn)確探測(cè)出規(guī)則格局影像的地物、背景以及地物變化周期大小。 對(duì)于隨機(jī)地物影像和真實(shí)影像，影像中地物格局與幅寬有依賴性，不同幅寬包含的地物格局尺度大小不同；影像幅寬對(duì)局部方差曲線的峰值點(diǎn)位置和變異函數(shù)曲線拐點(diǎn)位置有明顯影響。
[Abstract]:Spatial pattern is an important geometric feature of terrestrial ecosystems , which controls the ecological processes and functions of the surface . Therefore , spatial pattern analysis is widely used in all relevant researches in the field of geoscience . As an important tool for earth observation , remote sensing technology provides a lot of image data ;
In the application of remote sensing images , the recognition of spatial pattern of remote sensing image is one of its important tasks .
In particular , the appearance of high spatial resolution remote sensing images can provide more detailed spatial data and greatly improve the extraction level of spatial pattern information . Many methods have been developed or introduced for the detection and analysis of spatial pattern of remote sensing images .

The research on the mechanism of spatial pattern detection can help to deepen the understanding of the practical application ability of various detection methods ;
However , the mechanism of two spatial pattern detection methods related to the variation function and the local variance method is less .
In the research of the mechanism of the two methods , there are limitations on the selection of spatial pattern types and relevant parameters . In this paper , based on a series of two - dimensional image series with different pattern types and simple discrete rule distributions , the relative parameters of the two methods are constantly changed to study their ability to detect spatial pattern .
Then , a two - dimensional image of discrete random distribution of different types and shapes is simulated , and the ability of two methods to detect random image spatial pattern is studied .
Finally , through different types of real remote sensing images , we further study the ability of two methods to detect the spatial pattern of real remote sensing images .

( 1 ) Ground object size detection

For the local variance method , there is no one - to - one correspondence between the size of the figure and the peak point of the local variance curve when the rule pattern is detected .
The peak point position of ALV curve and the number of peak points are affected by the type of the change period of the figure . In detecting the random pattern and the real image figure , it is difficult to judge the specific size of the figure by the peak point position of the local variance curve , and the possible range of the figure size cannot be given .

For the variation function method , the figure and background size in the regular image can be detected by using the inflection point of the variation function curve when the rule pattern is detected , and the detection result is not influenced by the change period type of the figure ;
However , the inflection point of the variation function curve may represent both the size of the figure and the background size , and it is necessary to introduce the image mean size to accurately judge . In detecting the random pattern and the real image figure , it is difficult to judge the size of the figure through the inflection point of the variation function curve .

( 2 ) Ground object periodic detection

For the local variance method , the regular pattern change cycle size in the image can be accurately detected by the " key valley point " position of the local variance curve when the regular pattern of the regular pattern is detected .

For the variation function method , the regular figure change cycle size in the image can be accurately detected by the " valley point " position of the variation function curve when the pattern of the regular pattern is detected .

( 3 ) Accurate detection of regular pattern figure

For the remote sensing image of regular spatial pattern , the improved local variance statistical index model method is proposed based on the use of the " key valley point " position of the local variance curve . The method can accurately detect the figure size information in the spatial pattern of remote sensing image rules , and overcome the limitation of the traditional local variance method to detect the size of remote sensing images .

( 4 ) Window size effect

For the local variance method , the location and number of the peak point of the local variance curve are significantly influenced by the window size when the rule of the rule is detected , regardless of the type of the change period of the figure .
however , that compute window size has no effect on the position of the " critical valley point " of the local variance curve . when the random figure pattern and the real image pattern are detected , the position of the peak point of the local variance curve is continuously shifted to the left as the compute window becomes larger .

For the variation function method , the position and the number of the inflection point of the variation function curve are also significantly influenced by the window size when the pattern of the rule is detected . As the calculation window becomes larger , the variation function curve changes from the inflection point to the inflection point . When the random figure pattern and the real image pattern are detected , the inflection point position of the variation function curve is continuously shifted to the right as the calculation window becomes larger .

( 5 ) Impact of image width

For the local variance method , the peak point of the local variance curve and the " key valley point " can occur at the same time when the image width is at least 2 脳 W 脳 P ( W is the calculated window size , P is the change period of the figure ) .

For the variation function method , when a rule floor pattern is detected with a lag distance interval of 1 pixel size , the area of the regular pattern image can be accurately detected when the width of the image is at least twice the size of the change period of the figure , so that the size of the figure of the regular pattern image , the background and the change period of the figure can be accurately detected .

For random figure images and real images , the pattern of the figure in the image is dependent on the breadth and the size of the figure is different .
The image breadth has an obvious influence on the peak point position and the inflection point position of the variation function curve of the local variance curve .
【學(xué)位授予單位】：中國(guó)科學(xué)院研究生院（東北地理與農(nóng)業(yè)生態(tài)研究所）
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：P237

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李雙成,蔡運(yùn)龍;地理尺度轉(zhuǎn)換若干問(wèn)題的初步探討[J];地理研究;2005年01期

2 王宇慶;劉維亞;王勇;;一種基于局部方差和結(jié)構(gòu)相似度的圖像質(zhì)量評(píng)價(jià)方法[J];光電子.激光;2008年11期

3 強(qiáng)贊霞,彭嘉雄,王洪群;基于小波變換局部方差的遙感圖像融合[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2003年06期

4 肖篤寧;李小玉;宋冬梅;楊國(guó)靖;;民勤綠洲地下水開(kāi)采時(shí)空動(dòng)態(tài)模擬[J];中國(guó)科學(xué).D輯:地球科學(xué);2006年06期

5 曾基兵;陳懷新;王衛(wèi)星;;基于改進(jìn)局部方差的小波圖像融合方法[J];計(jì)算機(jī)工程與應(yīng)用;2007年32期

6 詹翔;周焰;;一種基于局部方差的霧天圖像增強(qiáng)方法[J];計(jì)算機(jī)應(yīng)用;2007年02期

7 李鐘山;地質(zhì)統(tǒng)計(jì)學(xué)中的區(qū)域化變量理論[J];世界地質(zhì);1997年02期

8 姜秋香;付強(qiáng);王子龍;;空間變異理論在土壤特性分析中的應(yīng)用研究進(jìn)展[J];水土保持研究;2008年01期

9 呂一河,傅伯杰;生態(tài)學(xué)中的尺度及尺度轉(zhuǎn)換方法[J];生態(tài)學(xué)報(bào);2001年12期

10 楊奇勇;楊勁松;李曉明;;不同閾值下土壤鹽分的空間變異特征研究[J];土壤學(xué)報(bào);2011年06期

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