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首頁> 外文學(xué)位 >Cairn detection in southern Arabia using a supervised automatic detection algorithm and multiple sample data spectroscopic clustering.
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Cairn detection in southern Arabia using a supervised automatic detection algorithm and multiple sample data spectroscopic clustering.

機(jī)譯：使用監(jiān)督自動檢測算法和多樣本數(shù)據(jù)光譜聚類在阿拉伯南部的凱恩河探測。

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摘要

Excavating cairns in southern Arabia is a way for anthropologists to understand which factors led ancient settlers to transition from a pastoral lifestyle and tribal narrative to the formation of states that exist today. Locating these monuments has traditionally been done in the field, relying on eyewitness reports and costly searches through the arid landscape.;In this thesis, an algorithm for automatically detecting cairns in satellite imagery is presented. The algorithm uses a set of filters in a window based approach to eliminate background pixels and other objects that do not look like cairns. The resulting set of detected objects constitutes fewer than 0.001% of the pixels in the satellite image, and contains the objects that look the most like cairns in imagery. When a training set of cairns is available, a further reduction of this set of objects can take place, along with a likelihood-based ranking system.;To aid in cairn detection, the satellite image is also clustered to determine land-form classes that tend to be consistent with the presence of cairns. Due to the large number of pixels in the image, a subsample spectral clustering algorithm called "Multiple Sample Data Spectroscopic clustering" is used. This multiple sample clustering procedure is motivated by perturbation studies on single sample spectral algorithms. The studies, presented in this thesis, show that sampling variability in the single sample approach can cause an unsatisfactory level of instability in clustering results. The multiple sample data spectroscopic clustering algorithm is intended to stabilize this perturbation by combining information from different samples. While sampling variability is still present, the use of multiple samples mitigates its effect on cluster results.;Finally, a step-through of the cairn detection algorithm and satellite image clustering are given for an image in the Hadramawt region of Yemen. The top ranked detected objects are presented, and a discussion of parameter selection and future work follows.
機(jī)譯：在阿拉伯南部挖掘凱恩斯是人類學(xué)家了解導(dǎo)致古代定居者從牧民生活方式和部落敘事過渡到今天形成的國家的因素的一種方式。傳統(tǒng)上，這些遺跡的定位是在野外進(jìn)行的，這要依靠目擊者的報(bào)告并在干旱的土地上進(jìn)行昂貴的搜索。；本文提出了一種在衛(wèi)星圖像中自動檢測石棺的算法。該算法在基于窗口的方法中使用了一組過濾器，以消除背景像素和其他看起來不像凱恩斯的物體。所得的一組檢測到的對象構(gòu)成了衛(wèi)星圖像中少于0.001％的像素，并且包含看起來最像圖像中的凱恩斯的對象。當(dāng)有一組凱恩斯訓(xùn)練集可用時(shí)，可以進(jìn)一步減少這組對象以及基于似然度的排名系統(tǒng)。為了幫助進(jìn)行凱恩斯檢測，還對衛(wèi)星圖像進(jìn)行聚類以確定地形類型，往往與凱恩斯的存在是一致的。由于圖像中的像素?cái)?shù)量很大，因此使用了稱為“多樣本數(shù)據(jù)光譜聚類”的子樣本光譜聚類算法。這種多樣本聚類過程是由對單個樣本頻譜算法的擾動研究推動的。本文提出的研究表明，單樣本方法中的樣本變異性可能會導(dǎo)致聚類結(jié)果的不穩(wěn)定性達(dá)到令人滿意的水平。多樣本數(shù)據(jù)光譜聚類算法旨在通過組合來自不同樣本的信息來穩(wěn)定這種干擾。雖然仍然存在采樣變異性，但使用多個樣本會減輕其對聚類結(jié)果的影響。最后，針對也門哈德拉毛特地區(qū)的圖像，給出了石標(biāo)檢測算法和衛(wèi)星圖像聚類的逐步介紹。給出了排名最高的檢測到的對象，然后討論了參數(shù)選擇和將來的工作。

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  • 作者

    Schuetter, Jared Michael.;

  • 作者單位

    The Ohio State University.;

  • 授予單位 The Ohio State University.;
  • 學(xué)科 Statistics.;Remote Sensing.
  • 學(xué)位 Ph.D.
  • 年度 2010
  • 頁碼 236 p.
  • 總頁數(shù) 236
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關(guān)鍵詞

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