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Information mining in remote sensing...

Li, Jiang.

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Information mining in remote sensing imagery.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Information mining in remote sensing imagery./
作者:	Li, Jiang.
面頁冊數:	151 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2321.
Contained By:	Dissertation Abstracts International64-05B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3092569

Information mining in remote sensing imagery.
Li, Jiang.

Information mining in remote sensing imagery. - 151 p.

Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2321.

Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2003.

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design.Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Information mining in remote sensing imagery.
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245 1 0 $a Information mining in remote sensing imagery.
300 $a 151 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-05, Section: B, page: 2321.
500 $a Adviser: Ram M. Narayanan.
502 $a Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2003.
520 $a The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design.
520 $a This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem.
520 $a Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized <italic> k</italic>-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A <italic>k</italic>-nearest neighbor search is performed using a query-by-example (QBE) approach.
520 $a Furthermore, an automatic parametric contour tracing algorithm and an <italic> O</italic>(<italic>n</italic>) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty.
520 $a Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.
590 $a School code: 0138.
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650 4 $a Computer Science. $3 626642
650 4 $a Remote Sensing. $3 1018559
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790 $a 0138
791 $a Ph.D.
792 $a 2003
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