Accession Number : ADA133276

Title :   Segmentation of a High Resolution Urban Scene Using Texture Operators.

Descriptive Note : Technical rept.,

Corporate Author : LOUISIANA STATE UNIV BATON ROUGE REMOTE SENSING AND IMAGE PROCESSING LAB

Personal Author(s) : Conners,Richard W ; Trivedi,Mohan M ; Harlow,Charles A

PDF Url : ADA133276

Report Date : Sep 1982

Pagination or Media Count : 72

Abstract : This paper describes a study aimed at segmenting a high resolution black and white image of Sunnyvale, California. In this study regions were classified as belonging to any one of nine classes, residential, commercial/industrial, mobile home, water, dry land, runway/taxiway, aircraft parking, multilane highway, and vehicle parking. The classes were selected so that they directly relate to the Defense Mapping Agency's Mapping, Charting and Geodesy tanglible features. To attack the problem a statistical segmentation procedure was devised. The primitive operators used to drive the segmentation are texture measures derived from coocurrence matrices. The segmentation procedure considers three kinds of regions at each level of the segmentation, uniform, boundary and unspecified. At every level the procedure differentiates uniform regions from boundary and unspecified regions. It then assigns a class label to the uniform regions. The boundary and unspecified regions are split to form higher level regions. The methodologies involved are mathematically developed as a series of hypothesis tests. While only a one level segmentation was performed studies are described which show the capabilities of each of these hypothesis tests.

Descriptors :   *Image processing, *Segmented, *Mapping, *Hierarchies, *Trees, *Flow charting, *Statistics, Hypotheses, Arid land, Classification, Black(Color), Series(Mathematics), White(Color), Test methods, High resolution, Urban areas, Runways, Regions, Water, Images, Geodesy, Taxiways, Defense systems, California

Subject Categories : Geodesy
      Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE