A semi-automatic segmentation procedure for feature extraction in remotely sensed imagery

Authors:
Quanfa Zhang;Goran Pavlic;Wenjun Chen;Robert Fraser;Sylvain Leblanc;Josef Cihlar
Affiliations:
Applications Division, Canada Centre for Remote Sensing, Natural Resources Canada, 588 Booth Street, Ottawa, Ont., Canada K1A 0Y7 and Chinese Academy of Sciences, Wuhan Botanical Garden, Wuhan 430 ...;Applications Division, Canada Centre for Remote Sensing, Natural Resources Canada, 588 Booth Street, Ottawa, Ont., Canada K1A 0Y7;Applications Division, Canada Centre for Remote Sensing, Natural Resources Canada, 588 Booth Street, Ottawa, Ont., Canada K1A 0Y7;Applications Division, Canada Centre for Remote Sensing, Natural Resources Canada, 588 Booth Street, Ottawa, Ont., Canada K1A 0Y7;Applications Division, Canada Centre for Remote Sensing, Natural Resources Canada, 588 Booth Street, Ottawa, Ont., Canada K1A 0Y7;Applications Division, Canada Centre for Remote Sensing, Natural Resources Canada, 588 Booth Street, Ottawa, Ont., Canada K1A 0Y7
Venue:
Computers & Geosciences
Year:
2005

Citing 2
Cited 3

Three-dimensional computer vision: a geometric viewpoint

Three-dimensional computer vision: a geometric viewpoint
Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis

Computers & Geosciences

Multispectral image segmentation by a multichannel watershed-based approach

International Journal of Remote Sensing
Trends in vegetation NDVI from 1 km AVHRR data over Canada for the period 1985-2006

International Journal of Remote Sensing
Development of a machine learning technique for automatic analysis of seafloor image data: Case example, Pogonophora coverage at mud volcanoes

Computers & Geosciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a semi-automatic procedure that integrates thresholding, region growing, and edge detection techniques for feature extraction in remotely sensed imagery. An interface has been developed to provide an interactive platform of the procedure. Thresholding technique is employed to sample object of interest. Estimated properties (i.e., mean and variance) of the sample are applied for feature extraction using region growing. Since the derived object is subject to the sample and initial conditions, edge detection is incorporated to calibrate initial parameters by examining how the derived object matches the local edges inherent in the imagery. The program is loosely linked to PCI (PCI Geomatics, Richmond Hill, Ontario, Canada), a widely distributed image processing software. We demonstrate applications of this procedure by deriving burned scars using SPOT VGT and NOAA AVHRR imagery.