Research on the application of relaxation technology to the extraction of linear feature from satellite image
Cui Min-jun Fang, You-ching
Nanjing Forestry University
kou Wen-zheng
Institute of Forestry Inventory, Planning & Designing,
Ministry of Forestry
China
Abstract
Based on the non-linear relax model in the relaxation theory, this article deals with the enhancement of linear features in use of contextual information. I order to extract continuous line curve with the width of one pixel, from the remote sensing satellite image, it makes it possible to extract the visible linear feature from the image and to reproduce the discontinuous linear objects covered by closed forests and terrain shadows.
Introduction
To import linear features for geographical information system by means of extracting linear feature from satellite image, in order to display highways, railways, forest roads, bridges, brooks, trees arranged in rows, the linear ring geological structure, faults and cracks etc not only can avoid large quantity of digitising and most part of field work, but also quicken the production of new map.
In recent years, in the field of remote sensing image processing, most of the research of extraction of linear feature focus on the design, research and application of local detector, that is, to use the difference of spectrum between certain pixel and its vicinity. This kind of detection can only be applied in small fields. Besides, the image processed by this method contains much noise and becomes mote complicated, les continuous for linear feature. The limitation of l0ocal detector will be no doubt lead to wrong interpretation on a larger scale.
The information of remote sensing image is involved in the change of spectrum and also in space change of energy. Whether certain point lies in line, it not only on the degree of spectrum difference between itself and its neighbour pixel, but also relates to their position, background and the information of vicinity. The main form of spatial informations in digital pattern recognition are contextual information and neighbourhood information.
If the context is regarded as a spatial change consisting of a group of pixels connected each other in the scene. Thus the field of any pixel can be related to other pixel or its group of the whole scene which means that the usage of neighbour hood structure information not only can
reduce the fallibility in linear feature extraction, but also make it possible to extract the different linear feature individually which have the same spectrum characteristics according to their neighbourhood structures.
One of the limitation the contextual analysis is that the extracted linear feature will also unavoidably produce noise and non-continuous features, because the contextual measure needs to be restore the continuity of the learn features the non-continuous lines have to be contected on basis of direction information and more other spatial knowledge. This article is based on on-linear detector to extracted linear features by means of probabilistic relaxation theory in order to get linear feature image with less noise. Of course, the extracted feature still has discontinuously line which demands further on the method of thinning and linking, so as to realize the extraction with choice of difference linear features in accordance with length.
To apply relaxation technology tot eh extraction of linear features takes great advantage of attributes of neighborhood in the object attribute judgment. That is, to adjust continuously the reliable degree of sorts of attributes and when the adjustment become stable, the results is used to judge the object attribute more rationally.
