Road extraction plays an important role in many applications such as traffic monitoring. In order to speed the extraction
and enhance its precision, an approach based on particle filtering is proposed in this paper. Firstly, an improved line
detector is presented to extract road candidates, which makes use of the road characteristic on SAR images. Then particle
filtering based on Monte Carlo theory is applied to group the candidates. Applied results show that the road extraction
method is effective and the road features on SAR images have been extracted accurately. Moreover, the method can be
realized simply and save the amount of calculation.
Tobacco enterprise is a special enterprise, which has strong correlation to regional geography. But in the past research
and application, the combination between tobacco and GIS is limited to use digital maps to assist cigarette distribution.
How to comprehensively import 3S technique and spatial data mining (SDM) to construct spatial decision support
system (SDSS) of tobacco enterprise is the main research aspect in this paper. The paper concretely analyzes the GIS
requirements in tobacco enterprise for planning location of production, monitoring production management and product
sale at the beginning. Then holistic solution is presented and frame design for tobacco enterprise spatial decision based
on SDM is given. This paper describes how to use spatial analysis and data mining to realize the spatial decision
processing such as monitoring tobacco planted acreage, analyzing and planning the cigarette sale network and so on.
Linear features are usually extracted from SAR imagery by a few edge detectors derived from the contrast ratio edge
detector with a constant probability of false alarm. On the other hand, the Hough Transform is an elegant way of
extracting global features like curve segments from binary edge images. Randomized Hough Transform can reduce the
computation time and memory usage of the HT drastically. While Randomized Hough Transform will bring about a
great deal of cells invalid during the randomized sample. In this paper, we propose a new approach to extract linear
features on SAR imagery, which is an almost automatic algorithm based on edge detection and Randomized Hough
Transform. The presented improved method makes full use of the directional information of each edge candidate points
so as to solve invalid cumulate problems. Applied result is in good agreement with the theoretical study, and the main
linear features on SAR imagery have been extracted automatically. The method saves storage space and computational
time, which shows its effectiveness and applicability.