In this letter, a semi-automatic method for road network extraction from high-resolution satellite images is proposed. First, we focus on detecting the seed points in candidate road regions using a method of self-organizing map (SOM). Then, an approach to road tracking is presented, searching for connected points in the direction and candidate domain of a road. A study of Geographical Information Systems (GIS) using high-resolution satellite images is presented in this letter. Experimental results verified the effectiveness and efficiency of this approach.

In this article, a hierarchical classifier is proposed for classification of ground-cover types of a satellite image of Kangaroo Island, South Australia. The image contains seven ground-cover types, which are categorized into three groups using principal component analysis. The first group contains clouds only, the second consists of sea and cloud shadow over land, and the third contains land and three types of forest. The sea and shadow over land classes are classified with 99% accuracy using a network of threshold logic units. The land and forest classes are classified by multilayer perceptrons (MLPs) using texture features and intensity values. The average performance achieved by six trained MLPs is 91%. In order to improve the classification accuracy even further, the outputs of the six MLPs were combined using several committee machines. All committee machines achieved significant improvement in performance over the multilayer perceptron classifiers, with the best machine achieving over 92% correct classification.

An improved algorithm is developed for correcting the topographic impact on satellite imagery. First, we analyze the topography induced distortion on satellite image. It is shown that the variation of aspect can cause the obvious different distortions in the remotely sensed image, and also effect the image illumination significantly. Because the illumination is the basis for topographic correction algorithms, we consider its variation in different sun-facing aspects in calculation a correction parameter and take it as a key element in the modified correction algorithm. Then, we apply the modified correction method on the actual Landsat Thematic Mapper satellite image. The topographic correction was done in different image data with different season and different solar angle. The corrected results show the effectiveness and accuracy using this approach.