In recent years, the images captured by AVHRR (Advanced Very High Resolution Radiometer) on the NOAA (National Oceanic and Atmospheric Administration) series of satellites have been used very widely for environment and land cover monitoring. In order to use NOAA images, they need to be accurately transformed from the image coordinate system into map coordinate system. This paper proposes a geometric correction method that corrects the errors caused by this transformation. In this method, the errors in NOAA image are corrected in the image coordinate system before transforming into the map coordinate system. First, the elevation values, which are read from GTOPO30 database, are verified to divide data into flat and rough blocks. Next, in order to increase the number of GCPs (Ground Control Points), besides the GCPs in the database, more GCPs are generated based on the feature of the coastline. After using reference images to correct the missing lines and noise pixels in the top and bottom parts of the image, the elevation errors of the GCP templates are corrected and GCP template matching is applied to find the residual errors for the blocks that match GCP templates. Based on these blocks, the residual errors of other flat and rough blocks are calculated by affine and Radial Basis Function transform respectively. According to the residual errors, all pixels in the image are moved to their correct positions. Finally, data is transformed from image into map by bilinear interpolation. With the proposed method, the average values of the error after correction are smaller than 0.2 pixels on both latitude and longitude directions. This result proved that the proposed method is a highly accurate geometric correction method.

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.

We have developed a map-based approach that enables us to efficiently extract information about man-made objects, such as buildings, from aerial images. An image is matched with a corresponding map in order to estimate the object information in the image (i. e. , presence, location, shape, size, kind, and surroundings). This approach is characterized by using a figure contained in a map as an object model for a top-down (model-driven) analysis of an object in the aerial image. We determined the principal steps of the map-based approach needed to extract object information and update a map. These steps were then applied to obtain the locations of missing buildings and the heights of existing buildings. The extraction results of experiments using aerial images of Kobe City (taken after the 1995 earthquake) show that the approach is effective for automatically extracting building information from aerial images and for rapidly updating map data.

Since its successful launch in February of 1992, the Japan Earth Resources Satellite-1 (JERS-1) has been sending back high resolution images of the earth for various studies, including the investigation of earth resources, the preservation of environments and the observation of coastal lines. Currently, received images are processed using the Earth Resources Satellite Data Information System (ERSDIS). The ERSDIS is a high speed image processing system utilizing an extended cellular array processor as its main processing module. The extended cellular array processor (CAP), consisting of 4096 processing elements configured into a two-dimensional array, is designed to have many parallel processing optimizing capabilities targetting large-scale image processing at a high speed. This paper desctribes a typical image processing flow, the structure of the ERSDIS, and the details of the CAP design.