High-resolution satellite images having a footprint smaller than 5 m are becoming increasingly available. A large number of the high resolution satellite sensors acquire imagery in a pushbroom mode with CCD linear array technology. The need for accurate 3D object reconstruction requires a sophisticated camera model, being able to deal with such sensor geometry. The software package SAT-PP (Sate...

The arrival of new-generation, high-spatial-resolution satellite imagery (e.g., Ikonos) has opened up new opportunities for detailed mapping and analysis of urban land use. Drawing on the traditional approach used in aerial photointerpretation, this study investigates an “object-oriented” method to classify a large urban area into detailed land-use categories. Spatial metrics and texture measur...

According to the fundamental goal of remote sensing technology, the image classification of desired sensors can be introduced as the most important part of satellite image interpretation. There exist various algorithms in relation to the supervised land use classification that the most pertinent one should be determined. Therefore, this study has been conducted to determine the best and most su...

Many conventional contrast enhancement techniques adopt a global approach to enhance the image. However, it is usually difficult to enhance all land cover classes appearing in the satellite image using these approaches, because local contrast information and details may be lost in the dark and bright areas. In this study, a three-stage algorithm based on fuzzy set theory is proposed to deal wit...

Cities are constantly evolving, complex systems; and modeling them, both theoretically and empirically, is a complicated task. In this paper, we develop a methodology to spatially model urban areas based on a grid system of data largely derived from satellite images. The work emphasizes spatial relationships between various geographic, land-use, and demographic variables characterizing fine zon...

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on graytone spatial dependancies, and illustrates their application in categoryidentification tasks of three different kinds of image...

This paper illustrates the use of Gabor wavelet transform on satellite images to classify the land in to crop land and noncrop land and to classify different crops. The input image is enhanced first using Colour Space Transform and Discrete Cosine Transform, and then a filter bank consisting of Gabor wavelets is used to extract texture features from the satellite image. The feature formation pr...

Satellite scene classification is challenging because of the high variability inherent in satellite data. Although rapid progress in remote sensing techniques has been witnessed in recent years, the resolution of the available satellite images remains limited compared with the general images acquired using a common camera. On the other hand, a satellite image usually has a greater number of spe...