Overhead image statistics

• Published in: 2008 37th IEEE Applied Imagery Pattern Recognition Workshop pp. 1 - 8
• Main Authors: Vijayaraj, V., Cheriyadat, A.M., Sallee, P., Colder, B., Vatsavai, R.R., Bright, E.A., Bhaduri, B.L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2008
• Subjects: Computer vision; Image analysis; Image databases; Layout; Performance analysis; Predictive models; Shape; Statistical distributions; Statistics; Wavelet coefficients
• ISBN: 1424431255; 9781424431250
• ISSN: 1550-5219; 2332-5615
• DOI: 10.1109/AIPR.2008.4906471

Statistical properties of high-resolution overhead images representing different land use categories are analyzed using various local and global statistical image properties based on the shape of the power spectrum, image gradient distributions, edge co-occurrence, and inter-scale wavelet coefficient distributions. The analysis was performed on a database of high-resolution (1 meter) overhead images representing a multitude of different downtown, suburban, commercial, agricultural and wooded exemplars. Various statistical properties relating to these image categories and their relationship are discussed. The categorical variations in power spectrum contour shapes, the unique gradient distribution characteristics of wooded categories, the similarity in edge co-occurrence statistics for overhead and natural images, and the unique edge co-occurrence statistics of downtown categories are presented in this work. Though previous work on natural image statistics has showed some of the unique characteristics for different categories, the relationships for overhead images are not well understood. The statistical properties of natural images were used in previous studies to develop prior image models, to predict and index objects in a scene and to improve computer vision models. The results from our research findings can be used to augment and adapt computer vision algorithms that rely on prior image statistics to process overhead images, calibrate the performance of overhead image analysis algorithms, and derive features for better discrimination of overhead image categories.