Advances in Visual Computing

Volume 5359 of the series Lecture Notes in Computer Science pp 113-122

Satellite Image Segmentation Using Wavelet Transforms Based on Color and Texture Features

  • Ricardo Dutra da SilvaAffiliated withDepartment of Computer Science, Federal University of Paraná
  • , Rodrigo MinettoAffiliated withInstitute of Computing, University of Campinas
  • , William Robson SchwartzAffiliated withDepartment of Computer Science, University of Maryland
  • , Helio PedriniAffiliated withInstitute of Computing, University of Campinas


Image segmentation is a fundamental process in remote sensing applications, whose main purpose is to allow a meaningful discrimination among constituent regions of interest. This work presents a novel image segmentation method based on wavelet transforms for extracting a number of color and texture features from the images. Traditional feature extraction techniques based on individual pixels usually demand high computational cost. To reduce such computational cost, while achieving high-quality results, our approach is composed of two main stages. Initially, the image is decomposed into blocks of pixels and a wavelet transform is applied to each block to identify homogeneous regions of the image, assigning the entire block to a class. A refinement stage is applied to the remaining pixels which belong to blocks marked as heterogenous in the first stage. The developed method, tested on several remote sensing images and compared to a well known image segmentation method, presents high adaptability to image regions.