Low and mid-level features for target detection in satellite images
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 2
Abstract
Reliably detecting objects in broadarea overhead or satellite images has become an increasingly pressing need, as the capabilities for image acquisition are growing rapidly. The problem is particularly difficult in the presence of large intraclass variability. An automatic approach is used to detect and classify targets in high-resolution broad-area satellite images, which relies on detecting statistical signatures of targets, in terms of a set of biologically-inspired low-level visual features. Biologically-inspired feature extraction methods use the “gestalt” information (continuity, symmetry, closure, repetition) to conduct object detection. Gestalt psychology studies how the human visual system organizes the complex visual input into unitary elements. The goal of the visual system, computer or biological, is to transform a visual input into meaningful semantic information. A new methodology to learn relations inferred from Gestalt principles and an application to segment unknown objects, even if objects are stacked or jumbled and tackle also the problem of segmenting partially occluded objects. The relevance of the relations for object segmentation is learned with support vector machines (SVMs). Multispectral imaging is significant technology for the acquisition and display of accurate color information. This study shows that the proposed target search method can reliably and effectively detect highly variable target objects in large image datasets.
Authors and Affiliations
Rajani. D. C
Keywords
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