Title

Gaussian Processes for Object Detection in High Resolution Remote Sensing Images

Description

Object detection in high resolution remote sensing images is a crucial yet challenging problem for many applications. With the development of satellite and sensor technologies, remote sensing images attain very high spatial resolution, giving rise to the employment of many computer vision algorithms. Therefore, the object detection is usually formalized as a supervised classification task. In this paper, we propose to apply the Gaussian process (GP) classification algorithm for our detection problem. Among different classifiers, the GP classifier is a Bayesian classification method that is able to make estimations in a probabilistic way. To demonstrate the performance of the proposed approach, we experiment the proposed framework with different feature extraction schemes and classification methods. We carry out a cross-validation experiment over an image dataset that consists of objects and non-objects to train an object detector, and apply the trained detector in an unobserved image scene to search for the objects of interest. Our results show that the GP classifier is competitive to support vector machines (SVM), which is considered stateof-the-art.

Date of creation, presentation, or exhibit

11-30-2016

Comments

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Document Type

Conference Proceeding

Department, Program, or Center

Electrical Engineering (KGCOE)

Campus

RIT – Main Campus

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