Vehicle tracking with multi-temporal hyperspectral imagery

Abstract

Hyperspectral imagery has the capability of capturing spectral features of interest that can be used to differentiate among similar materials. While hyperspectral imaging has been demonstrated to provide data that enable classification of relatively broad categories, there remain open questions as to how fine of discrimination is possible. An application of this fine discrimination question is the potential that spectral features exist in the surface reflectance of ordinary civilian vehicles that would enable tracking of a particular vehicle across repeated hyperspectral images in a cluttered urban area.To begin to explore this question a vehicle tracking experiment was conducted in the summer of 2005 on the Rochester Institute of Technology (RIT) campus in Rochester, New York. Several volunteer vehicles were moved around campus at specific times coordinated with over flights of RIT's airborne Modular Imaging Spectrometer Instrument (MISI). MISI collected sequential images of the campus in 70 spectral channels from 0.4 to 1.0 microns with a ground resolution of approximately 2.5 meters. Ground truth spectra and photographs were collected for the vehicles.These data are being analyzed to determine the ability to uniquely associate a vehicle in one image with its location in a subsequent image. Initial results have demonstrated that the spectral measurement of a specific vehicle can be used to find the same vehicle in a subsequent image, although this is not always possible and is very dependent upon the specifics of the situation. Additionally, efforts are presented that explore predicted performance for variations in scene and sensor parameters through an analytical performance prediction model.

Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

“…Our work is inspired by Yin, Porikli and Collins [31] who proposed a single target tracker for aerial imagery and Palaniappan et al [16] who used different set of visual features to extract feature likelihood maps and adaptively fuse them to obtain a single fusion map. While hyperspectral imagery has shown to have potential for vehicle tracking [10,27,28,24,29,26,25], the sensors are rare and expensive and hence most of the aerial tracking work has been done in infrared, single-band or RGB. In this section, we review the efforts done in the above sensor domains to solve vehicle detection and tracking and how hyperspectral imagery could possibly solve some of the problems.…”

“…Hyperspectral imagery captures spectral signatures that can be used to uniquely characterize the materials in a given scene, the most common application being vegetation and mineral separation. Kerekes et al [10] showed that it is possible to uniquely associate a vehicle in one image with its location in a subsequent image by using matched filter, given some constraints are imposed on the scene under consideration. The recent work of Svejkosky [21] shows that the spectral signatures of vehicles in hyperspectral imagery exhibit temporal variations due to changes in illumination, road surface properties and vehicle orientation -which justifies the outcomes in [10].…”

Spatial Object Tracking Using an Enhanced Mean Shift Method Based on Perceptual Spatial-Space Generation Model

“…In spatial on-orbit docking, object tracking could be used to track spatial aircraft and assist with ground control. Because the spatial images are mainly generated from low-rate videos [6] or airborne spectral imagery [7], which are captured by the aircraft sensors [8,9], their resolution and spatialtemporal coverage are not very ideal. In addition, because of differences in the sensor spectral bands, acquisition position, and contrast gradient setting, there are shifts in the relative position and scale zoom in multisource images with the same scene.…”

Enhanced Target Tracking Based on Mean Shift Algorithm for Satellite Imagery

“…C. Carrano implemented an ultrascale capable multiple-vehicle tracking algorithm for overhead persistent surveillance imagery which relies on the mover map, path dynamics, and image features to perform tracking [6]. Kerekes et al evaluated the feasibility for particular objects of interest to be located and tracked in sequential frames of hyperspectral imagery through the use of their potentially unique spectral reflectance characteristics and then using that information to find the same vehicle in a subsequent image [7].…”