This paper presents a system that integrates biologically and geometrically inspired approaches to detecting objects from hyperspectral and/or multispectral (HS/MS), multiscale, multiplatform imagery. First, dimensionality reduction methods are studied and used for hyperspectral dimensionality reduction. Then, a biologically inspired method, SLEGION (Spatial Locally Excitatory Globally Inhibito...

This paper examines the influence of data fusion and wavelet compression on the automatic classification of urban environments. The principal data used is airborne Daedalus scanner imagery. Laser scanning data is introduced as an additional channel alongside the spectral channels thus effectively fusing the local height and multispectral information. The feature base is expanded to include both...

-Automated terrain analysis is required for many practical applications, such as outdoor navigation, image exploitation, remote sensing, reconnaissance and surveillance. In this paper, we present a hierarchical approach to analyze multispectral (MS) imagery for autonomous land vehicle navigation. The approach integrates several strategies to label various terrain classes in these images acquire...

Unmanned Aerial Vehicles (UAVs) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. This study has developed a UAV capable of collecting hyper resolution visible, multispectral and thermal imagery for application to Precision Viticulture (PV). Traditional modes of data collection are not well suited to the detection of subtle but important changes in viney...

Classification is an important field with many applications. In particular, the classification of digital imagery has important applications in the mapping community. In this paper the authors compare five different classification methods on LANDSAT imagery of Australia and multispectral imagery of south-central Virginia: support vector machines, neural networks, nearest-neighbor, discriminant ...

Onboard compression of hyperspectral imagery is important for reducing the burden on downlink resources. Here we describe a novel adaptive predictive technique for lossless compression of hyperspectral data. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that is competitive with the best results from the literature. Al...

Classifying roads in remotely sensed imagery has been addressed by a number of research efforts. Detecting these features is important for a variety of endeavors such as agricultural assessment and urban planning. This study investigates the viability of using a pulse-coupled neural network to recognize roads in Landsat-4 Thematic Mapper multispectral imagery.

Urban greenspace is important for the health of cities. Up-to-date databases and information are vital to maintain and monitor growth in cities. During the last decade, advances in spaceborne hyperspectral sensors have resulted in some advantages being gained over multispectral sensors for land cover monitoring (due to increased spectral resolution). The objective of this research was to compar...

Under controlled illumination conditions, visual Face Recognition systems perform well for faces with low or no disguise [1]. Infrared (Thermal) Face Recognition provides an enticing alternative to Visible Face Recognition due to the relative insensitivity of IR imagery to illumination changes and disguise [2]. Image fusion is in fact a combination that extracts redundant and complementary info...