Hyperspectral imaging has important benefits in remote sensing and material identification. This paper describes a class of hyperspectral imaging systems which utilize a novel optical processor that provides video-rate hyperspectral datacubes. These systems have no moving parts and do not operate by scanning in either the spatial or spectral dimension. They are capable of recording a full three-dimensional (two spatial, one spectral) hyperspectral datacube with each video frame, ideal for recording data on transient events, or from unstabilized platforms. We will present the results of laboratory and field-tests for several of these imagers operating in the visible, near-infrared, mid-wavelength infrared (MWIR) and long-wavelength infrared (LWIR) regions.

In EO tracking, target spatial and spectral features can be used to improve performance since they help distinguish the targets from each other when confusion occurs during normal kinematic tracking. In this paper we introduce a method to encode a target's descriptive spatial information into a multi-dimensional signature vector, allowing us to convert the problem of spatial template matching into a form similar to spectral signature matching. This allows us to leverage multivariate algorithms commonly used with hyperspectral data to the problem of exploiting panchromatic imagery. We show how this spatial signature formulation naturally leads to a hybrid spatial-spectral descriptor vector that supports exploitation using commonly-used spectral algorithms.We introduce a new descriptor called Spectral DAISY for encoding spatial information into a signature vector, based on the concept of the DAISY 1 dense descriptor. We demonstrate the process on real data and show how the combined spatial/spectral feature can be used to improve target/track association over spectral or spatial features alone.

No abstract

No abstract

This paper presents a feature-based watermarking scheme which is robust to geometric and common image processing attacks. The improved Harris corner detector is used to find the robust feature points, which correspond to high corner responses and survive various attacks. The watermark is embedded in the Fourier frequency domain of the disk area centered at each feature point to ensure the resilience to the local attacks and common image processing. The image normalization method is adopted to determine the possible rotation for reducing synchronization errors between the embedded and extracted watermarks. The watermark detection decision is based on the number of matched disks in terms of the number of matched bits between the recovered and embedded watermarks in embedding blocks. Experimental results demonstrate that our scheme is more robust to geometric and common image processing attacks than the peer feature-point-based approaches.