Given recent advancements of modern hyperspectral (HS) sensors, the potential for information extraction has increased
drastically given the continual improvements in spatial and spectral resolution. As a result, more sophisticated feature
extraction and target detection (TD) algorithms are needed to improve the performance of the image analyst, whether
computer-based or human. In this paper, a novel TD algorithm based on Projection Pursuit (PP) is proposed and
implemented. PP is a well-known technique for dimensionality reduction in multi-band data sets without loss of any
critical information. This technique highlights different features of interest in an image, thus improving and simplifying
subsequent anomaly detection. The new target detection technique is based on a hybrid of PP and Reed_Xiaoli (RX)
anomaly detector. In this study, the combining of PP with the RX detector (PPRX) adds some extra value to the standard
RX detection technique and leads the development of a TD method that can be applied on hyperspectral/multispectral
(MS) data sets. This novel technique, after being trained by using the Projection Index (PI) and a priori information of
target of interest, utilizes RX detector to evaluate each potential projection. The main drawback of previously introduced
PP methods such as those based on Information Divergence and Kurtosis/Skewness is that these techniques are sensitive
to statistical outliers and cannot be used to highlight a specific target of interest. This study uses three data sets: (1) 4-band IKONOS multispectral data (2) 210-band HYDICE, and (3) 200-band simulated hyperspectral data set.
Direct-sequence spread spectrum (DSSS) modulation offers many properties that make it well suited for a mobile environment including some inherent narrowband interference or jamming (NBJ) suppression capability and resistance to multipath fading. The estimation and filtering of unwanted narrowband signals in DSSS systems has been extensively addressed in previous work but has given limited insight to system performance when multipath fading is introduced and a diversity solution such as the ubiquitous Rake receiver is implemented. In this case, multiple correlators (or fingers) are used to extract the desired signal replicas from the individual delay path components. For the maximum ratio combiner (MRC) version of the Rake receiver, the signal replicas from each finger are then combined in some weighted sense to formulate the final decision threshold. The focus of this study is twofold: to investigate the inaccuracies incurred on path delay estimation due to the presence of NBJ and its impact on the system Bit Error Rate (BER). In order to reduce the impact of NBJ, some adaptive NBJ suppression filters are suggested.
Multispectral (MS) and hyperspectral (HS) sensors can facilitate target or anomaly detection in clutter since natural clutter and man-made objects diff er in the energy they radiate across the electromagnetic spectrum. Previous research in anomaly detection has formulated two popular algorithms: those based on Gauss-Markov Random Fields (GMRF) and the so-called RX-detector. Performance of these algorithms is dependent on a number of issues including spatial resolution, spectral correlation between the imaging bands, clutter/target model accuracy and the acquired data's signal-to-noise ratio (SNR). This paper provides a comparison study of the anomaly detection performance of the RXdetector and the GMRF-based algorithm using: (1) 4m MS imagery acquired f rom the IKONOS satellite and (2) pansharpened 1m MS imagery created by fusing the 4m MS and the associated 1m panchromatic image sets. The study will be based on the detection performance for stationary and slow moving targets selected f rom imagery acquired during training exercises at Canadian Forces Base (CFB) Petawawa and CFB Wainwright, Canada.
This paper describes a semi-automated building assessment method (SABAM) for estimating building edges with sub-pixel accuracy. The semi-automated approach is based on an earlier manual point method which determined building height using shadow length analysis. The manual method was then semi-automated using a sub-pixel edge detection algorithm to obtain more precise building edges and reduce human interpretation. Edge locations have been evaluated to within 1/100th of a pixel using gradient descent.
The recovery of a stellar object's Fourier phase from the bispectrum of the atmospherically- degraded images has been investigated with respect to object complexity, a wide range of light levels, severity of the atmospheric effects and the number of bispectral subplanes used. The incremental improvement in the quality of the recovered phase as the number of near-axis subplanes increases is greatest for complex sources and when photon-noise dominates. At high signal-to-noise ratios it was found that the extended Knox-Thompson transfer function has a higher phase variance than the bispectral transfer function. Analysis has confirmed this difference in behavior.
The triple correlation algorithm employing a minimum of computation is evaluated as a candidate process for near-real-time, quick-look, stellar image reconstruction at the telescope. Computational efficiency is gained by exploiting the binary nature of the photon-list images and by using a small number of high SNR bispectrum subplanes. When compared to the Knox-Thompson algorithm extended to 2, 4 and 6 subplanes, the fidelity of reconstruction by the TC process is better and less object dependent, even in the minimum-computation case of 2 planes.