Jacqueline LeMoigne

2-phase GA-based image registration on parallel clusters

Abstract Genetic algorithms (GAs) are known to be robust for search and optimization problems. Image registration can take advantage of the robustness of GAs in finding best transformation between two images, of the same location with slightly different orientation, produced by moving spaceborne remote sensing instruments. In this paper, we present 2-phase sequential and coarse-grained parallel image registration algorithms using GAs as optimization mechanism. In its first phase, the algorithm finds a small set of good solutions using low-resolution versions of the images. Based on these candidate low-resolution solutions, the algorithm uses the full resolution image data to refine the final registration results in the second phase. Experimental results are presented and revealed that our algorithms yield very accurate registration results for LandSat Thematic Mapper images, and the parallel algorithm scales quite well on the Beowulf parallel cluster.

Cost and risk analysis of small satellite constellations for earth observation

Distributed Space Missions (DSMs) are gaining momentum in their application to Earth science missions owing to their ability to increase observation sampling in spatial, spectral, temporal and angular dimensions. Past literature from academia and industry have proposed and evaluated many cost models for spacecraft as well as methods for quantifying risk. However, there have been few comprehensive studies quantifying the cost for multiple spacecraft, for small satellites and the cost risk for the operations phase of the project which needs to be budgeted for when designing and building efficient architectures. This paper identifies the three critical problems with the applicability of current cost and risk models to distributed small satellite missions and uses data-based modeling to suggest changes that can be made in some of them to improve applicability. Learning curve parameters to make multiple copies of the same unit, technological complexity based costing and COTS enabled small satellite costing have been studied and insights provided.

Hierarchical PCA Techniques for Fusing Spatial and Spectral Observations With Application to MISR and Monitoring Dust Storms

In this letter, we propose hierarchical principal component analysis (HPCA) techniques for fusing spatial and spectral data, and compare them to direct principal component analysis (DPCA) over Multiangle Imaging SpectroRadiometer (MISR) data. It is shown that the proposed methods are significantly faster than DPCA. In case of DPCA, we merge the 20 different images resulting from the four spectral bands over the nadir and the four forward angles. In the hierarchical case, we first merge the information from the four spectral camera bands; then, we integrate the spatial information from the five cameras in the second step (or vice versa) by applying principal component analysis (PCA) twice. The classification results show that fused data using HPCA compare favorably to DPCA or to classification using the original data. This is because applying PCA to one particular data domain (e.g., spectral data followed by spatial data or vice versa) tends to better remove redundancies and enhance features within that domain. In addition, classification through hierarchical data fusion results in computational savings over the other methods.

Mission to Planet Earth: AI views the world

In the late 1990s, NASA will launch a series of satellites to study the Earth as a dynamic system. The enormous size and complexity of the resulting data holdings pose several challenges and promise to test the limits of practical AI techniques.

The performance impact of data placement for wavelet decomposition of two-dimensional image data on SIMD machines

Wavelet transform is a mathematical tool through which 2D spatial image data can be mapped into wavelet space for compact representation and for various signal analyses. The highly regular structure of the wavelet decomposition algorithm makes it well-suited for fine-grained parallelization. Most existing parallelization approaches focus on how to map computing functions to processors, but pay little attention to the problem of data placement. We investigate the impact of different data placement schemes on their achievable speedups in a MasPar MP-2 parallel computer. Our experimental results show that data communication is a dominating factor which can influence the overall system performance. We drastically speed up the computation of the wavelet transform by maximally localizing interprocessor communications for data exchange.<<ETX>>

Wavelet dimension reduction of AIRS infrared (IR) hyperspectral data

Recently developed hyperspectral sensors provide much richer information than comparable multispectral sensors. However traditional methods that have been designed for multispectral data are not easily adaptable to hyperspectral data. One way to approach this problem is to perform dimension reduction as pre-processing, i.e. to apply a transformation that brings data from a high order dimension to a low order dimension. Wavelet spectral analysis of hyperspectral images has been recently proposed as a method for dimension reduction and, when tested on the classification of AVIRIS data, has shown promising results over the traditional principal component analysis (PCA) technique. We propose to extend and apply the wavelet analysis reduction method to the Atmospheric Infrared Sounder (AIRS) instrument data, designed to measure the Earths atmospheric water vapor and temperature profiles on a global scale. With more than 2,000 channels, the AIRS infrared data represent a good candidate for dimension reduction, and especially wavelet reduction, due to its computational efficiency and the large data sizes involved.

Computation of earth science products on spaceborne platforms

The next generation of Earth-observing spacecraft are likely to generate enormous volumes of data. A major challenge lies in the conversion of these mountains of data into information useful to researchers and other users. Hierarchical segmentation is one way to detect relationships among regions in a hyperspectral image. We implemented this algorithm on a next-generation space-capable hardware platform, and studied its performance before and after adapting it to use the platforms unique computational resources. We found that these adaptations enable an order-of-magnitude increase in performance over our initial implementation, and our detailed analysis points to areas for additional improvement.