Showing papers by "Reid B. Porter published in 2002"

Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

Abstract: The authors have developed an automated feature detection/classification system, called GENetic Imagery Exploitation (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENIE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multispectral remotely-sensed images. The authors describe their system in detail together with experiments involving comparisons of GENIE with several conventional supervised classification techniques, for a number of classification tasks using multispectral remotely sensed imagery.

Genetic algorithms and support vector machines for time series classification

Abstract: We introduce an algorithm for classifying time series data. Since our initial application is for lightning data, we call the algorithm Zeus. Zeus is a hybrid algorithm that employs evolutionary computation for feature extraction, and a support vector machine for the final backend classification. Support vector machines have a reputation for classifying in high-dimensional spaces without overfitting, so the utility of reducing dimensionality with an intermediate feature selection step has been questioned. We address this question by testing Zeus on a lightning classification task using data acquired from the Fast On-orbit Recording of Transient Events (FORTE) satellite.

Feature extraction from multiple data sources using genetic programming

Abstract: Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Automated simultaneous multiple feature classification of MTI data

Abstract: Los Alamos National Laboratory has developed and demonstrated a highly capable system, GENIE, for the two-class problem of detecting a single feature against a background of non-feature. In addition to the two-class case, however, a commonly encountered remote sensing task is the segmentation of multispectral image data into a larger number of distinct feature classes or land cover types. To this end we have extended our existing system to allow the simultaneous classification of multiple features/classes from multispectral data. The technique builds on previous work and its core continues to utilize a hybrid evolutionary-algorithm-based system capable of searching for image processing pipelines optimized for specific image feature extraction tasks. We describe the improvements made to the GENIE software to allow multiple-feature classification and describe the application of this system to the automatic simultaneous classification of multiple features from MTI image data. We show the application of the multiple-feature classification technique to the problem of classifying lava flows on Mauna Loa volcano, Hawaii, using MTI image data and compare the classification results with standard supervised multiple-feature classification techniques.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Evolving a spatio-spectral network on reconfigurable computers for multispectral feature identification

Abstract: Feature identification attempts to find algorithms that can consistently separate a feature of interest from the background in the presence of noise and uncertain conditions. This paper describes the development of a high-throughput, reconfigurable computer based, feature identification system known as POOKA. POOKA is based on a novel spatio-spectral network, which can be optimized with an evolutionary algorithm on a problem-by-problem basis. The reconfigurable computer provides speed up in two places: 1) in the training environment to accelerate the computationally intensive search for new feature identification algorithms, and 2) in the application of trained networks to accelerate content based search in large multi-spectral image databases. The network is applied to several broad area features relevant to scene classification. The results are compared to those found with traditional remote sensing techniques as well as an advanced software system known as GENIE. The hardware efficiency and performance gains compared to software are also reported.