Patricia G. Foschi

Confidence in linear spectral unmixing of single pixels

The authors propose a method that estimates the membership of a mixed pixel in various possible mixture proportions. The method relies on the creation of model mixtures and the estimation of their local density in the vicinity of the pixel under consideration.

Analysis of polar stratospheric cloud measurements from AVHRR

This work investigates thermal emission from polar stratospheric clouds (PSCs) measured at 10.9 and 11.9 μm wavelengths (channels 4 and 5) by the advanced very high resolution radiometer (AVHRR). PSCs can be broadly categorized by particle composition as either ice or nitric acid mixtures. This work shows that nitric acid PSCs are invisible to AVHRR, while some ice PSCs can be detected. Methods were developed to discriminate ice PSCs from other cloud types in AVHRR imagery based on the brightness temperature difference between channels 4 and 5. When PSCs are identified in AVHRR imagery, it is possible to estimate the PSC optical depth, effective radius, and ice water path using relationships reported here, however, these estimates may have large uncertainties.

A geometric approach to a mixed pixel problem: detecting subpixel woody vegetation

Abstract In digital satellite imagery, small fragments of woody vegetation are difficult to detect because they frequently are smaller than the pixel size and are mixed with other land cover classes. A method for detecting subpixel woody vegetation, which analyzes mixture phenomena at the individual pixel level, is presented. This method relies on a moving window to collect training sets for adjacent land cover. In order to locate pixels of interest and to decrease noise, image-derived masks are integrated with the original digital imagery in a geocoded information system. A rule-based scheme is employed to organize relative spatial and spectral information into classification decision procedures. Tests using simulated multispectral and panchromatic SPOT HRV imagery of lowland Britain have shown that the developed method discriminates significantly more woody vegetation than standard multispectral classification.

Toward a polar stratospheric cloud climatology using advanced very high resolution radiometer thermal infrared data

Polar stratospheric clouds (PSCs) play a critical role in ozone depletion over both polar regions. To date, the most complete PSC records consist of measurements from limb-viewing satellites that offer limited spatial and temporal coverage. To construct a more complete and long-term PSC climatology, we investigated the use of advanced very high resolution radiometer (AVHRR) satellite imagery for detecting PSCs. Two approaches were examined: (1) a correlative approach relating image-derived data to verification data, and (2) an interpretation of the image-derived data based on a radiative transfer model. The model determined that Type II or ice PSCs can be detected using the AVHRR thermal infrared channels. The image-derived data, namely density-sliced channel 5 temperature data, color composites, and density-sliced channel 4‐5 brightness temperature difference images, provide quick views of potential PSC locations. The model-based approach provides the best method for constructing a long-term ice PSC climatology from the AVHRR archive.

Active learning for detecting a spectrally variable subject in color infrared imagery

To classify Egeria densa, Brazilian waterweed, in scan-digitized color infrared aerial photographs, we are developing an interactive computer system based on data-mining techniques with active learning capabilities. Key components of the system are: feature extraction, automatic classification, active learning, and experimental evaluation.

Identification of Waste Tires Using High-resolution Multispectral Satellite Imagery

This article discusses how locating waste tire piles is an important priority for regulatory agencies because of the health and environmental risks that they pose. Governments are making great progress in cleaning up known waste tire piles, but are unable to address piles of waste tires that they are unaware of. The authors of this article developed a methodology for detecting waste tire piles by combining spectral processing and visual interpretation. Though this method is far from an automated, instant solution, there has been great success in identifying waste tire piles that were previously unknown to government officials.

An active learning approach to egeria densa detection in digital imagery

This chapter focuses on the development of an active learning approach to an image mining problem for detectingEgeria densa(a Brazilian waterweed) in digital imagery. An effective way of automatic image classification is to employ learning systems. However, due to a large number of images, it is often impractical to manually create labeled data for supervised learning. On the other hand, classification systems generally require labeled data to carry out learning. In order to strike a balance between the difficulty of obtaining labeled images and the need for labeled data, we explore an active learning approach to image mining. The goal is to minimize the task of expert labeling of images: if labeling is necessary, only those important parts of an image will be presented to experts for labeling. The critical issues are: (1) how to determine what should be presented to experts; (2) how to minimize the number of those parts for labeling; and (3) after a small number of labeled instances are available, how to effectively learn a classifier and apply it to new images. We propose to use ensemble methods for active learning in Egeria detection. Our approach is to use the combined classifications of the ensemble of classifiers to reduce the number of uncertain instances in the image classification process and thus achieve reduced expert involvement in image labeling. We demonstrate the effectiveness of our proposed system via experiments using a real-world application of Egeria detection. Practical concerns in image mining using active learning are also addressed and discussed.Trends in data-mining applications : from research labs to fortune 500 companies. 1. Mining wafer fabrication : framework and challenges. 2. Damage detection employing data-mining techniques. 3. Data projection techniques and their application in sensor array data processing. 4. An application of evolutionary and neural data-mining techniques to customer relationship management. 5. Sales opportunity miner : data mining for automatic evaluation of sales opportunity. 6. A fully distributed framework for cost-sensitive data mining. 7. Application of variable precision rough set approach to care driver assessment. 8. Discovery of patterns in earth science data using data mining. 9. An active learning approach to Egeria densa detection in digital imagery. 10. Experiences in mining data from computer simulations. 11. Statistical modeling of large-scale scientific simulation data. 12. Data mining for gene mapping. 13. Data-mining techniques for microarray data analysis. 14. The use of emerging patterns in the analysis of gene expression profiles for the diagnosis and understanding of diseases. 15. Proteomic data analysis : pattern recognition for medical diagnosis and biomarker discovery. 16. Discovering patterns and reference models in the medical domain of isokinetics. 17. Mining the cystic fibrosis data. 18. On learning strategies for topic-specific web crawling. 19. On analyzing web log data : a parallel sequence-mining algorithm. 20. Interactive methods for taxonomy editing and validation. 21. The use of data-mining techniques in operational crime fighting. 22 .Using data mining for intrusion detection. 23. Mining closed and maximal frequent itemsets. 24. Using fractals in data mining. 25 .Genetic search for logic structures in data.