Simultaneous Analysis of Hyperspectral Data Using the Fused Lasso

• Activity Number: 237
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Government Statistics Section
• Author(s): Nicole Mendoza*+ and Abel Rodriguez
• Companies: University of California, Santa Cruz and University of California, Santa Cruz
• Keywords: Fused LASSO ; Hyperspectral ; Variable Selection ; Chemical Plume Identification

Abstract

The use of hypersectral technology in remote sensing of chemical plumes has proven to be important for a wide variety of military and environmental applications. For example, hyperspectral imagery can be used to detect chemical warfare agents, and to monitor gas plumes in the atmosphere. A hyperspectral image is taken over the electromagnetic spectrum, and typically consists of thousands of pixels. Currently, algorithms for detecting, quantifying, and identifying the constituents of chemical plumes involve analyzing such images one pixel at a time. However, analyzing pixels individually is inefficient because it ignores the spatial relationships among neighboring pixels. As a novel approach, we consider a variant of the least absolute shrinkage and selection operator (LASSO), called the Fused LASSO, which analyses pixels simultaneously, and allows us to borrow strength or information from nearby pixels. As an illustration, we apply the Fused Lasso to a hyperspectral image which contains plume-present pixels and no-plume pixels, and show that borrowing information across nearby pixels substantially improves the ability of our model to identify gases present in an embedded plume.