Share Email Print
cover

Proceedings Paper

Land cover classification in multispectral satellite imagery using sparse approximations on learned dictionaries
Author(s): Daniela I. Moody; Steven P. Brumby; Joel C. Rowland; Garrett L. Altmann
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a modified Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using CoSA: unsupervised Clustering of Sparse Approximations. We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska (USA). Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties (e.g., soil moisture and inundation), and topographic/geomorphic characteristics. In this paper, we explore learning from both raw multispectral imagery, as well as normalized band difference indexes. We explore a quantitative metric to evaluate the spectral properties of the clusters, in order to potentially aid in assigning land cover categories to the cluster labels.

Paper Details

Date Published: 22 May 2014
PDF: 11 pages
Proc. SPIE 9124, Satellite Data Compression, Communications, and Processing X, 91240Y (22 May 2014); doi: 10.1117/12.2049843
Show Author Affiliations
Daniela I. Moody, Los Alamos National Lab. (United States)
Steven P. Brumby, Los Alamos National Lab. (United States)
Joel C. Rowland, Los Alamos National Lab. (United States)
Garrett L. Altmann, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 9124:
Satellite Data Compression, Communications, and Processing X
Bormin Huang; Chein-I Chang; José Fco. López, Editor(s)

© SPIE. Terms of Use
Back to Top