We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Classifying land development in high-resolution Satellite imagery using hybrid structural-multispectral features

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Unsalan, C. ; Dept. of Electr. & Comput. Eng., Ohio State Univ., Columbus, OH, USA ; Boyer, K.L.

It is well known that combining spatial and spectral information can improve land use classification from satellite imagery. Human activity on the ground, such as construction, induces changes in both the photometric structure of the image and in its spectral content owing to, primarily, changes in vegetation density and surface materials. This paper introduces a novel approach to combine spatial (more precisely, structural) information extracted from (1-m resolution) panchromatic Ikonos imagery with the multispectral response (4-m resolution) available from the same sensor. Of the prior work combining spatial and spectral information, none has extracted structural features as we do, and none has combined these information sources as early in the process. The classifier we describe here, discriminating urban and rural regions, is a front-end component of a fairly complete satellite image analysis system that identifies suburban residential areas and extracts their street networks and single-family houses. We extract structural information in the form of photometric straight lines and their spatial arrangement over relatively small neighborhoods. To capture the multispectral information, we turn to the well-known normalized difference vegetation index (NDVI) and an improved linearized version of our own development (details of the structural analysis and the theoretical development of the linearized NDVI appear elsewhere). This paper addresses the novel combination of these types of features (hybrids) by using the structural features, straight line support regions based on gradient orientation, as cue regions for multispectral analysis. We test the hybrid features in a range of parametric and nonparametric classifiers. We also implement and test a probabilistic relaxation algorithm followed by the maximum a priori decision rule. We report extensive results that indicate significant improvements in classification accuracy using the hybrid features.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:42 ,  Issue: 12 )