Classifying Land Development in High-Resolution Satellite Imagery Using Hybrid Structural-Multispectral Features

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 relaxatio..

Empirical Analysis and Modelling of Information and Communications Technology in Agriculture for Southern Ontario, Canada

Information and communications technology (ICT) represents an important enabling technology for on-farm operations that helps to maximise yield and minimise on-farm inputs. This study investigates the adoption factors and coverage characteristics of ICT in Southern Ontario. A set of eight site and situation adoption factors were identified explaining 57% of the variation in agricultural high-speed Internet utilisation for Southern Ontario. ICT coverage was assessed through service carrier and band factors, and their presence in rural settlements. Findings of the research indicate that there exists a digital divide among settlements in Southern Ontario and recommendations for targeted policy and investment in infrastructure are proposed to bridge the gap