High-resolution land cover classification using low resolution global data

Mark J. Carlotto

doi:10.1117/12.1518460

23 May 2013 High-resolution land cover classification using low resolution global data

Mark J. Carlotto

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 874513 (2013) https://doi.org/10.1117/12.1518460
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

A fusion approach is described that combines texture features from high-resolution panchromatic imagery with land cover statistics derived from co-registered low-resolution global databases to obtain high-resolution land cover maps. The method does not require training data or any human intervention. We use an MxN Gabor filter bank consisting of M=16 oriented bandpass filters (0-180°) at N resolutions (3-24 meters/pixel). The size range of these spatial filters is consistent with the typical scale of manmade objects and patterns of cultural activity in imagery. Clustering reduces the complexity of the data by combining pixels that have similar texture into clusters (regions). Texture classification assigns a vector of class likelihoods to each cluster based on its textural properties. Classification is unsupervised and accomplished using a bank of texture anomaly detectors. Class likelihoods are modulated by land cover statistics derived from lower resolution global data over the scene. Preliminary results from a number of Quickbird scenes show our approach is able to classify general land cover features such as roads, built up area, forests, open areas, and bodies of water over a wide range of scenes.

Citation Download Citation

Mark J. Carlotto "High-resolution land cover classification using low resolution global data", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 874513 (23 May 2013); https://doi.org/10.1117/12.1518460

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KEYWORDS

Image classification

Roads

Image filtering

Bandpass filters

Filtering (signal processing)

Image resolution

Sensors

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