Abstract
The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm.
Highlights
Nowadays the increasing technology of airborne sensors with their capabilities for capturing images, including those on board the new generations of Unmanned Aerial Vehicles, demands solutions for different image-based applications
The areas where the identification of spectral signatures are suitable include agricultural crop ordination, forest areas determination, urban identification and damage evaluation in catastrophes or dynamic path planning during rescue missions or intervention services in catastrophes, among others. This justifies the choice of the images with different spectral signatures as the data where the proposed approach is to be applied, providing an application for this kind of sensors
The behaviour of different features has been studied in texture classifications [1,2,3]
Summary
Nowadays the increasing technology of airborne sensors with their capabilities for capturing images, including those on board the new generations of Unmanned Aerial Vehicles, demands solutions for different image-based applications. The areas where the identification of spectral signatures are suitable include agricultural crop ordination, forest areas determination, urban identification and damage evaluation in catastrophes or dynamic path planning during rescue missions or intervention services in catastrophes (fires, floods, etc.), among others. This justifies the choice of the images with different spectral signatures as the data where the proposed approach is to be applied, providing an application for this kind of sensors. The aerial images used in our experiments do not display texture patterns This implies that textured regions cannot be identified.
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