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An landcover fuzzy logic classification by maximumlikelihood
In present days remote sensing is most used application in many sectors....
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A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions
In the field of empirical modeling using Genetic Programming (GP), it is...
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A Recent Survey on the Applications of Genetic Programming in Image Processing
During the last two decades, Genetic Programming (GP) has been largely u...
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Genetic Programming and Gradient Descent: A Memetic Approach to Binary Image Classification
Image classification is an essential task in computer vision, which aims...
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Soft Genetic Programming Binary Classifiers
The study of the classifier's design and it's usage is one of the most i...
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How Far Can You Get By Combining Change Detection Algorithms?
In this paper we investigate how state-of-the-art change detection algor...
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On Uniquely Closable and Uniquely Typable Skeletons of Lambda Terms
Uniquely closable skeletons of lambda terms are Motzkin-trees that prede...
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A Soft Computing Approach for Selecting and Combining Spectral Bands
We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimization problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learned spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes.
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