dimensionality reduction of hyperspectral images via bands fuzzy clustering
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منابع مشابه
Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images
Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of thr...
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This paper presents genetic algorithm based band selection and classification on hyperspectral image data set. Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. In this paper, first filtering based on 2-D Emp...
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Dimensionality reduction is a big challenge in many areas. In this research we address the problem of high-dimensional hyperspectral images in which we are aiming to preserve its information quality. This paper introduces a study stability of the non parametric and unsupervised methods of projection and of bands selection used in dimensionality reduction of different noise levels determined wit...
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In this paper we present an eecient method for estimating the signiicant points of a gray level image by means of a fuzzy clustering algorithm. This method can be used to reduce the resolution of the image so it can be transmited and later reconstructed with the greatest reliability. We will show how using less than 0; 01 of the original information it is possible to reconstruct an image with a...
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Feature extraction based on evolutionary search offers new possibilities for improving classification accuracy and reducing measurement complexity in many data mining and machine learning applications. We present a family of genetic algorithms for feature synthesis through clustering of discrete attribute values. The approach uses new compact graph-based encoding for cluster representation, whe...
متن کاملimpact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images
anomaly detection (ad) has recently become an important application of hyperspectral images analysis. the goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. one way to improve the performance and runtime of these algorithms is to use dimensionality reduction (dr) techniques. this paper evaluates the effect of thr...
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نشریه دانشکده فنیجلد ۴۳، شماره ۴، صفحات ۰-۰
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