International audienceThe advantage of hyperspectral image is the possibility to access the spectral signature for each pixel having the capability to see the unseen. The classifiers used for hyperspectral images can be deeply studied and analyzed for obtaining an accurate classification to extract the features from the images. In this paper we aim to analyze the hyperspectral images by trying to identify the nature of each element in the field, which is represented by a pixel, using the techniques of supervised learning
Supervised classification is commonly used to produce a thematic map from hyperspectral data. A clas...
Feature extraction is a preprocessing step for hyperspectral image classification. Principal compone...
Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invalu...
Abstract: Remote sensing involves collection and interpretation of information about an object, area...
Hyperspectral imaging is a technique which uses hyperspectral sensors to collect spectral informatio...
Supervised hyperspectral image (HSI) classification has been acknowledged as one of the fundamental ...
International audienceThe recent advances in hyperspectral remote sensing technology allow the simul...
The goal of this work is to gain basic knowledge about hyperspectral imaging. The theoretical part d...
Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery p...
With the development of remote sensing technology, the application of hyperspectral images is becomi...
International audienceThe purpose of this tutorial is to get familiar with some techniques for the a...
This chapter introduces several feature extraction techniques (FETs) and machine learning algorithms...
This research work presents a supervised classification framework for hyperspectral data that takes ...
This paper presents a classification methodology for hyperspectral data based on synergetics theory....
Hyperspectral imagery has received considerable attention in the last decade as it provides rich spe...
Supervised classification is commonly used to produce a thematic map from hyperspectral data. A clas...
Feature extraction is a preprocessing step for hyperspectral image classification. Principal compone...
Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invalu...
Abstract: Remote sensing involves collection and interpretation of information about an object, area...
Hyperspectral imaging is a technique which uses hyperspectral sensors to collect spectral informatio...
Supervised hyperspectral image (HSI) classification has been acknowledged as one of the fundamental ...
International audienceThe recent advances in hyperspectral remote sensing technology allow the simul...
The goal of this work is to gain basic knowledge about hyperspectral imaging. The theoretical part d...
Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery p...
With the development of remote sensing technology, the application of hyperspectral images is becomi...
International audienceThe purpose of this tutorial is to get familiar with some techniques for the a...
This chapter introduces several feature extraction techniques (FETs) and machine learning algorithms...
This research work presents a supervised classification framework for hyperspectral data that takes ...
This paper presents a classification methodology for hyperspectral data based on synergetics theory....
Hyperspectral imagery has received considerable attention in the last decade as it provides rich spe...
Supervised classification is commonly used to produce a thematic map from hyperspectral data. A clas...
Feature extraction is a preprocessing step for hyperspectral image classification. Principal compone...
Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invalu...