Satellite hyperspectral imaging deals with heterogenous images containing different texture areas. Filter banks are frequently used to characterize textures in the image performing pixel classification. This filters are designed using Different scales and orientations in order to cover all areas in the frequential domain. This work is aimed at studying the influence of the different scales used in the analysis, comparing texture analysis theory with hyperspectral imaging necessities. To pursue this, Gabor filters over complex planes and opponent features are taken into account and also compared in the feature extraction proces
This research work presents a supervised classification framework for hyperspectral data that takes ...
The best technique to extract information from remotely sensed image is classification. The problem ...
International audienceIn this paper, we deal with the problem of extracting meaningful textural feat...
Abstract. Satellite hyperspectral imaging deals with heterogenous images con-taining different textu...
This paper presents a spectral-spatial pixel characterization method for hyperspectral images. The ...
In this article various methodologies, based on the use of Gabor filters, are described and analysed...
In this paper, a texture based algorithm is developed for classifying color images. The images are f...
Gabor filter is widely used to extract spatial texture features of hyperspectral images (HSI) for HS...
We present a study of the contribution of the different scales used by several feature extraction me...
International audienceTexture characterization from the metrological point of view is addressed in o...
International audienceMany approaches of texture analysis by color or hyperspectral imaging are base...
Spatial-frequency methods have been extensively and successfully employed by many computer vision re...
In this paper, a novel rotation and scale invariant approach for texture classification based on Gab...
Texture features are useful for segmentation of high-resolution satellite imagery. This paper prese...
Hyperspectral remote sensing provides data in large amounts from a wide range of wavelengths in the ...
This research work presents a supervised classification framework for hyperspectral data that takes ...
The best technique to extract information from remotely sensed image is classification. The problem ...
International audienceIn this paper, we deal with the problem of extracting meaningful textural feat...
Abstract. Satellite hyperspectral imaging deals with heterogenous images con-taining different textu...
This paper presents a spectral-spatial pixel characterization method for hyperspectral images. The ...
In this article various methodologies, based on the use of Gabor filters, are described and analysed...
In this paper, a texture based algorithm is developed for classifying color images. The images are f...
Gabor filter is widely used to extract spatial texture features of hyperspectral images (HSI) for HS...
We present a study of the contribution of the different scales used by several feature extraction me...
International audienceTexture characterization from the metrological point of view is addressed in o...
International audienceMany approaches of texture analysis by color or hyperspectral imaging are base...
Spatial-frequency methods have been extensively and successfully employed by many computer vision re...
In this paper, a novel rotation and scale invariant approach for texture classification based on Gab...
Texture features are useful for segmentation of high-resolution satellite imagery. This paper prese...
Hyperspectral remote sensing provides data in large amounts from a wide range of wavelengths in the ...
This research work presents a supervised classification framework for hyperspectral data that takes ...
The best technique to extract information from remotely sensed image is classification. The problem ...
International audienceIn this paper, we deal with the problem of extracting meaningful textural feat...