Filter Banks for Hyperspectral Pixel Classification of Satellite Images

Rajadell, Olga; García-Sevilla, Pedro; Pla, Filiberto

doi:10.1007/978-3-642-10268-4_121

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Regarding the feature space for classification, this is changed after clustering. Coordinates are dismissed and spatial/spectral features suggested by Rajadell et al 11 are used. To calculate these features, 3 bands, 4 orientations and 2 frequency scales are used.…”

Section: Methodsmentioning

confidence: 99%

Selection of samples for active labeling in semi-supervised hyperspectral pixel classification

et al. 2011

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One of the problems in semi-supervised land classification tasks lies in improving classification results without increasing the number of pixels to be labeled. This would be possible if, instead of increasing the amount of data we increased the reliability of the data. We suggest to replace the random selection by a unsupervised clustering based selection strategy in building the training data. We use a mode seeking clustering method to search for cluster representatives, which will be labeled and then used for training. Here an improvement to the result of the clustering algorithm is introduced by taking advantage of the spatial information in the image. The number of selected samples provided by the clustering can be reduced by using a spatial-density criterion to dismiss redundant training information. Two different alternatives are considered for a spatial criterion, one dismisses selected samples in the same neighbourhood and the other includes the pixel coordinates for giving the spatial information a larger weight in the clustering. Both alternatives improve the classification-segmentation results. The classification scheme with training selection provides state-of-the-art pixel classification results using a smaller training set and suggests an alternative to random selection.

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Section: Methodsmentioning

confidence: 99%

Selection of samples for active labeling in semi-supervised hyperspectral pixel classification

et al. 2011

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“…In this paper Gabor filtering will be used because, in general, they have provided the best results in different sort of texture characterization experiments [10] [11]. In this case, features are obtained by filtering the input image with a set of filters.…”

Section: Feature Extraction Based On Gabor Filtersmentioning

confidence: 99%

On the Influence of Spatial Information for Hyper-spectral Satellite Imaging Characterization

Rajadell

García-Sevilla

Pla

2011

Pattern Recognition and Image Analysis

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Abstract. Land-use classification for hyper-spectral satellite images requires a previous step of pixel characterization. In the easiest case, each pixel is characterized by its spectral curve. The improvement of the spectral and spatial resolution in hyper-spectral sensors has led to very large data sets. Some researches have focused on better classifiers that can handle big amounts of data. Others have faced the problem of band selection to reduce the dimensionality of the feature space. However, thanks to the improvement in the spatial resolution of the sensors, spatial information may also provide new features for hyper-spectral satellite data. Here, an study on the influence of spectral-spatial features combined with an unsupervised band selection method is presented. The results show that it is possible to reduce very significantly the number of spectral bands required while having an adequate description of the spectral-spatial characteristics of the image for pixel classification tasks.

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