J. A. Benediktsson

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Feature extraction of hyperspectral remote sensing data is investigated. Principal component analysis (PCA) has shown to be a good unsupervised feature extraction. On the other hand, this methods only focus on second orders statistics. By mapping the data onto another feature space and using nonlinear function, Kernel PCA (KPCA) can extract higher order(More)
The purpose of this paper is to develop a method for denoising images corrupted with additive white Gaussian noise (AWGN). The noise degrades quality of the images and makes interpretations, analysis and segmentation of images harder. In the paper the use of the time invariant discrete curvelet transform for noise reduction is considered. The discrete(More)
This paper proposes a novel approach to the retrieval of buildings’ height from multi-angular high spatial resolution images. To achieve this task, we combined two main concepts: multilevel morphological attribute filters, used for the definition of the objects in the image, and geometric invariant moments exploited for the characterization of the spatial(More)
The purpose of this paper is to develop a method for denoising images corrupted with additive white Gaussian noise (AWGN). The noise degrades quality of the images and makes interpretations, analysis and segmentation of images harder. The discrete curvelet transform is a new image representation approach that codes image edges more efficiently than the(More)
Here an improvement to our previous framework for satellite image fusion is presented. A framework purely based on the sensor physics and on prior assumptions on the fused image. The contributions of this paper are two fold. Firstly, a method for ensuring 100% spectrally consistency is proposed, even when more sophisticated image priors are applied.(More)
In this paper, a new method for supervised hyperspectral data classification is proposed. In particular, the notion of stochastic minimum spanning forest (MSF) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, <i>M</i> marker maps are generated by randomly selecting pixels and(More)
In this paper different types of image classification will be studied. Decision level fusion, using a specific criterion or algorithm to integrate the classified results from different classifiers, has shown great benefits to improve classification accuracy of multi-source remote sensing images. Based on a survey to hyperspectral remote sensing(More)
The truth about what chemicals are to be found on the surface of Mars lies hidden in Gigabytes of hyperspectral data. How to reveal this mystery is the subject of this paper. Independent component analysis (ICA) is used for identification and classification of endmembers and for artifact removal. The classification results are compared with the result of a(More)
With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data(More)
Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspectral data. In this paper,(More)