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, we provide a seminal view on recent a...

In a recent studie (Greiwe and Ehlers, 2005), the author developed a decision based data fusion approach for the analysis of spatial and spectral high resolution image data of an urban scene. Image segments of high resolution orthophotos were classified with additional material information derived from hyperspectral image data. To retrieve this material information, the hyperspectral image data...

Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and we...

In the research of remote sensing for precision farming applications, the data quality of the aerial hyperspectral imaging system suffers from geometric distortions. Some of the distortions are caused by aircraft attitude change during the current pushbroom type image scanning. These distortions must be corrected before image data can be geo-referenced and used for field pattern identifications...

We describe a method of processing hyperspectral images of natural scenes that uses a combination of kmeans clustering and locally linear embedding (LLE). The primary goal is to assist anomaly detection by preserving spectral uniqueness among the pixels. In order to reduce redundancy among the pixels, adjacent pixels which are spectrally similar are grouped using the k-means clustering algorith...

Hyperspectral imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues. Tissue samples mounted on slides were identified from 10 different patients...

This paper describes a new algorithm for feature extraction on hyperspectral images based on blind source separation (BSS) and distributed processing. I use Independent Component Analysis (ICA), a particular case of BSS, where, given a linear mixture of statistical independent sources, the goal is to recover these components by producing the unmixing matrix. In the multispectral/hyperspectral i...

The incorporation of hyperspectral sensors aboard airborne/satellite platforms is currently producing a nearly continual stream of multidimensional image data, and this high data volume has soon introduced new processing challenges. The price paid for the wealth spatial and spectral information available from hyperspectral sensors is the enormous amounts of data that they generate. Several appl...

Hyperspectral remote sensing technique provides fine and detailed spectral information by contiguous and narrow spectral channels. For the traditional classification algorithms, most of them are based on spectral information; spatial information which is useful for the hyperspectral data analysis is paid a little attention to. So, hyperspectral image classification based on effective combinatio...

Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. In recent years, several efforts have been directed towards the incorporation of high-performance computing systems and architectures into remote sensing missions. With the aim of providing an overview of current and new t...