Remotely sensed hyperspectral imaging instruments are capable of collecting hundreds of images corresponding to different wave length channels for the same area on the surface of the Earth. For instance, NASA is continuously gathering high dimensional image data with instruments such as the Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS). This advanced sensor for Earth observation records the visible and near-infrared spectrum of the reflected light using more than 200 spectral bands, thus producing a stack of images in which each pixel (vector) is represented by a spectral signal that uniquely characterizes the underlying objects. The resulting data volume typically comprises several gigabytes per flight. In this article, we describe the state of the art in the devel opment and application of image and signal processing techniques for advanced information extraction from hyperspectral data. The article also describes new trends for efficient pro cessing of such data using parallel and distributed processing techniques in the context of time-critical applications.