Sensor: a tool for the simulation of hyperspectral remote sensing systems

Abstract The consistent end-to-end simulation of airborne and spaceborne earth remote sensing systems is an important task, and sometimes the only way for the adaptation and optimisation of a sensor and its observation conditions, the choice and test of algorithms for data processing, error estimation and the evaluation of the capabilities of the whole sensor system. The presented software simulator SENSOR (Software Environment for the Simulation of Optical Remote sensing systems) includes a full model of the sensor hardware, the observed scene, and the atmosphere in between. The simulator consists of three parts. The first part describes the geometrical relations between scene, sun, and the remote sensing system using a ray-tracing algorithm. The second part of the simulation environment considers the radiometry. It calculates the at-sensor radiance using a pre-calculated multidimensional lookup-table taking the atmospheric influence on the radiation into account. The third part consists of an optical and an electronic sensor model for the generation of digital images. Using SENSOR for an optimisation requires the additional application of task-specific data processing algorithms. The principle of the end-to-end-simulation approach is explained, all relevant concepts of SENSOR are discussed, and first examples of its use are given. The verification of SENSOR is demonstrated. This work is closely related to the Airborne PRISM Experiment (APEX), an airborne imaging spectrometer funded by the European Space Agency.

[1]  M. Schaepman,et al.  THE POTENTIAL OF SPECTRAL RESAMPLING TECHNIQUES FOR THE SIMULATION OF APEX IMAGERY BASED ON AVIRIS DATA , 1999 .

[2]  Changyong Cao,et al.  Satellite Hyperspectral Imaging Simulation , 1999 .

[3]  Rainer Sandau,et al.  TARGET RELATED MULTISPECTRAL AND TRUE COLOUR OPTIMIZATION OF THE COLOUR CHANNELS OF THE LH SYSTEMS ADS40 , 2000 .

[4]  Daniel Schläpfer,et al.  APEX - Airborne PRISM Experiment: A new Airborne Hyperspectral Imager for the Simulation of ESA's Land Surface Processes and Interactions Mission , 1999 .

[5]  R. Richter.  A fast atmospheric correction algorithm applied to Landsat TM images , 1990 .

[6]  Rolando V. Raqueno,et al.  Advanced synthetic image generation models and their application to multi/hyperspectral algorithm development , 1999, Other Conferences.

[7]  D. E. Bowker,et al.  Spectral reflectances of natural targets for use in remote sensing studies , 1985 .

[8]  Rudolf Richter.  Model SENSAT: a tool for evaluating the system performance of optical sensors , 1990, Defense, Security, and Sensing.

[9]  K. Itten,et al.  APEX – AIRBORNE PRISM EXPERIMENT A NEW CONCEPT FOR AN AIRBORNE IMAGING SPECTROMETER , 1998 .

[10]  R. Jenssen,et al.  1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE , 1998 .

[11]  A. Goetz,et al.  Terrestrial imaging spectroscopy , 1988 .

[12]  P Mouroulis,et al.  Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information. , 2000, Applied optics.

[13]  M. Schaepman,et al.  Calibration and Validation Concept for the Airborne PRISM Experiment (APEX) , 2000 .

[14]  J. Hay,et al.  Estimating Solar Irradiance on Inclined Surfaces: A Review and Assessment of Methodologies , 1985 .

[15]  Ralf Reulke,et al.  Systemtheoretische Grundlagen optoelektronischer Sensoren. , 1995 .

[16]  A. Börner,et al.  The Optimization of the Stereo Angle of CCD-Line-Scanners , 1996 .

[17]  J. Dozier,et al.  A faster solution to the horizon problem , 1981 .

[18]  A. Berk.  MODTRAN : A moderate resolution model for LOWTRAN7 , 1989 .