A method for atmospheric correction of ATSR-2 optical imagery is presented which exploits the sensor's dual view capability. A general model of land surface bidirectional reflectance is developed and used as a constraint to simultaneously retrieve atmospheric aerosol opacity and bidirectional reflectance from top of atmosphere radiance. The inversion assumes no a priori knowledge of the land surface cover, Validation has been performed over boreal forest, showing close agreement between ATSR-2 optical depth retrieval and field measurements.

This paper presents a method for retrieval of aerosol opacity and land surface bi-directional reflectance using data from the second Along-Track Scanning Radiometer (ATSR-2). The method is based on inversion of a physically based model of land surface reflectance to provide a constraint on the spectral variation of reflectance. Validation is performed for a range of cover types

High-resolution data from the HRV (High Resolution Visible) sensors onboard the SPOT-1 satellite have been utilized for mapping semi-natural and agricultural land cover using automated digital image classification algorithms. Two methods for improving classification performance are discussed. The first technique involves the use of digital terrain information to reduce the effects of topography on spectral information while the second technique involves the classification of land-cover types using training data derived from spectral feature space. Test areas in Snowdonia and the Somerset Levels were used to evaluate the methodology and promising results were achieved. However, the low classification accuracies obtained suggest that spectral classification alone is not a suitable tool to use in the mapping of semi-natural cover types.

We have devised, written and tested an implementation of the Gaussian Maximum Likelihood classification method for a commercial image processor. This has resulted in significant savings in execution time for the classification of multispectural remotely-sensed imagery, at very little cost to the accuracy, when compared to a software version of the same algorithm.