Advances in classification for land cover mapping using SPOT HRV imagery

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

Utilizing Temporally Invariant Calibration Sites to Classify Multiple Dates and Types of Satellite Imagery

Mapping past time periods (retrospective mapping) using remotely sensed data is hindered by a lack of coincident calibration and validation information. The identification of features of same ground cover invariant across time and their use as calibration and validation data addresses this challenge by: (a) streamlining the process of image calibration for multiple dates, and (b) allowing each image to generate its own spectral signature. This study investigates the use of temporally invariant calibration and validation data to map land-cover in Massachusetts, employing five satellite images collected from five separate dates and different sensors. The results indicate that this technique can be used to produce land cover classifications of similar overall map accuracy to published mapping studies. Classification accuracy using this method is highly dependent on the characteristics (radiometric, spectral, and spatial) of the satellite imagery. © 2011 American Society for Photogrammetry and Remote Sensing