Land use classification of ERS-1 images with an artificial neural network

Sebastian Carl; Roland Kraft

doi:10.1117/12.196745

30 December 1994 Land use classification of ERS-1 images with an artificial neural network

Sebastian Carl, Roland Kraft

Proceedings Volume 2315, Image and Signal Processing for Remote Sensing; (1994) https://doi.org/10.1117/12.196745
Event: Satellite Remote Sensing, 1994, Rome, Italy

Abstract

Agricultural crop monitoring by remote sensing techniques benefits from the availability of weather independent data like ERS-1.SAR.PRI scenes. Signature based traditional agricultural landuse classification methods can only be performed on heavily filtered radar data due to apparent speckle noise. However, the speckle contains textural information on the illuminated area. Thus a processing pipeline for a texture based classification approach of unfiltered ERS- 1.SAR.PRI data was developed using two different kinds of neural networks. A Kohonen Map is used to visualize the textural features and to define proper training areas for a subsequent supervised classification with a backpropagation net. The pipeline was applied to mono- and multitemporal ERS-1 scenes of a site near Prenzlau in Brandenburg, Germany. First results are encouraging and demonstrate the possibility to discriminate several landuse types. The reclassification error of the training samples was less than 5%. Entire classification results correspond to the ground truth data quite well. The main advantage of the processing pipeline is that both signature and texture features of the unfiltered image are used to distinguish between different classes.

Citation Download Citation

Sebastian Carl and Roland Kraft "Land use classification of ERS-1 images with an artificial neural network", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); https://doi.org/10.1117/12.196745

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