Classification of Multi-sensor Data using a combination of Image Analysis Techniques

Most image analysis techniques have both strong and weak aspects. Training a complex neural network of image classification, for example, is a time-consuming process. Using a conventional classifier (e.g. maximum likelihood) in combination with a neural network classifier can reduce processing time while, at the same time, reinforcing the performances of both classifiers..

This paper deals with the classification of an area where the land use is mainly agriculture, with optical and microwave data as input. The optical data is acquired by the SPOT satellite in multi-spectral mode and the microwave data is acquired by the ERS-1 and ERS-2 satellites in single look complex mode. in the first step of the classification, the maximum likelihood classifier is used; the classes that are classified satisfactorily are masked out and the remaining data is classified within a reduced neural network. the results of the classifications of several data combinations are monitored. The influence of the mode in which the microwave data is used in these combinations is especially highlighted (intensity images versus coherence maps).

The coherence map is created based on the correlation of two complex SAR data sets. Indication the level of (de) correlation, this map gives information about the relation between groundcover and temporal changes. For classes that cannot be separated with optical data alone an investigation is made into the use of such (de) correlation as an additional layer.