Y. SMARA, A. BELHADJ-AISSA & B. SANSAL

Image Processing Laboratory, Electronic Institute, U.S.T.H.B, BP 32, Eli-Alia Bab-Ezzouar, 16111 Algiers, Algeria

J. LICHTENEGGER

European Space Agency / Earthnet, E.S.R.I.N., Via Galileo Galilei, 00044 Frascati, Itlay

A. BOUZENOUNE

Vegetal Biology Laboratory, Natural Sciences Institute, U.S.T.H.B., BP 32,. Eli-Alia Bab-Ezzouar, 16111 Algiers, Algeria

ABSTRACT:

This paper overviews results obtained by multisource and multidate optical data analysis and be comparing ERS-1 and Landsat TM data due to aspects of radar image enhancement techniques and restitution of roughness of different types of vegetation in steppic regions. In effect, image data integration has become a valuable approach to integrate multisource satellite data. It has been found that image data from different spectral domains (visible, near infrared, microwave) provides data sets with complementarity information content and can be used to improve the spatial resolution of satellite images. In this communication, we present a part of the cooperation research project which deals with fusing ERS-1 SAR geocoded images with Landsat TM, investigating different combinations of integration and classification techniques. The methodology consists of several steps which are:

- Comparative performance of different filtering algorithms for the purpose of reducing Speckle noise. Several filtering algorithms (median and separable median, edge preserving smoothing filter, Lee sigma, modified Lee filter and Kuan filter) were implemented and tested with different window sizes, iterations and parameters.

- Geometric superposition and geocoding of optical images regarding GEC type SAR ERS-1 image available and resampling at unique resolution of 25m.

- Application of different numerical combinations of integration techniques and unsupervised classifications such as the Forgy method, the MacQueen method and other methods.

The results are compared with vegetal cover mapping from aerial photographs of the region of Foum Redad in the south of the saharian Atlas. The combinations proposed above allow us in a colour composite image to distinguish different themes which exist in the image such as low and high steppic vegetation, trees, bare soil and mountainous zones.

The multisource classification seem to be more efficient when the speckle is well filtered and allows us to distinguish the different themes existing in the arid and semi-arid regions in the south of the saharian Atlas and shows a good correlation between different types of land cover and land use and radar backscattering level in the SAR data which corresponds essentially to the roughness of the soil surface.

Keywords: Speckle filtering, unsupervised classification, Data fusion, Geocoding, Co-registration, semi arid areas, Image processing, steppic areas, IHS transform.

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry