Catherine Mering

Radar Image Analysis Using Morphological Filters

The speckle noise is one severe obstacle for automatic mapping from radar images. For geological mapping, the connectivity of structures has to be restored. New filters for speckle reduction are presented here and compared to already known ones according to an index of connectivity.

Speckle reduction: filtering related to spatial resolution

We have experimented on the effects on SAR ERS1 scenes of two classes of filters designed for speckle reduction according to the size of the objects to be extracted. The valuation of both classes of filters is done by two criteria: homogeneity and connectivity. The first class of filters is devoted to the extraction of thin features. In this case, the various filters operate within an elementary neighborhood. The filters belonging to the second class enable the extraction of larger elements. These filters are alternated sequential filters obtained by composition of morphological filters using structuring elements of increasing size. The comparison of the results provided by these filters, as well as the parameters quantifying homogeneity and connectivity, will allow the choice of the more accurate filter in order to ensure the extraction of features of various sizes on SAR-ERS1 images.

Filtering of radar images for geological structural mapping

Geological structural features may be described on satellite images as thin lines or contours. The extraction of these features is usually performed by enhancement techniques such as filtering and edge detection. However, radar images being characterized by the presence of speckle noise, the classical enhancement techniques do not provide good results in this case. We present here two methods of filtering based on local transformations of the gray-tone function. They provide gray-tone images where the speckle is removed and the continuity and sharpness of thin structures are preserved. The valuation of the methods is done by analyzing the binary images resulting from an automatic thresholding of the filtered images according to two criteria: homogeneity and connectivity.

GRAY TONE FILTERING ON ERS-SAR IMAGES APPLIED TO CHANGE DETECTION AND MAPPING

In SAR images, the pixel values are tightly related to physical parameters of the soil such as topography, roughness and humidity, regardless to atmospheric conditions. Therefore, SAR images may be used to detect, and quantify changes in land cover, by comparison of time series SAR data. Classical change detection techniques from SAR images are based on additive synthesis of RGB colors and arithmetic operations between images. The noisy aspect of ERS image due to the original speckle is an obstacle for available mapping and quantification of the changes. Here, statistical and morphological filters are used to reduce the speckle noise. Combined techniques of change detection and noise filtering are applied here to assess and map from ERS-SAR images the impact of regular or catastrophic flood and deforestation in the East Coast of Madagascar.

Use of macro texture for landscape units mapping from satellite images: application to vegetation-density mapping in arid and semi-arid areas

The definition of macro-texture proposed here comes from granulometric analysis which is a way to describe statistically the size of the connected components in a binary. The pixels of the binary image are classified according to the quantitative descriptors provided by this analysis. The whole procedure is applied here to the mapping of the density of the vegetation cover in arid lands from SPOT and thematic mapper.