Alternating Sequential Filters with Morphological Attribute Operators for the Analysis of Remote Sensing Images

In this paper we propose Alternating Sequential Attribute Filters, which are Alternating Sequential Filters (ASFs) computed with Attribute Filters. ASFs are obtained by the iterative subsequent application of morphological opening and closing transformations and process an image by filtering both bright and dark structures. ASFs are widely used for achieving a simplification of a scene and for the removal of noisy structures. However, ASFs are not suitable for the analysis of very high geometrical resolution remote sensing images since they do not preserve the geometrical characteristics of the objects in the image. For this reason, instead of the conventional morphological operators, we propose to use attribute filters, which are morphological connected filters and process an image only by merging flat regions. Thus, they are suitable for the analysis of very high resolution images. Since the attribute selected for use in the analysis mainly defines the effects obtained by the morphological filter, when applying attribute filters in an alternate composition (as the ASF) it is possible to obtain a different image simplification according to the attribute considered. For example, if one considers the area as attribute, an input image will be processed by progressively removing both larger dark and bright areas. When using an attribute that measures the homogeneity of the regions (e.g., the standard deviation of the values of the pixels) the scene can be simplified by merging progressively more homogeneous zones. Moreover, the computation of the ASF with attribute filters can be performed with a reduced computational load by taking advantage of the efficient representation of the image as min- and max-tree. The proposed alternating sequential attribute filters are qualitatively evaluated on a panchromatic GeoEye-1 image.