Threshold-Free Attribute Profile for Classification of Hyperspectral Images

Selection of threshold values to generate nonredundant filtered images in attribute profiles (APs) is an unresolved issue. This paper presents a novel filtering approach to the construction of APs that does not require the definition of any threshold value. The proposed approach creates a max-tree (or min-tree), traverse to the first encountered leaf node using depth first traversal, and defines a leaf attribute function (LAF) to demonstrate the changes in attribute values from leaf to root node. The LAF is analyzed based on a novel criterion to automatically detect the node along the path that has a first significant difference in the attribute value. All its descendant nodes are merged to it and the process is repeated for each unvisited leaf node to create the final filtered tree which is transformed back as a filtered image. The proposed approach can incorporate maximum spatial information by applying a few filtering operations without the need to define any threshold value. This is of great importance in spectral-spatial classification applications. Moreover, since the proposed approach requires one depth first traversal to generate a filtered image, it is very efficient in terms of computation time. To show the effectiveness of the proposed method, three real hyperspectral data sets are considered and the results are compared to the state-of-the-art method considering five different attributes. The results show that the proposed method has several important advantages with respect to the existing threshold-based filtering techniques. Furthermore, the proposed method is also effective when compared with different spectral-spatial classification techniques.