Object-Based Morphological Profiles for Classification of Remote Sensing Imagery

• Published in: IEEE transactions on geoscience and remote sensing Vol. 54; no. 10; pp. 5952 - 5963
• Main Authors: Geiss, Christian, Klotz, Martin, Schmitt, Andreas, Taubenbock, Hannes
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Computational modeling; Image classification; Image reconstruction; Image segmentation; Land use/land cover (LULC) classification; mathematical morphology; morphological profiles (MPs); Morphology; object-based image analysis (OBIA); Partitioning; Preserves; Remote sensing; Shape; supervised classification; Topology; Transforms; very high resolution imagery
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2016.2576978

Morphological operators (MOs) and their enhancements such as morphological profiles (MPs) are subject to a lively scientific contemplation since they are found to be beneficial for, for example, classification of very high spatial resolution panchromatic, multi-, and hyperspectral imagery. They account for spatial structures with differing magnitudes and, thus, provide a comprehensive multilevel description of an image. In this paper, we introduce the concept of object-based MPs (OMPs) to also encode shape-related, topological, and hierarchical properties of image objects in an exhaustive way. Thereby, we seek to benefit from the so-called object-based image analysis framework by partitioning the original image into objects with a segmentation algorithm on multiple scales. The obtained spatial entities (i.e., objects) are used to aggregate multiple sequences obtained with MOs according to statistical measures of central tendency. This strategy is followed to simultaneously preserve and characterize shape properties of objects and enable both the topological and hierarchical decompositions of an image with respect to the progressive application of MOs. Subsequently, supervised classification models are learned by considering this additionally encoded information. Experimental results are obtained with a random forest classifier with heuristically tuned hyperparameters and a wrapper-based feature selection scheme. We evaluated the results for two test sites of panchromatic WorldView-II imagery, which was acquired over an urban environment. In this setting, the proposed OMPs allow for significant improvements with respect to classification accuracy compared to standard MPs (i.e., obtained by paired sequences of erosion, dilation, opening, closing, opening by top-hat, and closing by top-hat operations).