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Unsupervised segmentation using the Attribute Maximization algorithm on the Alpha-Tree data structure. The result is produced using a measure of compactness and constraining the range of alpha-connected components with size and dissimilarity range.
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Alpha-Tree Segmentation
The exercise demonstrates the Non-Target Clustering method of the Alpha-Tree algorithm for anomaly detection. In this case, since airplanes differ substantially in shape and size between them, intead of trying to find a robust rule to segment them, we cluster the rather homogeneous background and remove clusters of odd shapes (like roads, buildings, etc.) that we are certain are not airplanes. Each anomaly or 'hole' in the clusterd background coincides with a single plane.
Refer to the Alpha-Tree Technical Report for more
Finding boats in VHR pan-sharp satellite/aerial imagery
using the alpha-tree non-target clustering algorithm. The method has been used for detecting and tracking maritime vessels with no beacon or other radio signatures. Can be utilized in salvage and rescue operations, monitoring illegal trafficking & maritime traffic management
SAR image processing
finding water, maritime vessels, built-up areas and computing the built-up confidence index; the warmer the colors are the higher the possibility the area is built.
Unsupervised satellite image segentation for building footprint extraction. The method used is the Attribute Maximization segmentation strategy for the Alpha-Tree algorithm with structural compactness selected as the maximization attribute. The tree search space is further constrained using size thresholds to mininmize the bias of small or very large connected components.
Location: Ankara, Turkey Geography: 39°59'15.0"N, 32°51'00.6"E Source imagery: DigitalGlobe
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Unsupervised building footprint extraction using Alpha-Tree Differential Attribute Profiles. The method makes use of the latest, state of the art hierarchical image representation data-structure, the Alpha-Tree. A tree polychotomy scheme know as “attribute zoning” is computed from which Differential Attribute Profile (DAP) vector fields can be accessed. The Alpha-Trees are computed from vector data (multi-spectral imagery) thus the entries of the new DAPs contain dissimilarity values that optimize material separation and lead to accurate and automatic segmentation based on size, shape and radiometry. The method employs additional layers such as the unsupervised LULC and unsupervised built-up extent, for false positive reduction and self-supervised learning. The method concludes in < 3min for typical WorldView-2 multi-spectral data-sets of approximate coverage of 120km x 20km @ 2m spatial resolution. This does not include target contour refinement and vectorization. The image shows a blend in of the original rgb layer and the segmentation result. Object intensity (gray-scale) indicates confidence [0%-100%] that the segmented object is a building. The scene shows the south sector of the city of Kano, Nigeria.Â
Alpha-Tree 3D pattern spectra: high energy feature space regions coincide to built-up (housing) as seen in satellite imagery. Axes: size, structural compactness and radiometric dissimilarity variance.
Article: http://link.springer.com/chapter/10.1007%2F978-3-642-21569-8_10#page-1
