Authors:
Dimitrios Konstantinidis
1
;
Tania Stathaki
1
;
Vasileios Argyriou
2
and
Nikos Grammalidis
3
Affiliations:
1
Imperial College London, United Kingdom
;
2
Kingston Univesity London, United Kingdom
;
3
CERTH-ITI, Greece
Keyword(s):
Building Detection, Satellite Images, HOG, NDVI, FAST Algorithm, Probabilistic Fusion.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
Building segmentation from 2D images can be a very challenging task due to the variety of objects that appear in an urban environment. Many algorithms that attempt to automatically extract buildings from satellite images face serious problems and limitations. In this paper, we address some of these problems by applying a
novel approach that is based on the fusion of Histogram of Oriented Gradients (HOG), Normalized Difference Vegetation Index (NDVI) and Features from Accelerated Segment Test (FAST) features. We will demonstrate that by taking advantage of the multi-spectral nature of a satellite image and by employing a probabilistic fusion of the aforementioned features, we manage to create a novel methodology that increases the performance of a building detector compared to other state-of-the-art methods.