Abstract
As adaptive neuro-fuzzy inference systems (ANFISs) have shown a high capability in solving various complicated problems, their usage have been increased so far. ANFISs are able to deal with large amounts of data with linear or nonlinear relations. In this paper, an ANFIS-based methodology was proposed in order to detect objects in complex city scenes using digital aerial images and LiDAR data. In this regard, four features were generated and normalized as ANFIS inputs including Green band, normalized difference vegetation index (NDVI), and normalized digital surface model (nDSM) using two different algorithms via morphological operations. Two types of objects were detected including buildings and trees. The proposed ANFIS used three different algorithms to build its fuzzy inference system structure including grid partition, subtractive clustering, and fuzzy c-means clustering. The results were evaluated on three different test areas provided by the fourth working group of the third commission (WG III/4) of international society of photogrammetry and remote sensing (ISPRS), over Vaihingen in Germany, known as Areas 1, 2, and 3. The achieved results were compared with each other, as well as with ISPRS WG III/4 participants’ results, by considering Completeness, Correctness, Quality, and RMS indices per-area and per-object levels. The achieved results demonstrated the capability of the proposed ANFIS in detecting buildings and trees in complex city scenes in comparison with other methods. In building detection, the proposed ANFIS-based methods achieved Completeness of 100 % in all three test areas for buildings larger than 50 m2. Also in both tree detection and building detection, the proposed ANFIS-based methods achieved Completeness and Correctness values larger than 90 %, by considering objects larger than 50 m2.
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Acknowledgments
The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) (Cramer 2010)http://www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg.html (in German).
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Siamak Talebi Nahr holds a PH.D., Tafresh University.
Parham Pahlavani holds a PH.D., University of Tehran.
Hamed Amini Amirkalayi holds a PH.D., Tafresh University.
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Talebi Nahr, S., Pahlavani, P. & Amini Amirkalayi, H. A Comparative Study of Adaptive Neuro-Fuzzy Inference Systems in Object Detection of Complex City Scenes Using Digital Aerial Images and LiDAR Data. J Indian Soc Remote Sens 43, 787–799 (2015). https://doi.org/10.1007/s12524-015-0457-1
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DOI: https://doi.org/10.1007/s12524-015-0457-1