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Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping This publication appears in: ISPRS Journal of Photogrammetry and Remote Sensing Authors: L. Demarchi, F. Canters, C. Cariou, G. Licciardi and J. C-W Chan Volume: 87 Pages: 166-179 Publication Date: Jan. 2014
Abstract: Despite the high richness of information content provided by airborne hyperspectral data, detailed urban
land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the
issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques.
While many studies focus on methodological developments in data reduction, less attention is paid to
the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using
state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method
- the first a transformation based approach, the second a feature-selection based approach - for mapping
of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both
methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest
(RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When
used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar
to or higher than obtained with the full dataset, demonstrating the method's capability of preserving
critical spectral information, required for the classifier to successfully distinguish between the 22 urban
land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important
spectral information seems to be compromised or lost, resulting in lower accuracies for three of the
four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset.
This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land
cover in spatially and spectrally complex urban areas.
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