Prof. Rubeena Vohra Profile

Prof. Rubeena Vohra

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Proceedings Article | 30 April 2016 Paper

Multisensor multiresolution data fusion for improvement in classification

Rubeena V., K. Tiwari

Proceedings Volume 9880, 98800X (2016) https://doi.org/10.1117/12.2222164

KEYWORDS: Buildings, Data fusion, Remote sensing, Vegetation, Long wavelength infrared, RGB color model, Information fusion, Roads, Image processing, Spatial resolution, Feature extraction, Image fusion, Image classification, Principal component analysis, Hyperspectral imaging

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The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification.

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