Behnood Rasti, Pedram Ghamisi, and Richard Gloaguen

Mineralogical maps.

Mineralogical maps obtained by different techniques.

We propose a new fusion-based classification technique for optical multi-source remote sensing images called OptFus. OptFus is developed to merge and process optical imagery having different spatial and spectral resolutions. The spatial features are extracted using morphological filters from the RGB data containing high spatial resolution. A feature fusion technique is developed to combine all the sensor data in a subspace using a common set of representative features. Finally, the fused features are classified using a Support Vector Machine to ensure a robust supervised spectral classification. The proposed method is designed to allocate varying weights to the data from various imaging sensors in the fusion process. OptFus is applied to two multi-source optical datasets captured from geological drill-core samples. The classification accuracy demonstrates considerable improvements compared to the state-of-the-art. A Matlab implementation of OptFus is available online.

IEEE Geoscience and Remote Sensing Letters, 2021-12-03.

View paper
IARAI Authors
Dr. Pedram Ghamisi
Remote Sensing
Data Fusion, Feature Extraction, Hyperspectral Image, Image Classification, Mineral Exploration, Optical Sensors, Support Vector Machines


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