A decision-based multi-sensor classification system using thermal hyperspectral and visible data in urban area
2017
Ghasem Abdi | Farhad Samadzadegan | Peter Reinartz
Multi-sensor data fusion has become more and more popular for classification applications. The fusion of multisource remote-sensing data can provide more information about the same observed site results in a superior comprehension of the scene. In this field of study, a combination of very high-resolution data collected by a digital color camera and a new coarse resolution hyperspectral data in the long-wave infrared range for urban land-cover classification has been extensively enticed much consideration and turned into a research hot spot in image analysis and data fusion research community. In this paper, a decision-based multi-sensor classification system is proposed to completely use the advantages of both sensors to attain enhanced land-cover classification results. In this context, spectral, textural and spatial features are extracted for the proposed multilevel classification. Then, a land-cover separability preprocessing is employed to identify how the proposed method can fully utilize the sensor advantages. Next, a support vector machine is applied to classify road classes by using thermal hyperspectral image data; plants, roofs and bare soils are classified by the joint use of sensors via Dempster–Shafer classifier fusion. Finally, an object-based post-processing is employed to improve the classification results. Experiments carried out on the dataset of 2014 IEEE GRSS data fusion contest indicate the superiority of the proposed methodology for the potentialities and possibilities of the joint utilization of sensors and refine the classification outcomes when evaluated against single sensor data. Meanwhile, the obtained classification accuracy can be a competitor against the results issued by the 2014 IEEE GRSS data fusion contest.
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