Automatic feature extraction using data fusion in remote sensing

Stephane Houzelle; Gerard Giraudon

doi:10.1117/12.57961

30 April 1992 Automatic feature extraction using data fusion in remote sensing

Stephane Houzelle, Gerard Giraudon

Proceedings Volume 1611, Sensor Fusion IV: Control Paradigms and Data Structures; (1992) https://doi.org/10.1117/12.57961
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

This paper presents two examples of low level strategies using multisensor data fusion, one for bridge extraction, and one for urban area extraction. These extractions are made front a couple of coregistred Synthetic Aperture Radar (SAR) and SPOT images. These features are very different by their dimensions, their shape, and their radiometry. So we C1U prove the reliability of our approach on many types of features. Our method uses the notion of complementarity of each sensor, and the notion of context in the observed scene. For bridge detection, we first segment water in the SPOT image, to spatially constrain the bridge research in the SAR image. This research is achieved using a correlation method. To detect an urban area, we first use the knowledge that it produces very bright texture in SAR imagery. Thus, the main part of urban backscatters is extracted using an adaptative thresholding which keeps the upper band of the gray level histogram of the SAR image. This mask is then used for classification as a training set using a distance map of urban area texture in SPOT image. We determine the non urban zone training set using a distance map of the urban training zone boundaries. Classification is performed with multivariate Gaussian classifier. The results we obtained are very encouracting, especially if we consider the robustness of the bridge detection method.

Citation Download Citation

Stephane Houzelle and Gerard Giraudon "Automatic feature extraction using data fusion in remote sensing", Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); https://doi.org/10.1117/12.57961

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