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RSS FeedsRemote Sensing, Vol. 11, Pages 1435: The Comparison of Fusion Methods for HSRRSI Considering the Effectiveness of Land Cover (Features) Object Recognition Based on Deep Learning (Remote Sensing)

 
 

18 june 2019 01:00:34

 
Remote Sensing, Vol. 11, Pages 1435: The Comparison of Fusion Methods for HSRRSI Considering the Effectiveness of Land Cover (Features) Object Recognition Based on Deep Learning (Remote Sensing)
 




The efficient and accurate application of deep learning in the remote sensing field largely depends on the pre-processing technology of remote sensing images. Particularly, image fusion is the essential way to achieve the complementarity of the panchromatic band and multispectral bands in high spatial resolution remote sensing images. In this paper, we not only pay attention to the visual effect of fused images, but also focus on the subsequent application effectiveness of information extraction and feature recognition based on fused images. Based on the WorldView-3 images of Tongzhou District of Beijing, we apply the fusion results to conduct the experiments of object recognition of typical urban features based on deep learning. Furthermore, we perform a quantitative analysis for the existing pixel-based mainstream fusion methods of IHS (Intensity-Hue Saturation), PCS (Principal Component Substitution), GS (Gram Schmidt), ELS (Ehlers), HPF (High-Pass Filtering), and HCS (Hyper spherical Color Space) from the perspectives of spectrum, geometric features, and recognition accuracy. The results show that there are apparent differences in visual effect and quantitative index among different fusion methods, and the PCS fusion method has the most satisfying comprehensive effectiveness in the object recognition of land cover (features) based on deep learning.


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21 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 1436: Comparison of Radar-Based Hail Detection Using Single- and Dual-Polarization (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1439: Special Issue on Selected Papers from the `International Symposium on Remote Sensing 2018` (Remote Sensing)
 
 
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