Spectral-Spatial Response for Hyperspectral Image Classiﬁcation
AbstractThis paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboringregions. SSRformsadeeparchitectureandisabletolearndiscriminativespectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special scenarios within a single uniﬁed framework. Based on SSR, we further propose the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classiﬁcation. In SLN, the joint spectral and spatial features are learned using templates simply learned by Marginal Fisher Analysis (MFA) and Principal Component Analysis (PCA). An important contribution to the success of SLN is the exploitation of label information of training samples and the local spatial structure of HSI. Extensive experimental results on four challenging HSI datasets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reﬂective Optics System Imaging Spectrometer (ROSIS) airborne sensors show the implementational simplicity of SLN and verify the superiority of SSR for HSI classiﬁcation. View Full-Text
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Wei, Y.; Zhou, Y.; Li, H. Spectral-Spatial Response for Hyperspectral Image Classiﬁcation. Remote Sens. 2017, 9, 203.
Wei Y, Zhou Y, Li H. Spectral-Spatial Response for Hyperspectral Image Classiﬁcation. Remote Sensing. 2017; 9(3):203.Chicago/Turabian Style
Wei, Yantao; Zhou, Yicong; Li, Hong. 2017. "Spectral-Spatial Response for Hyperspectral Image Classiﬁcation." Remote Sens. 9, no. 3: 203.
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