.png){kind=icon}
.png){kind=icon}
Return
Spectral-Spatial Hyperspectral Classification via Structural-Kernel Collaborative Representation
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2020.2988124.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This letter introduces a novel spatial-spectral classification method for hyperspectral images (HSIs) based on a structural-kernel collaborative representation (SKCR), which considers one weak assumption of spatial neighborhood that of the pixels in a superpixel belong to the same class when exploiting contextual information in HSI. The proposed method consists of the following steps. First, a superpixel segmentation strategy is used to construct self-adaptive regions for the HSI. Then, the structural information within each superpixel block is extracted based on the density peak and K nearest neighbors. Next, dual kernels are separately utilized for the exploitation of the spectral and the spatial information. Finally, the dual kernels are combined and incorporated into a support-vector-machine classifier. Since the weak assumption of spatial neighborhood is well considered in the collaborative representation, the proposed method showed excellent classification performance for two widely used real hyperspectral data sets even when the number of training samples was relatively small.
Keywords:
Density peak (DP)
dual kernel
hyperspectral image (HSI)
spectral-spatial classification
superpixel
support vector machines (SVMs)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K