Superpixel-based and Spatially-regularized Diffusion Learning for
  Unsupervised Hyperspectral Image Clustering
Kangning Cui Ruoning Li Sam L. Polk Yinyi Lin Hongsheng Zhang James M. Murphy Robert J. Plemmons Raymond H. Chan
Hyperspectral images (HSIs) provide exceptional spatial and spectral
resolution of a scene, crucial for various remote sensing applications.
However, the high dimensionality, presence of noise and outliers, and the need
for precise labels of HSIs present significant challenges to HSIs analysis,
motivating the development of performant HSI clustering algorithms. This paper
introduces a novel unsupervised HSI clustering algorithm, Superpixel-based and
Spatially-regularized Diffusion Learning (S2DL), which addresses these
challenges by incorporating rich spatial information encoded in HSIs into
diffusion geometry-based clustering. S2DL employs the Entropy Rate Superpixel
(ERS) segmentation technique to partition an image into superpixels, then
constructs a spatially-regularized diffusion graph using the most
representative high-density pixels. This approach reduces computational burden
while preserving accuracy. Cluster modes, serving as exemplars for underlying
cluster structure, are identified as the highest-density pixels farthest in
diffusion distance from other highest-density pixels. These modes guide the
labeling of the remaining representative pixels from ERS superpixels. Finally,
majority voting is applied to the labels assigned within each superpixel to
propagate labels to the rest of the image. This spatial-spectral approach
simultaneously simplifies graph construction, reduces computational cost, and
improves clustering performance. S2DL's performance is illustrated with
extensive experiments on three publicly available, real-world HSIs: Indian
Pines, Salinas, and Salinas A. Additionally, we apply S2DL to landscape-scale,
unsupervised mangrove species mapping in the Mai Po Nature Reserve, Hong Kong,
using a Gaofen-5 HSI. The success of S2DL in these diverse numerical
experiments indicates its efficacy on a wide range of important unsupervised
remote sensing analysis tasks.