Abstract
Recently, representation-based subspace clustering algorithms for hyperspectral images (HSIs) have been developed with the assumption that pixels belonging to the same land-cover class lie in the same subspace. Polarization is regarded to be a complement to spectral information, but related research only focus on the clustering for HSIs without considering polarization, and cannot effectively process large-scale hyperspectral datasets. In this paper, we propose an efficient representation-based subspace clustering framework for polarized hyperspectral images (PHSIs). Combining with spectral information and polarized information, this framework is extensible for most existing representation-based subspace clustering algorithms. In addition, with a sampling-clustering-classification strategy which firstly clusters selected in-sample data into several classes and then matches the out-of-sample data into these classes by collaborative representation-based classification, the proposed framework significantly reduces the computational complexity of clustering algorithms for PHSIs. Some experiments were carried out to demonstrate the accuracy, efficiency and potential capabilities of the algorithms under the proposed framework.
Highlights
Polarization, which can describe surface roughness and edge properties of objects and increase the contrast between objects and background, has been demonstrated over recent decades to provide useful information for atmosphere monitoring, land surface characterizing and material classification [1,2,3,4].In addition, hyperspectral images (HSIs) consist of high-resolution spectral correlation and rich spatial information that support land-cover classification and clustering [5,6,7,8]
As a combination of HSIs and polarization, polarized hyperspectral images (PHSIs) that provide the multidimensional information of polarization, spectral, spatial and radiant features are expected to possess great potential in object detection and clustering tasks
The window edges are composed of metal, the surface is a layer of white paint, so it belongs to the same cluster as the white painted wall
Summary
Polarization, which can describe surface roughness and edge properties of objects and increase the contrast between objects and background, has been demonstrated over recent decades to provide useful information for atmosphere monitoring, land surface characterizing and material classification [1,2,3,4]. Of target lies in the same subspace, In this paper, a clustering framework for a representation-based clustering the some clustering algorithms for HSIs were proposed to make full usemodel of thecombining spectral-spatial polarization and spectral properties of targets is presented. In order to reduce the computational complexity, a new sampling-clusteringEspecially for HSIs and PHSIs, which are usually accompanied by both high dimensionality and large classification strategy is limits adopted the proposed framework. This sampling-clustering-classification scale, this shortcoming theinapplication of those algorithms. A clustering framework representation-based clustering model combining clusters the selected in-sample data with proposed polarized hyperspectral clustering method. Subspaces and (2) calculating the cluster membership of the dataset using statistical methods or
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