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Improving K-Nearest Neighbor Approaches for Density-Based Pixel Clustering in Hyperspectral Remote Sensing Images
| Content Provider | MDPI |
|---|---|
| Author | Cariou, Claude Moan, Steven Le Chehdi, Kacem |
| Copyright Year | 2020 |
| Description | We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local density and to propagate labels through algorithm-specific labeling decisions. We first improved two of these techniques, a KNN variant of the density peaks clustering method dpc, and a weighted-mode variant of knnclust, so the four methods use the same input KNN graph and only differ by their labeling rules. We propose two regularization schemes for hyperspectral image analysis: (i) a graph regularization based on mutual nearest neighbors (MNN) prior to clustering to improve cluster discovery in high dimensions; (ii) a spatial regularization to account for correlation between neighboring pixels. We demonstrate the relevance of the proposed methods on synthetic data and hyperspectral images, and show they achieve superior overall performances in most cases, outperforming the state-of-the-art methods by up to 20% in kappa index on real hyperspectral images. |
| Starting Page | 3745 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs12223745 |
| Journal | Remote Sensing |
| Issue Number | 22 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-11-14 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Clustering Methods Density Estimation Nearest Neighbor Search Deterministic Algorithm Unsupervised Learning |
| Content Type | Text |
| Resource Type | Article |
