In:
Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 87, No. 6 ( 2021-06-01), p. 445-455
Abstract:
Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N 2 ). We pres- ent in this article an incremental manifold
learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support
vector machine.
Type of Medium:
Online Resource
ISSN:
0099-1112
DOI:
10.14358/PERS.87.7.445
Language:
English
Publisher:
American Society for Photogrammetry and Remote Sensing
Publication Date:
2021
detail.hit.zdb_id:
188870-5
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