Identification of duplicate parts of hyper spectral images based on fuzzy by dimensionality reduction techniques-Reference-Cited by-同舟云学术

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Identification of duplicate parts of hyper spectral images based on fuzzy by dimensionality reduction techniques

Published:2023-06-12 Issue: Volume: Page:
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Pattem Sampurnima,Thatavarti Satish

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-023-08574-2.pdf

Reference33 articles.

1. Azimpour P, Shad R, Ghaemi M et al (2020) Hyper spectral image clustering with Albedo recovery fuzzy C-Means. Int J Rem Sens 41:6117–6134

2. Bo H, Bei Z, Yimeng S (2018) Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Rem Sens Environ 214:73–86

3. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2012) An ensemble of filters and classifiers for microarray data classification. Pattern Recognit 45:531–539

4. Bolon-Canedo V, Alonso-Betanzos A (2019) Ensembles for feature selection: a review and future trends. Inf Fusion 52:1–12. https://doi.org/10.1016/j.inffus.2018.11.008

5. Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020) Benchmark for filter methods for feature selection in high-dimensional classification data. Comput Stat Data Anal 143:106839. https://doi.org/10.1016/j.csda.2019

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3. An Analysis of Partition Tree Clustering Techniques for Automated Classification of Hyper Spectral Scans;2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC);2023-12-19

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