Iterative Filtering and Structural Features for Hyperspectral Image Classification with Limited Samples-Reference-Cited by-同舟云学术

Iterative Filtering and Structural Features for Hyperspectral Image Classification with Limited Samples

Author:

Wang Wenning¹²³,Liu Xuebin²,Mou Xuanqin¹,Sun Li³

Affiliation:

1. School of electronic and information engineering, Xi'an Jiaotong University, Xi’an, Shaanxi, China;

2. Key Laboratory of spectral imaging technology, Xi'an Institute of Optics Precision Mechanics of CAS, Xi'an, Shaanxi, China;

3. School of information science and engineering, Shandong Agricultural University, Tai'an, Shandong, China

Funder

Natural Science Foundation of China

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

Reference43 articles.

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1. Hyperspectral Image Classification Framework via Information Fusion and Bilateral Filtering;2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC );2022-10-03

2. An Improved Image Entropy Algorithm Suitable for Digital Painting Style;2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE);2020-12