Hyperspectral hierarchical representation based on density peak ranking

Bingnan Han; Jihao Yin; Aiqun Xiao; Xiaoyan Luo

doi:10.1117/1.JRS.13.016508

18 January 2019 Hyperspectral hierarchical representation based on density peak ranking

Bingnan Han, Jihao Yin, Aiqun Xiao, Xiaoyan Luo

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 1, 016508 (January 2019). https://doi.org/10.1117/1.JRS.13.016508

Abstract

An unsupervised hyperspectral hierarchical representation (HHR) framework is proposed by estimating density peak index (DPI). To accelerate our proposed algorithm, we first aggregate adjacent homogenous pixels into extended-pixels as basic initial units. Next, to rank the importance of initial units, both local density and interunit distance of each extended-pixel are taken into account to calculate the DPI. Then, a local–global merging procedure is conducted to construct the hierarchical structure. Selecting the extended-pixels with high DPI values as centers, the related adjacent extended-pixels are merged into local-regions. To generate final HHR, the local-regions are grouped globally based on their updated DPI rank. Considering the representative property of HHR, we also explore a hyperspectral classification strategy. Different from traditional segmentation-based classification methods, our classification scheme only focuses on the classification of the representative subregions, then the labels of other subregions directly refer to the results of representative subregions in the same HHR group. Thus, the computational load for classification can be significantly decreased than for pixel-wise classification. The experiments on three hyperspectral subsets show that the proposed HHR framework can yield remarkable performance in unsupervised clustering, and the classification scheme can obtain equivalent classification accuracy with distinctly limited calculation.

Citation Download Citation

Bingnan Han, Jihao Yin, Aiqun Xiao, and Xiaoyan Luo "Hyperspectral hierarchical representation based on density peak ranking," Journal of Applied Remote Sensing 13(1), 016508 (18 January 2019). https://doi.org/10.1117/1.JRS.13.016508

Received: 29 August 2018; Accepted: 19 December 2018; Published: 18 January 2019

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available