Next Article in Journal
Predicting Intentions of Pedestrians from 2D Skeletal Pose Sequences with a Representation-Focused Multi-Branch Deep Learning Network
Next Article in Special Issue
Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA
Previous Article in Journal
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Previous Article in Special Issue
Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging
Article

Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures

Department of Electronic Systems, NTNU: Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(12), 330; https://doi.org/10.3390/a13120330
Received: 2 October 2020 / Revised: 6 December 2020 / Accepted: 8 December 2020 / Published: 10 December 2020
(This article belongs to the Special Issue Algorithms in Hyperspectral Data Analysis)
Hyperspectral image classification has been increasingly used in the field of remote sensing. In this study, a new clustering framework for large-scale hyperspectral image (HSI) classification is proposed. The proposed four-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Initially, multidimensional Watershed is used for pre-segmentation. Region-based hierarchical hyperspectral image segmentation is based on the construction of Binary partition trees (BPT). Each segmented region is modeled while using first-order parametric modelling, which is then followed by a region merging stage using HSI regional spectral properties in order to obtain a BPT representation. The tree is then pruned to obtain a more compact representation. In addition, principal component analysis (PCA) is utilized for HSI feature extraction, so that the extracted features are further incorporated into the BPT. Finally, an efficient variant of k-means clustering algorithm, called filtering algorithm, is deployed on the created BPT structure, producing the final cluster map. The proposed method is tested over eight publicly available hyperspectral scenes with ground truth data and it is further compared with other clustering frameworks. The extensive experimental analysis demonstrates the efficacy of the proposed method. View Full-Text
Keywords: hyperspectral image (HSI); HSI clustering; HSI segmentation; k-means; watershed; Principle Component Analysis (PCA) hyperspectral image (HSI); HSI clustering; HSI segmentation; k-means; watershed; Principle Component Analysis (PCA)
Show Figures

Figure 1

MDPI and ACS Style

Ismail, M.; Orlandić, M. Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures. Algorithms 2020, 13, 330. https://doi.org/10.3390/a13120330

AMA Style

Ismail M, Orlandić M. Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures. Algorithms. 2020; 13(12):330. https://doi.org/10.3390/a13120330

Chicago/Turabian Style

Ismail, Mohamed, and Milica Orlandić. 2020. "Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures" Algorithms 13, no. 12: 330. https://doi.org/10.3390/a13120330

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop