Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model
Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In th...
Main Authors: | Yi Wang, Haiwei Song, Yan Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2016-09-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | http://www.mdpi.com/2072-4292/8/9/748 |
Similar Items
-
Bilateral texture filtering for spectral-spatial hyperspectral image classification
by: Ying Zhang, et al.
Published: (2019-12-01) -
A Bilateral Filter Based Post-Processing Approach for Supervised Spectral-Spatial Hyperspectral Image Classification
by: Bushra Naz Soomro, et al.
Published: (2018-07-01) -
Incremental Graph Embedding Based on Spatial-Spectral Neighbors for Hyperspectral Image Classification
by: Dongqing Li, et al.
Published: (2018-01-01) -
Hyperspectral Image Classification with Localized Graph Convolutional Filtering
by: Shengliang Pu, et al.
Published: (2021-02-01) -
SuperBF: Superpixel-Based Bilateral Filtering Algorithm and Its Application in Feature Extraction of Hyperspectral Images
by: Zhikun Chen, et al.
Published: (2019-01-01)