Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion

Renbo Luo, Wenzhi Liao, Hongyan Zhang, Youguo Pi, Wilfried Philips, Ji WU, Yaqiu JIN, Jiancheng SHI

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

Hyperspectral and LiDAR data, can provide plentiful information about the objects on the Earths surface. However there are some shortages for each of them, where hyperspectral sensor is easily influenced by cloud and difficult to distinguish different objects contained same materials, LiDAR cannot discriminate different objects which are similar in altitude. Fusion of these multi-source data for reliable classification attracts increasing interests but remains challenging. In this paper, we propose a new framework to fuse multi-source data for classification. The proposed method contains three main works: 1) cloud shadows extraction; 2) feature fusion of spectral and spatial information extracted from hyperspectral image, elevation information extracted from LiDAR data; 3) decision fusion of cloud and non-cloud regions. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.
Original languageEnglish
Pages2518-2521
Number of pages4
DOIs
Publication statusPublished - 3 Nov 2016
Event2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) - Beijing, China
Duration: 10 Jul 201615 Jul 2016

Conference

Conference2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Abbreviated titleIGARSS 2016
CountryChina
CityBeijing
Period10/07/1615/07/16

Fingerprint

optical radar
lidar
fusion
fuses
Earth surface
sensors
decision
sensor
method

Keywords

  • hyperspectral image
  • LiDAR data
  • remote sensing
  • feature fusion

Cite this

Luo, R., Liao, W., Zhang, H., Pi, Y., Philips, W., WU, J., ... SHI, J. (2016). Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion. 2518-2521. Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China. https://doi.org/10.1109/IGARSS.2016.7729650
Luo, Renbo ; Liao, Wenzhi ; Zhang, Hongyan ; Pi, Youguo ; Philips, Wilfried ; WU, Ji ; JIN, Yaqiu ; SHI, Jiancheng. / Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion. Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.4 p.
@conference{4a2d50fc63a248af97e9b67eda71460c,
title = "Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion",
abstract = "Hyperspectral and LiDAR data, can provide plentiful information about the objects on the Earths surface. However there are some shortages for each of them, where hyperspectral sensor is easily influenced by cloud and difficult to distinguish different objects contained same materials, LiDAR cannot discriminate different objects which are similar in altitude. Fusion of these multi-source data for reliable classification attracts increasing interests but remains challenging. In this paper, we propose a new framework to fuse multi-source data for classification. The proposed method contains three main works: 1) cloud shadows extraction; 2) feature fusion of spectral and spatial information extracted from hyperspectral image, elevation information extracted from LiDAR data; 3) decision fusion of cloud and non-cloud regions. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.",
keywords = "hyperspectral image, LiDAR data, remote sensing, feature fusion",
author = "Renbo Luo and Wenzhi Liao and Hongyan Zhang and Youguo Pi and Wilfried Philips and Ji WU and Yaqiu JIN and Jiancheng SHI",
year = "2016",
month = "11",
day = "3",
doi = "10.1109/IGARSS.2016.7729650",
language = "English",
pages = "2518--2521",
note = "2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

}

Luo, R, Liao, W, Zhang, H, Pi, Y, Philips, W, WU, J, JIN, Y & SHI, J 2016, 'Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion' Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10/07/16 - 15/07/16, pp. 2518-2521. https://doi.org/10.1109/IGARSS.2016.7729650

Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion. / Luo, Renbo; Liao, Wenzhi; Zhang, Hongyan; Pi, Youguo; Philips, Wilfried; WU, Ji; JIN, Yaqiu; SHI, Jiancheng.

2016. 2518-2521 Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion

AU - Luo, Renbo

AU - Liao, Wenzhi

AU - Zhang, Hongyan

AU - Pi, Youguo

AU - Philips, Wilfried

AU - WU, Ji

AU - JIN, Yaqiu

AU - SHI, Jiancheng

PY - 2016/11/3

Y1 - 2016/11/3

N2 - Hyperspectral and LiDAR data, can provide plentiful information about the objects on the Earths surface. However there are some shortages for each of them, where hyperspectral sensor is easily influenced by cloud and difficult to distinguish different objects contained same materials, LiDAR cannot discriminate different objects which are similar in altitude. Fusion of these multi-source data for reliable classification attracts increasing interests but remains challenging. In this paper, we propose a new framework to fuse multi-source data for classification. The proposed method contains three main works: 1) cloud shadows extraction; 2) feature fusion of spectral and spatial information extracted from hyperspectral image, elevation information extracted from LiDAR data; 3) decision fusion of cloud and non-cloud regions. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.

AB - Hyperspectral and LiDAR data, can provide plentiful information about the objects on the Earths surface. However there are some shortages for each of them, where hyperspectral sensor is easily influenced by cloud and difficult to distinguish different objects contained same materials, LiDAR cannot discriminate different objects which are similar in altitude. Fusion of these multi-source data for reliable classification attracts increasing interests but remains challenging. In this paper, we propose a new framework to fuse multi-source data for classification. The proposed method contains three main works: 1) cloud shadows extraction; 2) feature fusion of spectral and spatial information extracted from hyperspectral image, elevation information extracted from LiDAR data; 3) decision fusion of cloud and non-cloud regions. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.

KW - hyperspectral image

KW - LiDAR data

KW - remote sensing

KW - feature fusion

UR - http://hdl.handle.net/1854/LU-7181948

U2 - 10.1109/IGARSS.2016.7729650

DO - 10.1109/IGARSS.2016.7729650

M3 - Paper

SP - 2518

EP - 2521

ER -

Luo R, Liao W, Zhang H, Pi Y, Philips W, WU J et al. Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion. 2016. Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China. https://doi.org/10.1109/IGARSS.2016.7729650