Collaborative classification of hyperspectral and visible images with convolutional neural network

Mengmeng Zhang; Wei Li; Qian Du

doi:10.1117/1.JRS.11.042607

1 September 2017 Collaborative classification of hyperspectral and visible images with convolutional neural network

Mengmeng Zhang, Wei Li, Qian Du

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 042607 (September 2017). https://doi.org/10.1117/1.JRS.11.042607

Abstract

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

Citation Download Citation

Mengmeng Zhang, Wei Li, and Qian Du "Collaborative classification of hyperspectral and visible images with convolutional neural network," Journal of Applied Remote Sensing 11(4), 042607 (1 September 2017). https://doi.org/10.1117/1.JRS.11.042607

Received: 21 April 2017; Accepted: 3 August 2017; Published: 1 September 2017

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