Object-based deep convolutional autoencoders for high-resolution remote sensing image classification

Weiwei Jiang

doi:10.1117/1.JRS.12.035002

5 July 2018 Object-based deep convolutional autoencoders for high-resolution remote sensing image classification

Weiwei Jiang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 3, 035002 (July 2018). https://doi.org/10.1117/1.JRS.12.035002

Abstract

Deep learning has been applied to many fields due to its capability for unsupervised feature learning. However, it is unsuitable for remote sensing image classification of deep convolutional networks due to the regular and fixed shape of receptive fields in current methods, which cannot freely adapt for ground objects with various shapes and scales. To tackle this problem, we propose object-based deep convolutional autoencoders (ODCAEs) to encode high-resolution remote sensing image features automatically. The receptive fields of deep convolutional autoencoders were designed based on a fractal net evolution approach to adapt for various ground objects in images, retaining category purity while providing adequate information. We collected 109,333 samples of seven classes from high-resolution satellite images over various locations to train the ODCAE, which encoded high level features automatically, coupled with a support vector machine for classification. We assessed the classification results using two WorldView-II image locations. The proposed ODCAE approach achieved higher accuracy than three manual design feature systems. Thus, the proposed ODCAE approach is useful and efficient for feature learning problems for high-resolution remote sensing images.

Citation Download Citation

Weiwei Jiang "Object-based deep convolutional autoencoders for high-resolution remote sensing image classification," Journal of Applied Remote Sensing 12(3), 035002 (5 July 2018). https://doi.org/10.1117/1.JRS.12.035002

Received: 14 August 2017; Accepted: 8 June 2018; Published: 5 July 2018

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