Very high resolution images classification by fine tuning deep convolutional neural networks

M. Iftene; Q. Liu; Y. Wang

doi:10.1117/12.2244339

29 August 2016 Very high resolution images classification by fine tuning deep convolutional neural networks

M. Iftene, Q. Liu, Y. Wang

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100332D (2016) https://doi.org/10.1117/12.2244339
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

The analysis and interpretation of satellite images generally require the realization of a classification step. For this purpose, many methods over the year have been developed with good performances. But with the explosion of VHR images availability, these methods became more difficult to use. Recently, deep neural networks emerged as a method to address the VHR images classification which is a key point in remote sensing field. This work aims to evaluate the performance of fine-tuning pretrained convolutional neural networks (CNNs) on the classification of VHR imagery. The results are promising since they show better accuracy comparing to that of CNNs as features extractor.

Citation Download Citation

M. Iftene, Q. Liu, and Y. Wang "Very high resolution images classification by fine tuning deep convolutional neural networks", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100332D (29 August 2016); https://doi.org/10.1117/12.2244339

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