Application analysis and prospect of deep learning in remote sensing image classification

Xiajiong Shen; Yan Jin; Ke Zhou; Yanna Zhang

doi:10.1117/12.2538161

14 February 2020 Application analysis and prospect of deep learning in remote sensing image classification

Xiajiong Shen, Yan Jin, Ke Zhou, Yanna Zhang

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 114320A (2020) https://doi.org/10.1117/12.2538161
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Remote sensing image classification has important research significance and application value in image information extraction, ground object detection and identification, and is widely used in military reconnaissance, disaster relief, crop recognition and yield estimation and other military and civil fields. In the past few decades, scholars have done a lot of research on remote sensing image classification, and put forward multiple classification methods, which are mainly divided into supervised classification and unsupervised classification. However, with the increasement of remote sensing image resolution, traditional classification algorithms can not meet the needs for high-precision classification, and also unable to solve “the different objects with same spectrum” and “the same object with different spectrum” problem. In recent years, machine learning has made breakthroughs in image classification research. As a branch of machine learning, deep learning stands out among many machine algorithms for its applicability of learning models and accuracy of classification results. Therefore, more and more scholars apply deep learning to remote sensing image classification. In this paper, the application of deep learning in remote sensing image classification is analyzed and prospected. Firstly, the basic process of classification is summarized, and the common data sets are introduced. Secondly, frequently-used models and open source tools in application has been introduced, with the analysis of the latest application progress in rapidly developing deep learning methods. Finally, the difficulties and challenges existing in the application is discussed and the trend is prospected.

Citation Download Citation

Xiajiong Shen, Yan Jin, Ke Zhou, and Yanna Zhang "Application analysis and prospect of deep learning in remote sensing image classification", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320A (14 February 2020); https://doi.org/10.1117/12.2538161

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Remote sensing

Image classification

Image processing

Convolution

Hyperspectral imaging

Neural networks

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years