Scene classification based on the bag-of-visual-words and Doc2Vec models for high-spatial resolution remote-sensing imagery

Wenqiang Li; Gui Jin; Yin Dong

doi:10.1117/1.JRS.13.026506

20 May 2019 Scene classification based on the bag-of-visual-words and Doc2Vec models for high-spatial resolution remote-sensing imagery

Wenqiang Li, Gui Jin, Yin Dong

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 2, 026506 (May 2019). https://doi.org/10.1117/1.JRS.13.026506

Abstract

A probabilistic topic model (PTM) combined with the bag-of-visual-words model is a common method to bridge the so-called “semantic gap” problem in remote-sensing image classification research. Owing to the inherent shortcomings of PTMs, such as time consumption and failures to consider a spatial arrangement of various objects, we introduce a natural language processing document-to-vector (Doc2Vec) model, to capture the high-level semantic information of the images, instead of a PTM. The model characterizes words and documents as dense, low-dimensional vectors and implements a simplified, shallow neural network to train a language model and word vectors. It is expected to mine semantic information of remote-sensing images from a new perspective. We also improve the low-level feature quality by using feature-specific sampling methods. Two high-spatial resolution remote-sensing image datasets, UC Merced and RSSCN7, are employed to conduct a scene classification experiment to discuss the performance of the Doc2Vec model. The experimental results show that the Doc2Vec model is highly efficient in mining semantic information of the images, compared with the state-of-the-art methods.

Citation Download Citation

Wenqiang Li, Gui Jin, and Yin Dong "Scene classification based on the bag-of-visual-words and Doc2Vec models for high-spatial resolution remote-sensing imagery," Journal of Applied Remote Sensing 13(2), 026506 (20 May 2019). https://doi.org/10.1117/1.JRS.13.026506

Received: 5 January 2019; Accepted: 29 April 2019; Published: 20 May 2019

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