Next Article in Journal
Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification
Previous Article in Journal
Remote Sensing of Deformation of a High Concrete-Faced Rockfill Dam Using InSAR: A Study of the Shuibuya Dam, China
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2016, 8(3), 259;

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

1,2,* , 2,3
School of Electronic Information, Wuhan University, Wuhan 430072, China
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan 430079, China
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
CNRS LTCI, Telecom ParisTech, Paris 75013, France
Author to whom correspondence should be addressed.
Academic Editors: Raad A. Saleh, Richard Gloaguen and Prasad S. Thenkabail
Received: 16 November 2015 / Revised: 11 March 2016 / Accepted: 14 March 2016 / Published: 17 March 2016
Full-Text   |   PDF [9315 KB, uploaded 17 March 2016]   |  


Scene classification plays an important role in understanding high-resolution satellite (HRS) remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT), which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms. View Full-Text
Keywords: feature descriptor; feature extraction; object categorization; scene classification; binary partition tree feature descriptor; feature extraction; object categorization; scene classification; binary partition tree

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Yu, H.; Yang, W.; Xia, G.-S.; Liu, G. A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification. Remote Sens. 2016, 8, 259.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top