Next Article in Journal
Urban Area Extraction by Regional and Line Segment Feature Fusion and Urban Morphology Analysis
Next Article in Special Issue
Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification
Previous Article in Journal
Regression Kriging for Improving Crop Height Models Fusing Ultra-Sonic Sensing with UAV Imagery
Previous Article in Special Issue
Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(7), 666; https://doi.org/10.3390/rs9070666

An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lizhe Wang, Liping Di, Qian Du, Peng Liu and Prasad S. Thenkabail
Received: 30 April 2017 / Revised: 12 June 2017 / Accepted: 23 June 2017 / Published: 28 June 2017
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
Full-Text   |   PDF [5985 KB, uploaded 30 June 2017]   |  

Abstract

Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a significant and challenging problem when further analyzing object-related information for civil and engineering applications. However, the computational efficiency and the separate region generation and localization steps are two big obstacles for the performance improvement of the traditional convolutional neural network (CNN)-based object detection methods. Although recent object detection methods based on CNN can extract features automatically, these methods still separate the feature extraction and detection stages, resulting in high time consumption and low efficiency. As a significant influencing factor, the acquisition of a large quantity of manually annotated samples for HSR remote sensing imagery objects requires expert experience, which is expensive and unreliable. Despite the progress made in natural image object detection fields, the complex object distribution makes it difficult to directly deal with the HSR remote sensing imagery object detection task. To solve the above problems, a highly efficient and robust integrated geospatial object detection framework based on faster region-based convolutional neural network (Faster R-CNN) is proposed in this paper. The proposed method realizes the integrated procedure by sharing features between the region proposal generation stage and the object detection stage. In addition, a pre-training mechanism is utilized to improve the efficiency of the multi-class geospatial object detection by transfer learning from the natural imagery domain to the HSR remote sensing imagery domain. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset were conducted to evaluate the proposed method. View Full-Text
Keywords: geospatial object detection; high spatial resolution (HSR) remote sensing imagery; integration; pre-training mechanism; feature sharing geospatial object detection; high spatial resolution (HSR) remote sensing imagery; integration; pre-training mechanism; feature sharing
Figures

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Han, X.; Zhong, Y.; Zhang, L. An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery. Remote Sens. 2017, 9, 666.

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

1

Comments

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