Next Article in Journal
A Change of Theme: The Role of Generalization in Thematic Mapping
Previous Article in Journal
Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data
Open AccessArticle

Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images

1
Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06560, Turkey
2
Biomedical Engineering Department, Afyon Kocatepe University, Afyon 03300, Turkey
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 370; https://doi.org/10.3390/ijgi9060370
Received: 26 March 2020 / Revised: 20 April 2020 / Accepted: 29 May 2020 / Published: 4 June 2020
(This article belongs to the Special Issue Machine Learning for High Spatial Resolution Imagery)
The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgrounds that decrease the success of object detection in remote sensing images. In this study, a model that performs weighted ensemble object detection using optimized coefficients is proposed. This model uses the outputs of three different object detection models trained on the same dataset. The model’s structure takes two or more object detection methods as its input and provides an output with an optimized coefficient-weighted ensemble. The Northwestern Polytechnical University Very High Resolution 10 (NWPU-VHR10) and Remote Sensing Object Detection (RSOD) datasets were used to measure the object detection success of the proposed model. Our experiments reveal that the proposed model improved the Mean Average Precision (mAP) performance by 0.78%–16.5% compared to stand-alone models and presents better mean average precision than other state-of-the-art methods (3.55% higher on the NWPU-VHR-10 dataset and 1.49% higher when using the RSOD dataset). View Full-Text
Keywords: aerial object detection; deep learning; remote sensing; ensemble object detection aerial object detection; deep learning; remote sensing; ensemble object detection
Show Figures

Figure 1

MDPI and ACS Style

Körez, A.; Barışçı, N.; Çetin, A.; Ergün, U. Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images. ISPRS Int. J. Geo-Inf. 2020, 9, 370.

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.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop