Next Article in Journal
A Review of the Real-Time Monitoring of Fluid-Properties in Tubular Architectures for Industrial Applications
Next Article in Special Issue
Quantization-Mitigation-Based Trajectory Control for Euler-Lagrange Systems with Unknown Actuator Dynamics
Previous Article in Journal
Validation of a Wireless Bluetooth Photoplethysmography Sensor Used on the Earlobe for Monitoring Heart Rate Variability Features during a Stress-Inducing Mental Task in Healthy Individuals
Previous Article in Special Issue
An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control
Open AccessArticle

Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification

1
Ministry of Defense of Republic of North Macedonia, 1000 Skopje, North Macedonia
2
Faculty of Computer Science and Engineering, Saints Cyril and Methodius University, 1000 Skopje, North Macedonia
3
Department of Computer Science, University of Bari Aldo Moro, 70125 Bari, Italy
4
Department of Computer Science, American University, Washington, DC 20016, USA
5
Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia
6
Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Radmile Matejcic 2, 51000 Rijeka, Croatia
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(14), 3906; https://doi.org/10.3390/s20143906
Received: 23 April 2020 / Revised: 11 May 2020 / Accepted: 15 May 2020 / Published: 14 July 2020
(This article belongs to the Special Issue Data Analysis for Smart Sensor Systems)
Scene classification relying on images is essential in many systems and applications related to remote sensing. The scientific interest in scene classification from remotely collected images is increasing, and many datasets and algorithms are being developed. The introduction of convolutional neural networks (CNN) and other deep learning techniques contributed to vast improvements in the accuracy of image scene classification in such systems. To classify the scene from areal images, we used a two-stream deep architecture. We performed the first part of the classification, the feature extraction, using pre-trained CNN that extracts deep features of aerial images from different network layers: the average pooling layer or some of the previous convolutional layers. Next, we applied feature concatenation on extracted features from various neural networks, after dimensionality reduction was performed on enormous feature vectors. We experimented extensively with different CNN architectures, to get optimal results. Finally, we used the Support Vector Machine (SVM) for the classification of the concatenated features. The competitiveness of the examined technique was evaluated on two real-world datasets: UC Merced and WHU-RS. The obtained classification accuracies demonstrate that the considered method has competitive results compared to other cutting-edge techniques. View Full-Text
Keywords: remote sensing; convolutional neural network (CNN); feature extraction; feature fusion remote sensing; convolutional neural network (CNN); feature extraction; feature fusion
Show Figures

Figure 1

MDPI and ACS Style

Petrovska, B.; Zdravevski, E.; Lameski, P.; Corizzo, R.; Štajduhar, I.; Lerga, J. Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification. Sensors 2020, 20, 3906.

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