Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques

Date

2021-12-15

Author

Karadal, Can Haktan
Kaya, Muhammed Çağrı
Tuncer, Turker
Dogan, Sengul
Acharya, U. Rajendra

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

370
views

0
downloads

Automated classification of remote sensing images is one of the complex issues in robotics and machine learning fields. Many models have been proposed for remote sensing image classification (RSIC) to obtain high classification performance. The objective of this study are twofold. First, to create a new space object image collection as such a dataset is not currently available. Second, propose a novel RSIC model to yield highest classification performance using our newly created dataset. Our presented automated classification model consists of multilevel deep feature generation, iterative feature selection, and classification steps. The features are extracted from the images using pre-trained MobileNetV2 and discrete wavelet transform (DWT) methods. The combination of DWT and MobileNetV2 generates large number of features. Then, iterative neighborhood component analysis (INCA) is used to select the best features. Finally, selected features are fed to support vector machine (SVM) for automated classification. The presented model is validated using two RSIC datasets: UC-Merced, and newly created space object images (publicly available at: http://web.firat.edu.tr/turkertuncer/space_object.rar). The developed model has obtained an accuracy of 98.10% and 95.95% using UC-Merced, and newly generated space object image datasets, respectively with 10-fold cross-validation strategy. It can be concluded from the results that, the presented RSIC model is accurate and ready for real-world applications.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85111806152&origin=inward
https://hdl.handle.net/11511/91604

Journal

Expert Systems with Applications

DOI

https://doi.org/10.1016/j.eswa.2021.115659

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Computational representation of protein sequences for homology detection and classification Oğul, Hasan; Mumcuoğlu, Ünal Erkan; Department of Information Systems (2006) Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by...
Stochastic analysis and adaptive control studies in legged systems Er, Güner Dilşad; Ankaralı, Mustafa Mert; Saranlı, Uluç; Department of Electrical and Electronics Engineering (2022-6-7) Underactuated legged robots depict highly nonlinear and complex dynamical behaviors that create significant challenges in accurately modeling system dynamics using both first principles and system identification approaches. Hence, the design of stabilizing controllers becomes more challenging due to inaccurate modeling. Suppose physical parameters on mathematical models have miscalibrations due to uncertainty in identifying and modeling processes. In that case, designed controllers could perform poorly or e...
Distributed adaptive output feedback control design and application to a formation flight experiment Kutay, Ali Türker; Calise, Anthony J.; D'Andrea, Raffaello (2005-01-01) An approach for augmenting existing distributed controller designs for large-scale interconnected systems with neural network based adaptive elements is proposed. It is assumed that the controllers are interconnected in the same way as the plant and based on the available measurements, a single hidden layer neural network is introduced for each subsystem to partially cancel the effects of the sub-system interconnections and modeling errors on tracking performance. Boundedness of error signals is shown throu...
Implementation of Robot Formation Control and Navigation Using Real Time Panel Method Merheb, Abdel Razzak; Atas, Yunus; Gazi, Veysel; Sezer Uzol, Nilay (null; 2010-10-22)
Multi objective conceptual design optimization of an agricultural aerial robot (AAR) Özdemir, Segah; Tekinalp, Ozan; Department of Aerospace Engineering (2005) Multiple Cooling Multi Objective Simulated Annealing algorithm has been combined with a conceptual design code written by the author to carry out a multi objective design optimization of an Agricultural Aerial Robot. Both the single and the multi objective optimization problems are solved. The performance figures of merits for different aircraft configurations are compared. In this thesis the potential of optimization as a powerful design tool to the aerospace problems is demonstrated.

Citation Formats

C. H. Karadal, M. Ç. Kaya, T. Tuncer, S. Dogan, and U. R. Acharya, “Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques,” Expert Systems with Applications, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85111806152&origin=inward.