Deep convolutional neural networks for airport detection in remote sensing images

Date

2018-05-05

Author

Budak, Umit
Sengur, Abdulkadir
Halıcı, Uğur

Metadata

Show full item record

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

Item Usage Stats

255
views

0
downloads

This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not only ensure a tuned feature vector, but also yield better classification accuracy. The method proposed in this study first detects various regions on RSIs and then uses these candidate regions to train CNN architecture. The CNN model used has five convolution and three fully connected layers. Normalization and dropout layers were employed in order to build efficient architecture. A data augmentation strategy was used to reduce overfitting. Several experiments were performed to evaluate the performance of CNNs. Comparative work validated the efficiency of the proposed method and yielded an accuracy of 95.21%.

Subject Keywords

Airport detection, Remote sensing images, Deep convolutional neural networks

URI

https://hdl.handle.net/11511/43492

DOI

https://doi.org/10.1109/siu.2018.8404195

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Efficient Airport Detection Using Line Segment Detector and Fisher Vector Representation Budak, Umit; Halıcı, Uğur; Sengur, Abdulkadir; Karabatak, Murat; Xiao, Yang (2016-08-01) In this letter, a two-stage method for airport detection on remote sensing images is proposed. In the first stage, a new algorithm composed of several line-based processing steps is used for extraction of candidate airport regions. In the second stage, the scale-invariant feature transformation and Fisher vector coding are used for efficient representation of the airport and nonairport regions and support vector machines employed for classification. In order to evaluate the performance of the proposed metho...
Unsupervised Change Detection in Satellite Images using Oversegmentation and Mutual Information Taskesen, Bahar; Besbinar, Beril; Koz, Alper; Alatan, Abdullah Aydın (2017-05-18) In this paper, a novel solution to the problem of unsupervised change detection in bitemporal satellite images is presented. Information measures, which are well-known and commonly-used in the change detection literature, result in unsharp change maps and masks without well defined boundaries as a result of local computation. In the proposed method, mutual information with local joint distributions computed within the over-segments after image registration, radiometric correction and some preprocessing step...
Does estimated depth help object detection? Çetinkaya, Bedrettin; Akbaş, Emre; Department of Computer Engineering (2019) With the widespread use of RGB-D cameras, depth information has improved solutions of many computer vision problems including object detection. Object detection can exploit depth information and different encodings obtained from the depth map. Although previous works proved that depth information can be used to improve object detection results, this thesis investigates the effects of depth map to object detection from different aspects in detailed experiments. To clarify these effects, we examine the follow...
Automated building detection from satellite images by using shadow information as an object invariant Yüksel, Barış; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2012) Apart from classical pattern recognition techniques applied for automated building detection in satellite images, a robust building detection methodology is proposed, where self-supervision data can be automatically extracted from the image by using shadow and its direction as an invariant for building object. In this methodology; first the vegetation, water and shadow regions are detected from a given satellite image and local directional fuzzy landscapes representing the existence of building are generate...
Alignment of uncalibrated images for multi-view classification Arık, Sercan Ömer; Vural, Elif; Frossard, Pascal (2011-12-29) Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pairwise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are captured from nearby viewpoints. In settings with large transformations and viewpoint changes, alignment of im...

Citation Formats

U. Budak, A. Sengur, and U. Halıcı, “Deep convolutional neural networks for airport detection in remote sensing images,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43492.