Geospatial object recognition using deep networks for satellite images

Download

index.pdf

Date

2018

Author

Barut, Onur

Metadata

Show full item record

Item Usage Stats

453
views

201
downloads

Deep learning paradigm has been drawing significant interest during the last decade due to the recent developments in novel machine learning algorithms and improvements in computational hardware. Satellite image analysis is also an important scientific area with many objectives, such as disaster and crisis management, forest cover, road mapping, city planning, even military purposes. Spatial correlations of land cover or geospatial objects between different images lead to widely utilization of convolutional neural networks (CNN) in the literature for detection and classification of land cover and geospatial objects with manmade structures. Noting the increase in the availability of high resolution satellite images due to the existence of many new satellites up in the orbit, data to be used during train of such networks is also more reachable compared to the past. In this study, three main research directions for satellite image analysis is examined and tested through simulations. Land cover classification of image patches are first achieved by using conventional CNNs. Next, the segmentation of natural scenes into different land cover is obtained by deep networks that are capable of providing segment labels for every pixel on the image. Finally, detection of geospatial objects on satellite images is obtained by conventional object detection techniques based on deep networks. For all these purposes, a multispectral satellite image dataset is manually labeled for several geospatial and human-made objects. The effect of different architectures, training techniques and the training parameters are examined through simulations.

Subject Keywords

Remote sensing., Geospatial data., Image processing., Convolutions (Mathematics)., Neural networks (Computer science).

URI

http://etd.lib.metu.edu.tr/upload/12622095/index.pdf
https://hdl.handle.net/11511/27295

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Geospatial Object Detection Using Deep Networks Barut, Onur; Alatan, Abdullah Aydın (2019-01-01) In the last decade, deep learning has been drawing a huge interest due to the developments in the computational hardware and novel machine learning techniques. This progress also significantly effects satellite image analysis for various objectives, such as disaster and crisis management, forest cover, road mapping, city planning and even military purposes. For all these applications, detection of geospatial objects has crucial importance and some recent object detection techniques are still unexplored to b...
On the analysis of deep convolutional neural networks applied to building detection in satellite images Karagöz, Batuhan; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2015) Deep Learning has gained much interest recently, probably induced by the re- quirements to learn more complex and abstract concepts. As concepts to be learned become more abstract, their regions in the raw input space also become highly variational. In many cases, shallow architectures fail to learn highly varia- tional functions. One area of interest where concepts to be learned are complex is remote sensing. In this thesis, performance and suitability of deep architectures for recognition of building patc...
Case studies on the use of neural networks in eutrophication modeling Karul, C; Soyupak, S; Cilesiz, AF; Akbay, N; Germen, E (2000-10-30) Artificial neural networks are becoming more and more common to be used in development of prediction models for complex systems as the theory behind them develops and the processing power of computers increase. A three layer Levenberg-Marquardt feedforward learning algorithm was used to model the eutrophication process in three water bodies of Turkey (Keban Dam Reservoir, Mogan and Eymir Lakes). Despite the very complex and peculiar nature of Keban Dam, a relatively good correlation (correlation coefficient...
Position estimation for timing belt drives of precision machinery using structured neural networks KILIÇ, Ergin; DOĞRUER, CAN ULAŞ; Dölen, Melik; Koku, Ahmet Buğra (2012-05-01) This paper focuses on a viable position estimation scheme for timing-belt drives using artificial neural networks. In this study, the position of a carriage (load) is calculated via a structured neural network topology accepting input from a position sensor on the actuator side of the timing belt. The paper presents a detailed discussion on the source of transmission errors. The characteristics of the error in different operation regimes are exploited to construct different network topologies. That is, a re...
Neural networks with piecewise constant argument and impact activation Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011) This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...

Citation Formats

O. Barut, “Geospatial object recognition using deep networks for satellite images,” M.S. - Master of Science, Middle East Technical University, 2018.