On the analysis of deep convolutional neural networks applied to building detection in satellite images

Download

index.pdf

Date

2015

Author

Karagöz, Batuhan

Metadata

Show full item record

Item Usage Stats

219
views

102
downloads

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 patches in satellite images are analyzed and discussed. Main architecture that is the subject of interest in this thesis is Deep Convo- lutional Neural Networks. Deep Convolutional Neural Networks has proven to be state of the art machine learning systems in several pattern recognition tasks such as bank check reading, handwriting recognition and face detection. We fo- cus on a particular CNN architecture and trained a Deep Convolutional Neural Network with fully supervised stochastic gradient descent. We obtained a clas- sification accuracy of 90 percent on average which is promising for deep learning implementations on the Remote Sensing Domain. Several measurements on the penultimate layer activations has been employed to reveal insights about what the models learn. Despite seemingly high accuracy results, these measurements put forward that the architecture we pick is unable to learn high level features.

Subject Keywords

Remote sensing., Artificial intelligence., Remote-sensing images., Neural networks (Computer science).

URI

http://etd.lib.metu.edu.tr/upload/12618988/index.pdf
https://hdl.handle.net/11511/25025

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Geospatial object recognition using deep networks for satellite images Barut, Onur; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2018) 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...
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...
An experimental comparison of symbolic and neural learning algorithms Baykal, Nazife (1998-04-23) In this paper comparative strengths and weaknesses of symbolic and neural learning algorithms are analysed. Experiments comparing the new generation symbolic algorithms and neural network algorithms have been performed using twelve large, real-world data sets.
Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks Kocamaz, Korhan; Binici, Barış; Tuncay, Kağan (2021-09-08) Application of deep learning algorithms to the problems of structural engineering is an emerging research field. Inthis study, a deep learning algorithm, namely recurrent neural network (RNN), is applied to tackle a problemrelated to the assessment of reinforced concrete buildings. Inter-storey drift ratio profile of a structure is a quiteimportant parameter while conducting assessment procedures. In general, procedures require a series of timeconsuming nonlinear dynamic analysis. In this study, an extensiv...
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

B. Karagöz, “On the analysis of deep convolutional neural networks applied to building detection in satellite images,” M.S. - Master of Science, Middle East Technical University, 2015.