Deep convolutional neural networks with an application towards geospatial object recognition /

Download

index.pdf

Date

2014

Author

Batı, Emrecan

Metadata

Show full item record

Item Usage Stats

168
views

99
downloads

The passion of human-being to invent intelligent systems becomes more and more meaningful day by day, as the data captured every second by artificial sensors needs to be examined and classified for many applications. The processing of ever-increasing amount of data by defining information explicitly seems nearly impossible, regarding the variability and the amount of the information, which reveals the need for intelligent systems that are capable of learning. Deep learning is a set of algorithms that attempts to find a hierarchical representation of the input data by trying to mimic the way human brain captures the critical aspects of excessive sensory data, to which it is exposed to every second. Convolutional neural networks, which are trainable learning structures, are also biologically inspired from the receptive fields in visual cortex. In this thesis, the performance of convolutional neural networks are investigated for an application towards geospatial target detection and classification from satellite images. Based on the experiments, it is observed that the utilization of preprocessing, dropout, i.e. dropping neurons randomly in the training phase, and rectified linear unit as the activation function improves the classification rate, significantly. However, the application of this deep classifier on satellite images still yields high false alarm rate, possibly due to insufficient number of training data.

Subject Keywords

Artificial intelligence., Geospatial data., Remote-sensing images., Neural networks (Computer science)., Back propagation (Artificial intelligence).

URI

http://etd.lib.metu.edu.tr/upload/12617992/index.pdf
https://hdl.handle.net/11511/24069

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Semantic data modeling of spatiotemporal database applications Yazıcı, Adnan; Sun, N (Wiley, 2001-07-01) Due to the ubiquity of space-related and time-related information, the ability of a database system to deal with both spatial and temporal phenomenon facts in a spatiotemporal applications is highly desired. However, uncertain and fuzzy information in these applications highly increases the complexity of database modeling. In this paper we introduce a semantic data modeling approach for spatiotemporal database applications. We specifically focus on various aspects of spatial and temporal database issues and...
Training Methodology for a Multiplication Free Implementable Operator Based Neural Networks Yıldız, Ozan; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017) Technological advances opened new possibilities for computing environments including smart phones, smart appliances, and drones. Engineers try to make these devices smart, self-sustaining through usage of machine learning techniques. However, most of the mobile environments have limited resources like memory, computing power and battery, and consequently traditional machine learning algorithms which require relatively high resources might not be suitable for them. Therefore, efficient versions of traditiona...
Adaptive mean-shift for automated multi object tracking Beyan, C.; Temizel, Alptekin (2012-01-01) Mean-shift tracking plays an important role in computer vision applications because of its robustness, ease of implementation and computational efficiency. In this study, a fully automatic multiple-object tracker based on mean-shift algorithm is presented. Foreground is extracted using a mixture of Gaussian followed by shadow and noise removal to initialise the object trackers and also used as a kernel mask to make the system more efficient by decreasing the search area and the number of iterations to conve...
Data mining in deductive databases using query flocks Toroslu, İsmail Hakkı (Elsevier BV, 2005-04-01) Data mining can be defined as a process for finding trends and patterns in large data. An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. Most research on data mining is concentrated on traditional relational data model. On the other hand, the query flocks technique, which extends the concept of association rule mining with a 'generate-and-test' model for different kind of patterns, can also be applied to deduct...
DEVELOPMENT OF SCIENTIFIC SOFTWARE: A SYSTEMATIC MAPPING, A BIBLIOMETRICS STUDY, AND A PAPER REPOSITORY Farhoodi, Roshanak; Garousi, Vahid; Pfahl, Dietmar; Sillito, Jonathan (World Scientific Pub Co Pte Lt, 2013-05-01) Scientific and engineering research is heavily dependent on effective development and use of software artifacts. Many of these artifacts are produced by the scientists themselves, rather than by trained software engineers. To address the challenges in this area, a research community often referred to as "Development of Scientific Software" has emerged in the last few decades. As this research area has matured, there has been a sharp increase in the number of papers and results made available, and it has thu...

Citation Formats

E. Batı, “Deep convolutional neural networks with an application towards geospatial object recognition /,” M.S. - Master of Science, Middle East Technical University, 2014.