Geo-spatial object detection using local descriptors

Download

index.pdf

Date

2011

Author

Aytekin, Çağlar

Metadata

Show full item record

Item Usage Stats

196
views

73
downloads

There is an increasing trend towards object detection from aerial and satellite images. Most of the widely used object detection algorithms are based on local features. In such an approach, first, the local features are detected and described in an image, then a representation of the images are formed using these local features for supervised learning and these representations are used during classification . In this thesis, Harris and SIFT algorithms are used as local feature detector and SIFT approach is used as a local feature descriptor. Using these tools, Bag of Visual Words algorithm is examined in order to represent an image by the help of histograms of visual words. Finally, SVM classifier is trained by using positive and negative samples from a training set. In addition to the classical bag of visual words approach, two novel extensions are also proposed. As the first case, the visual words are weighted proportional to their importance of belonging to positive samples. The important features are basically the features occurring more in the object and less in the background. Secondly, a principal component analysis after forming the histograms is processed in order to remove the undesired redundancy and noise in the data, reduce the dimension of the data to yield better classifying performance. Based on the test results, it could be argued that the proposed approach is capable to detecting a number of geo-spatial objects, such as airplane or ships, for a reasonable performance.

Subject Keywords

Information Technology.

URI

http://etd.lib.metu.edu.tr/upload/12613488/index.pdf
https://hdl.handle.net/11511/20751

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Developing an integrated system for semi-automated segmentation of remotely sensed imagery Kök, Emre Hamit; Türker, Mustafa; Department of Geodetic and Geographical Information Technologies (2005) Classification of the agricultural fields using remote sensing images is one of the most popular methods used for crop mapping. Most recent classification techniques are based on per-field approach that works as assigning a crop label for each field. Commonly, the spatial vector data is used for the boundaries of the fields for applying the classification within them. However, crop variation within the fields is a very common problem. In this case, the existing field boundaries may be insufficient for perfo...
Positional uncertainty analysis using data uncertainy engine a case study on agricultural land parcels Urgancı, İlksen; Düzgün, H. Şebnem; Department of Geodetic and Geographical Information Technologies (2009) Most of spatial data extraction and updating procedures require digitization of geographical entities from satellite imagery. During digitization, errors are introduced by factors like instrument deficiencies or user errors. In this study positional uncertainty of geographical objects, digitized from high resolution Quickbird satellite imagery, is assessed using Data Uncertainty Engine (DUE). It is a software tool for assessing uncertainties in environmental data; and generating realisations of uncertain da...
Wavelet-based outlier detection and denoising of airborne laser scanning data Akyay, Tolga; Karslıoğlu, Mahmut Onur; Department of Geodetic and Geographical Information Technologies (2008) The method of airborne laser scanning also named as LIDAR has recently turned out to be an efficient way for generating high quality digital surface and elevation models. In this work, wavelet-based outlier detection and different wavelet thresholding (wavelet shrinkage) methods for denoising of airborne laser scanning data are discussed. The task is to investigate the effect of wavelet-based outlier detection and find out which wavelet thresholding methods provide best denoising results for post-processi...
Road detection by mean shift segmentation and structural analysis Dursun, Mustafa; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2012) Road extraction from satellite or aerial images is a popular issue in remote sensing. Extracted road maps or networks can be used in various applications. Normally, maps for roads are available in geographic information systems (GIS), however these informations are not being produced automatically. Generally they are formed with the aid of human. Road extraction algorithms are trying to detect the roads from satellite or aerial images with the minimum in-teraction of human. Aim of this thesis is to analyze ...
Road extraction from high-resolution satellite images Özkaya, Meral; Temizel, Alptekin; Department of Information Systems (2009) Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for different applications such as automated map generation and change detection. Roads are also important to detect other structures such as buildings and urban areas. In this thesis, the road extraction approach is based on Active Contour Models for 1- meter resolution gray level images. Active Contour Models contains Snake Approach. During applications, the road structure was sepa...

Citation Formats

Ç. Aytekin, “Geo-spatial object detection using local descriptors,” M.S. - Master of Science, Middle East Technical University, 2011.