Correlation-based variational change detection

Download

index.pdf

Date

2018

Author

Aktaş, Gizem

Metadata

Show full item record

Item Usage Stats

172
views

199
downloads

Change detection is an important research topic for observing the earth but there are various challenges to obtain an accurate change map such as image noise, subtle differences and image acquisition alteration. Various studies handled these problems as separate steps. In the ﬁrst step, images are registered, then the image noise are eliminated. After images are normalized to eliminate image acquisition differences, actual change detection routine can be applied as a ﬁnal step. However, this step-by-step approach leads to accumulation of errors in each step which leads to decrease in accuracy of ﬁnal change detection map. Step-by-step approach is widely used because these problems are interpenetrated to each other and researchers use this to divide problem into sub-problems. In this study, a correlation based variational change detection (CVCD) method for elevation models is proposed. In essence, CVCD aims to produce smooth change maps while preserving the details of the terrain by minimizing a variational cost function. In this variational cost function a novel correlationbased data ﬁdelity term is used with an L1-norm regularization term which imposes smoothness on obtained change map. In addition, L1-norm TV regularization term is preferred because it can preserve details such as point-changes, edges and corners of changes. In order to minimize the proposed cost function using simple approximations in an iterative manner, a simple and efﬁcient algorithm is suggested. Quantitative experiments on synthetic noisy data show that CVCD can provide a detection rate of 95% while staying in the low false alarm regime, i.e. less than 10^-2. Also, qualitative experiments on real-world data show the success of the CVCD for the changes with different characteristics.

Subject Keywords

Remote sensing., Imaging systems., Image processing.

URI

http://etd.lib.metu.edu.tr/upload/12622241/index.pdf
https://hdl.handle.net/11511/27571

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

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...
Shadow detection on multispectral images Dağlayan Sevim, Hazan; Çetin, Yasemin; Department of Information Systems (2015) Shadows caused by clouds, mountains or high human-made structures pose challenges for identiﬁcation of objects from satellite or aerial images since they deteriorate and mask true spectral properties of the objects. Therefore, they should be detected to accurately classify objects. Moreover, in change detection problems, shadows deteriorate performance because mere spectral changes act as actual object changes. Shadows are also useful as cues for estimating the height of an object or determining the time of...
A Fast shape detection approach by directional integrations Okman, Osman Erman; Akar, Gözde; Department of Electrical and Electronics Engineering (2013) Detection and identification of objects from aerial images are important problems for various types of application areas. For many of the man-made structures shape is a fundamental feature by which these objects are separated from the background and other structures. In this thesis, a novel geometric shape detection algorithm based on the spatial properties of structures is proposed. Since the objects are transformed into 1-D vectors by evaluating directional integrals and detections occur by the analysis o...
Image fusion for improving spatial resolution of multispectral satellite images Ünlüsoy, Deniz; Süzen, Mehmet Lütfi; Department of Geological Engineering (2013) In this study, four different image fusion techniques have been applied to high spectral and low spatial resolution satellite images with high spatial and low spectral resolution images to obtain fused images with increased spatial resolution, while preserving spectral information as much as possible. These techniques are intensity-hue-saturation (IHS) transform, principle component analysis (PCA), Brovey transform (BT), and Wavelet transform (WT) image fusion. Images used in the study belong to Çankırı reg...
Multi-modal stereo-vision using infrared / visible camera pairs Yaman, Mustafa; Kalkan, Sinan; Department of Computer Engineering (2014) In this thesis, a novel method for computing disparity maps from a multi-modal stereo-vision system composed of an infrared-visible camera pair is introduced. The method uses mutual information as the basic similarity measure where a segmentation based adaptive windowing mechanism is proposed along with a novel mutual information computation surface for greatly enhancing the results. Besides, the method incorporates joint prior probabilities when computing the cost matrix in addition to negative mutual info...

Citation Formats

G. Aktaş, “Correlation-based variational change detection,” M.S. - Master of Science, Middle East Technical University, 2018.