Change detection for hyperspectral imagery

Vongsy, Karmon; Karimkhan, Shamsaddin; Shaw, A.K.; Wicker, D.

doi:10.1117/12.723161

Cited by 10 publications

(3 citation statements)

References 3 publications

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“…After the HS imagery is registered, the differencing [11] spectral change detector is applied to extract all change regions from the non-planar imagery. This stage provides the first change approximation.…”

Section: Methodsmentioning

confidence: 99%

Removing parallax-induced changes in Hyperspectral Change Detection

Vongsy

Mendenhall

Eismann

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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Hyperspectral-based change detection is often inadvertently affected by image artifacts, reducing the accuracy of the change detector. We present a Hyperspectral Change Detection (HSCD) process to distinguish parallax-induced change from legitimate change. Image parallax decreases the accuracy of change detection results. The approach introduced in this paper utilizes a combination of a spectral change detector and stereo geometry to reduce parallax-induced false alarms. Image parallax is determined by considering the error in the epipolar constraint, meaning the corresponding points between two images must lie on epipolar lines. Experimental analysis shows a false alarm reduction by nearly one order of magnitude on synthetic hyperspectral imagery and nearly two orders of magnitude on real hyperspectral imagery.

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Section: Methodsmentioning

confidence: 99%

Removing parallax-induced changes in Hyperspectral Change Detection

Vongsy

Mendenhall

Eismann

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

Self Cite

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“…On the contrary, pixels containing a change would be more unique in their spectral appearance and would be expected to lie on a different axis (PC2). The modified correlation coefficient uses a maximum likelihood estimate of the change parameter incorporated in the joint conditional probability density function (PDF) of an image pair [5]. CD is complex process where the detection results are influenced by several factors.…”

Section: Change Detectionmentioning

confidence: 99%

Change detection using synthetic hyperspectral imagery

Vongsy

Mendenhall

Hanna

et al. 2009

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

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In the best of circumstances, change detection (CD) is accomplished using measurements from the same instrument and under similar collection circumstances. Complications in the CD process arise when the variability in the collection process is not minimized. Variations between collected images and a lack of precise corresponding ground truth make accurate evaluation of a given CD method imprecise at best. This work leverages synthetic hyperspectral imagery, with known ground truth to include primary and tertiary materials, to investigate the use of common CD algorithms for the hyperspectral CD problem. Specifically, we use synthetic hyperspectral images with different spatial resolutions acquired at different altitudes, thus exhibiting different atmospheric affects. The importance of this work is in definition of a CD taxonomy and using that taxonomy for the accurate evaluation of several CD methods. Results are presented using receiver operating characteristic (ROC) curves and the area under the ROC curve, indicating that, under mildly varying imaging conditions, principal component analysis-based CD outperforms simple image differencing and correlation coefficientbased CD methods.

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“…Temporal change information with comparison and analysis among multi-temporal digital images can be achieved through Change Detection (Vongsy, 2007). This approach answers some of the fundamental questions such as, 1) how fast the changes are taking places; and 2) what are the trends of then changes (Shehaby, Semary, Salah, Ibrahim, 2012).…”

Section: Introductionmentioning

confidence: 99%

Image-Based Change Detection for Bridge Inspection

Adhikari¹,

Moselhi²,

Bagchi³

2013

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building Th

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The changes in defects patterns or in element condition index during visual inspection of bridges are primary concerns for inspectors. This paper presents a new approach for change detection of defects in bridges by identifying changes in texture patterns through spectral analysis of digital images. The commonly used method for change detection is image differentiation. This subtraction method requires images to be of same size, scale, and rotation. However, no two images are same in real practices. Thus, image registration is required to align images and to produce change maps. This process is tedious and it is difficult often to achieve a good registered image. But, the change detection task can be readily modeled in frequency domain for texture patterns discrimination and also for quantifying their properties. This paper proposes a novel approach for change detection by transforming digital images into Fourier spectrum. In new coordinate system, 1-D signature functions can be drawn which facilitates easy comparison of textures in different directions. The proposed methodology provides useful tools for comparison of inspection history graphically and quantitatively. In practice, expensive sensors are used to detect subtle change in defect patterns. The proposed method can be used to detect any subtle change in defect patterns using digital images at much lower cost.

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Change detection for hyperspectral imagery

Cited by 10 publications

References 3 publications

Removing parallax-induced changes in Hyperspectral Change Detection

Removing parallax-induced changes in Hyperspectral Change Detection

Change detection using synthetic hyperspectral imagery

Image-Based Change Detection for Bridge Inspection

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