Detecting changes of buildings from aerial images using shadow and shading model

Watanabe, Shinji; Miyajima, Koji; Mukawa, Naoki

doi:10.1109/icpr.1998.711966

Cited by 22 publications

(14 citation statements)

References 9 publications

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“…(i) Detecting structural changes without being affected by differences in the methods of viewing structures (ii) Detecting structural changes without being affected by differences in the shapes of shadows For problem (ii), previous authors [16,17] decided to assume that the previous and current aerial images were taken at exactly the same position with no discrepancy occurring and proposed a method of using the outdoor light source model to detect structural changes without being affected by differences in the shapes of shadows. However, this method did not take problem (i) into consideration.…”

Section: Introductionmentioning

confidence: 99%

Detecting changes of buildings using stereo matching from aerial images taken at different positions and times

Watanabe¹,

Miyajima

Inoue³

et al. 2004

Electron Comm Jpn Pt III

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SUMMARYGIS base maps are updated by using parallax to compare two aerial images taken at different times to interpret structural changes. Since an aerial image generally photographs a wide range, this interpretation work requires a great deal of time. Therefore, in this paper, the authors propose a method of automatically extracting structural changes with high precision to reduce the load of this interpretation work. Not only do the times differ at which the aerial images used for comparison were taken, but the shooting positions also frequently differ. This causes differences in shadow shapes and differences in structure viewing methods, and when aerial images are compared during automatic interpretation, it will cause the detection precision of structural changes to decrease. To deal with this problem, the authors eliminated the effects of differences in structure viewing methods by applying stereo matching and letting noncorresponding regions be structural change candidates. In addition, to correctly detect structural changes from those candidates, the authors used the outdoor light source model, which had been proposed by previous authors, to eliminate the effects of differences in shadow shapes. The results that were obtained by the proposed method showed that the detection precision of structural changes was approximately four times that of the conventional method.

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Section: Introductionmentioning

confidence: 99%

Detecting changes of buildings using stereo matching from aerial images taken at different positions and times

Watanabe¹,

Miyajima

Inoue³

et al. 2004

Electron Comm Jpn Pt III

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“…Watanabe et al [21] discussed an interesting radiometric pre-processing step specifically for change detection applied to overhead imagery of buildings. Using the known position of the sun and a method for fitting building models to images, they were able to remove strong shadows of buildings due to direct light from the sun, prior to applying a change detection algorithm.…”

Section: ) Sudden Changes In Illuminationmentioning

confidence: 99%

Image change detection algorithms: a systematic survey

Radke

Andra

Al-Kofahi

et al. 2005

IEEE Trans. on Image Process.

1,618

790

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Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

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“…Unfortunately, these methods can only deal with man-made structures 1,3,11 , simple shapes 6,7,8 or two-dimensional maps 12 . They cannot validate the geometry of complex databases such as those required in synthetic vision systems.…”

Section: Figure 1 Esvs Conceptmentioning

confidence: 99%

“…Nevatia et al 1,3 have developed a technique to confirm the presence of man-made structures (buildings) in aerial images. Watanabe, Miyajima and Mukawa 11 have developed an algorithm to validate the presence and geometry of buildings, using a shadow and shading model. Leclerc et al 6,7,8 proposed a method that uses aerial images for detecting changes in shapes that can be modelled as single-valued functions.…”

Section: Figure 1 Esvs Conceptmentioning

confidence: 99%

<title>Online database updating by change detection</title>

Simard

Ferrie

2001

SPIE Proceedings

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Synthetic vision systems render artificial images of the world based on a database and position/attitude information of the aircraft. Due to both its static nature and inherent modelling errors, the database introduces anomalies in the synthetic imagery. Since it reflects at best a nominal state of the environment, it often requires updating via online measurements. The latter can vary from correction of pose and geometry to more complex operations such as marking the locations of detected obstacles. This paper presents an approach for detecting database geometric anomalies online. Since range sensors have a low update rate, they cannot be used for quick validation. Instead of range data, the proposed technique employs an imaging sensor, which can be of any type. It takes advantage of the fact that given a geometric model of the scene and known motion of the observer, the sensor image warping can be exactly predicted. If the geometry of the database is incorrect, the sensor image will not be correctly predicted and geometric differences will thus be detected. The algorithm is tested against simulated imagery and results show that it can correctly identify geometric anomalies. It can cope with known misalignment of the database and pose estimation errors. The technique is shown to be quite robust in low visibility conditions, given that the validated features are at least partially visible. It also automatically detects if a motion is sufficient for a given sensitivity.

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Detecting changes of buildings from aerial images using shadow and shading model

Cited by 22 publications

References 9 publications

Detecting changes of buildings using stereo matching from aerial images taken at different positions and times

Detecting changes of buildings using stereo matching from aerial images taken at different positions and times

Image change detection algorithms: a systematic survey

<title>Online database updating by change detection</title>

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