Change detection for high resolution satellite images, based on SIFT descriptors and an a contrario approach

Dellinger, Flora; Delon, Julie; Gousseau, Yann; Michel, Julien; Tupin, Florence

doi:10.1109/igarss.2014.6946667

Cited by 23 publications

(15 citation statements)

References 7 publications

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“…A contrario framework could be used to detect changes as in [20] from the scatterers images. Furthermore, strategies should be investigated to tackle the practical problem caused by the heavy consumption of memory of our optimization method.…”

Section: Discussionmentioning

confidence: 99%

Sparse + smooth decomposition models for multi-temporal SAR images

Lobry

Denis²,

Tupin

2015

2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp)

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SAR images have distinctive characteristics compared to optical images: speckle phenomenon produces strong fluctuations, and strong scatterers have radar signatures several orders of magnitude larger than others. We propose to use an image decomposition approach to account for these peculiarities.Several methods have been proposed in the field of image processing to decompose an image into components of different nature, such as a geometrical part and a textural part. They are generally stated as an energy minimization problem where specific penalty terms are applied to each component of the sought decomposition.We decompose temporal series of SAR images into three components: speckle, strong scatterers and background. Our decomposition method is based on a discrete optimization technique by graph-cut. We apply it to change detection tasks.

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Section: Discussionmentioning

confidence: 99%

Sparse + smooth decomposition models for multi-temporal SAR images

Lobry

Denis²,

Tupin

2015

2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp)

Self Cite

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“…In addition, when image transformations take place, unrelated and unwanted areas around the scene are also detected. In this situation, change detection algorithms [42] report these areas as a change in the scene. This result is not accurate in construction scenarios where change detection algorithms are used to detect the construction progress based on changes in the image frames.…”

Section: Problem Statementmentioning

confidence: 99%

Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites

Sabzevar

Gheisari

2020

Applied Sciences

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Image matching techniques offer valuable opportunities for the construction industry. Image matching, a fundamental process in computer vision, is required for different purposes such as object and scene recognition, video data mining, reconstruction of three-dimensional (3D) objects, etc. During the image matching process, two images that are randomly (i.e., from different position and orientation) captured from a scene are compared using image matching algorithms in order to identify their similarity. However, this process is very complex and error prone, because pictures that are randomly captured from a scene vary in viewpoints. Therefore, some main features in images such as position, orientation, and scale of objects are transformed. Sometimes, these image matching algorithms cannot correctly identify the similarity between these images. Logically, if these features remain unchanged during the picture capturing process, then image transformations are reduced, similarity increases, and consequently, the chances of algorithms successfully conducting the image matching process increase. One way to improve these chances is to hold the camera at a fixed viewpoint. However, in messy, dusty, and temporary locations such as construction sites, holding the camera at a fixed viewpoint is not always feasible. Is there any way to repeat and retrieve the camera’s viewpoints during different captures at locations such as construction sites? This study developed and evaluated an orientation and positioning approach that decreased the variation in camera viewpoints and image transformation on construction sites. The results showed that images captured while using this approach had less image transformation in contrast to images not captured using this approach.

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“…In other words, some work on adaptation of homogenization methods is present in the literature. However, and to the best of the authors' knowledge, no formal approach exists that properly accounts for the complexity of data acquired by similar, yet different VHR multispectral sensors [15][16][17][18], nor does an approach exist that guides the user from data pre-processing until the final CD process.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Unsupervised Change Detection in Multitemporal VHR Images Acquired by Different Multispectral Sensors

2018

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This paper proposes an approach for the detection of changes in multitemporal Very High Resolution (VHR) optical images acquired by different multispectral sensors. The proposed approach, which is inspired by a recent framework developed to support the design of change-detection systems for single-sensor VHR remote sensing images, addresses and integrates in the general approach a strategy to effectively deal with multisensor information, i.e., to perform change detection between VHR images acquired by different multispectral sensors on two dates. This is achieved by the definition of procedures for the homogenization of radiometric, spectral and geometric image properties. These procedures map images into a common feature space where the information acquired by different multispectral sensors becomes comparable across time. Although the approach is general, here we optimize it for the detection of changes in vegetation and urban areas by employing features based on linear transformations (Tasseled Caps and Orthogonal Equations), which are shown to be effective for representing the multisensor information in a homogeneous physical way irrespectively of the considered sensor. Experiments on multitemporal images acquired by different VHR satellite systems (i.e., QuickBird, WorldView-2 and GeoEye-1) confirm the effectiveness of the proposed approach.

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Change detection for high resolution satellite images, based on SIFT descriptors and an a contrario approach

Cited by 23 publications

References 7 publications

Sparse + smooth decomposition models for multi-temporal SAR images

Sparse + smooth decomposition models for multi-temporal SAR images

Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites

An Approach for Unsupervised Change Detection in Multitemporal VHR Images Acquired by Different Multispectral Sensors

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Change detection for high resolution satellite images, based on SIFT descriptors and an a contrario approach

Cited by 23 publications

References 7 publications

Sparse &#x002B; smooth decomposition models for multi-temporal SAR images

Sparse &#x002B; smooth decomposition models for multi-temporal SAR images

Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites

An Approach for Unsupervised Change Detection in Multitemporal VHR Images Acquired by Different Multispectral Sensors

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Sparse + smooth decomposition models for multi-temporal SAR images

Sparse + smooth decomposition models for multi-temporal SAR images