XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Type of content:: Konferenzschrift

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Reihe:: ISPRS archives

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Editor:: International Society for Photogrammetry and Remote Sensing

Author:: International Society for Photogrammetry and Remote Sensing, 22.; 2012; Melbourne

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Type of content:: Konferenzschrift

DOI:: 10.14463/KXP:1663821976

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Reihe:: ISPRS archives (volume 39, B7 (2012))

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing

Author:: International Society for Photogrammetry and Remote Sensing, 22.; 2012; Melbourne

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/5: METHODS FOR CHANGE DETECTION AND PROCESS MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ACCURACY IMPROVEMENT OF CHANGE DETECTION BASED ON COLOR ANALYSIS J. Wang, H. Koizumi, T. Kamiya

Document type:: Multivolume work

Structure type:: Chapter

overall nputing and 7, as the we put hift I, in 1]. For and the as the (1) ed 7 I, xels in ow that )ver the howing ce after ioimage regions | errors, 1appens fication sults in a local | under ind the tions in )pear in t times. of color nify the E ination hooting nes the ave the omplex sfer, to yok like , which atistics. esigned e target 0 x«0 f(x) = 9 (x-g)+g, 0«x«255 @ where g, = intensity mean of the source image g, = intensity mean of the target image oO, = standard deviation of the source image C, = standard deviation of the target image Since the main transfer function for intensity value between 0 and 255 is linear, we call it linear transfer function here. The above method is designed to assure the following rules. f(8.)78, (3) f(g.)=— a a, f(0)=0 (5) fQ55) = 255 (6) With these rules, the designed transfer function is able to map all the possible intensity values still to the range of [0,255], and also map the intensity mean from g to g,, and the standard deviation from 0,100,. (c) (d) Figure 1. Illumination change adjustment in one example (a) Original old year orthoimage (b) Original current year orthoimage (c) Adjusted current year orthoimage according to old year orthoimage by linear transfer function (d) Adjusted old year orthoimage according to current year orthoimage by linear transfer function. We show the experimental results of illumination change adjustment through linear transfer function in Figure 1. From it, we find that after adjustment, the color tone in orthoimages becomes more similar than the original case, i.e. (a) and (c), also (b) and (d) are more similar in illumination than (a) and (b). A piecewise cubic spline transfer function is also proposed in [5], to overcome the fast saturation near very low and very high intensity values. According to our experimental results on several datasets, the illumination change adjustment under this function results in fewer wrong detections, but also fewer International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia correct detections. To assure no correct detections are missed, we decide to take the linear transfer function in this framework. Furthermore, when it comes to select which orthoimage as the source image, we make further experiments on comparing the change detection results from two possible cases. The experimental results show that it is better to choose the darker orthoimage as the source image and the brighter one as the target image, based on the rule of fewer wrong detections and no influence on correct detections. To summarize the above analysis, we undertake the linear color transfer on the darker orthoimage to make it have similar color tone to the brighter orthoimage. That is to say, in the case of Figure 1, after illumination change adjustment, (a) and (c) are taken as the input data for the next step. 3.3 Precise Location Difference Rectification To solve the remaining location errors after global location difference rectification by global matching method, we further propose the following local matching method. In this method, the rectification amount of location difference is computed for each local region. It mainly consists of three steps. Firstly, key points are extracted over the whole image by Harris corner extraction [6]. Note the threshold to select the key points is set relatively high to assure that only reliable corners are selected. We perform Harris corner extraction respectively in two orthoimages and select the one with more reliable corners for example ;, , as the benchmark image. Secondly, for each key point in the benchmark image ;,, we try to search for its matching point in ; by template matching. Since there is only small local difference after global rectification, the searching for the matching point is only carried out in the neighbourhood of each key point. The computing is similar to Function (1) of the global matching, only with the difference that the matching cost is computed in a template surrounding the key point, and the template is shifted in the neighbourhood of the key point. We further filter the pair of matched points with high matching cost. In this case, wrong matching pairs are removed, including cases like the pair including the corners from some noise in the benchmark orthoimage, or the corners from the moving object in the benchmark image, and so on. Through the experimental results, we find that after filtering, only the matching pairs of unchanging object corners are mainly left, like the corners of unchanging buildings. Thirdly, in each local block, the rectification amount is obtained by a voting scheme. All the remaining matched pairs are used for voting by the shift information between the two corresponding pixels. For each possible shift position in the shift range, the one with the highest votes is taken as the final rectification amount of this block, as described in Function (7). arg max ic. ) (7) me[-r,r],ne[-r,r] where C mn ^ number of votes for the shift position m, n Here the whole image is segmented into several same-sized non-overlapping rectangle blocks. The size of each block is also carefully set. Because if it is too small, the shift amount for rectification may be easily affected by the image details, while if it is too large, there is not quite much difference from the global matching method. Based on the experience, for the experimental image of 3000*2500, we set the block size as 500*500.

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