Image registration by normalized mapping

Wang, Qi; Zou, Cuiming; Yuan, Yuan; Lu, Hongbing; Yan, Pingkun

doi:10.1016/j.neucom.2012.08.012

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…A new non-rigid method based on normalized mapping through which any image can be transformed into an intermediate space is proposed by Q. Wang et al [26]. A new registration method based on nonlinear complex diffusion corner detection and (k) , U (k+1) End thin-plate spline model was proposed by W. Sun et al [27].…”

Section: B Methods Based On the Transformation Modelsmentioning

confidence: 99%

Current Status of Bio-medical Imaging Technology and Method in China: A Review

Liu¹,

Liu²,

Wang³

et al. 2014

BER

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Abstract-Medicine imaging plays a more and more important role in illness detection and treatment. Various imaging skills, including CT imaging, MR imaging, ultrasound imaging and so on are in application and under research nowadays, and those images could be got from medicine databases in hospitals, clinical cases or volunteers. Since the biomedical researches are attracting more and more attention in China, our group investigated papers on biomedical published in the last 3 years in China and referenced 35 papers here. From different views, we investigated methods applied in image resources, image preprocessing, image registration, image segmentation, and their application fields. By reviewing those papers, we aim at getting a brief view of the current biomedical research trend in China.

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Section: B Methods Based On the Transformation Modelsmentioning

confidence: 99%

Current Status of Bio-medical Imaging Technology and Method in China: A Review

Liu¹,

Liu²,

Wang³

et al. 2014

BER

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“…As final pre-processing steps, the optical patches were converted to gray-scale, and all patches were normalized [11] to a radiometric range of [0; 1] with subsequent subtraction of their means.…”

Section: B Data Preparationmentioning

confidence: 99%

Identifying Corresponding Patches in SAR and Optical Images With a Pseudo-Siamese CNN

Hughes

Schmitt

Mou

et al. 2018

IEEE Geosci. Remote Sensing Lett.

185

117

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This is the pre-print version, to read the final version please go to IEEE Geoscience and Remote Sensing Letters on IEEE Xplore. In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross-entropy, we achieve a one-hot indication if two patches correspond or not.The network is trained and tested on an automatically generated dataset that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently co-registered 3D point clouds. The satellite images, from which the patches comprising our dataset are extracted, show a complex urban scene containing many elevated objects (i.e. buildings), thus providing one of the most difficult experimental environments. The achieved results show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development towards a generalized multi-sensor key-point matching procedure.Index Terms-synthetic aperture radar (SAR), optical imagery, data fusion, deep learning, convolutional neural networks (CNN), image matching, deep matchingThe authors are with Signal Processing in Earth Observation,

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“…In the case of the GDEM, the image obtained on 17 October 2011-which is temporally similar to the subject image-were utilized. At this time, one of the most important preprocessing steps for accurate normalization and change detection results is image registration [48], which matches the geometric location information of two images. The Landsat-5 TM images used in this study were the Level-1 precision and terrain corrected product (L1TP) that was corrected geometrically and topographically through co-registration [49].…”

Section: Study Sites and Datamentioning

confidence: 99%

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Seo

Kim

et al. 2017

Remote Sensing

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Abstract:Efforts have been made to detect both naturally occurring and anthropogenic changes to the Earth's surface by using satellite remote sensing imagery. There is a need to maintain the homogeneity of radiometric and phenological conditions to ensure accuracy in change detection, but images to assess long-term changes in time-series data that satisfy such conditions are difficult to obtain. For this reason, image normalization is essential. In particular, the normalizing compositive conditions require nonlinear modeling, and random forest (RF) techniques can be utilized for this normalization. This study employed Landsat-5 Thematic Mapper satellite images with temporal, radiometric and phenological differences, and obtained Radiometric Control Set Samples by selecting no-change pixels between the subject image and reference image using scattergrams. In the obtained no-change regions, RF regression was modeled, and normalized images were obtained. Next, normalization performance was evaluated by comparing the results against the following conventional linear regression methods: mean-standard deviation regression, simple regression, and no-change regression. The normalization performance of RF regression was much higher. In addition, for an additional usefulness evaluation in normalization, the normalization performance was compared with other nonlinear ensemble regressions, i.e. Adaptive Boosting regression and Stochastic Gradient Boosting regression, which confirmed that the normalization performance of RF regression was significantly higher. In other words, it was found to be highly useful for normalization when compared to other nonlinear ensemble regressions. Finally, as a result of performing change detection, normalized subject images generated by RF regression showed the highest accuracy, which indicated that the proposed method (where the image was normalized using RF regression) may be useful in change detection between multi-temporal image datasets.

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Image registration by normalized mapping

Cited by 15 publications

References 33 publications

Current Status of Bio-medical Imaging Technology and Method in China: A Review

Current Status of Bio-medical Imaging Technology and Method in China: A Review

Identifying Corresponding Patches in SAR and Optical Images With a Pseudo-Siamese CNN

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

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