{"title":"Reflectance-based 3D shape refinement of surfaces with spatially varying BRDF properties","authors":"Malte Lenoch, Christian Wohlery","doi":"10.1109/PRRS.2016.7867015","DOIUrl":"https://doi.org/10.1109/PRRS.2016.7867015","url":null,"abstract":"This paper proposes a novel algorithm to determine the spatially varying reflectance of an arbitrary object based on per-pixel estimates of the bidirectional reflectance distribution function (BRDF). The proposed procedure is especially designed to work in environments that yield relatively sparse reflectance measurements, since the parameter estimation is initialized on regions of similar brightness to ensure sufficient data to rely on. We suppose a physically plausible version of the Blinn BRDF model to be sufficiently accurate in combination with pixelwise parameter maps. Thus, we consider isotropic reflectance only. The initial estimate of the depth data can either be supplied to the algorithm or, as proposed here, be obtained by solving the photometric stereo equations for the diffuse component of the surface reflectance. These data are probably affected by noise and errors due to the simplified diffuse estimate. We use an iterative approach to refine this depth information based on the surface gradient that is computed from BRDF parameter maps. A low-passed version of the initial depth is used as boundary condition when integrating the surface normal field. We apply the proposed method to real-world data sets and show that the gradient information is able to refine the initial depth data. Comparison to an industrial standard 3D scanner shows that accurate results are obtained at a lower noise level without removing high frequency details of the surface that are visible in the image data.","PeriodicalId":327270,"journal":{"name":"2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128418193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improving semantic orthophotos by a fast method based on harmonic inpainting","authors":"B. Kottler, D. Bulatov, H. Schilling","doi":"10.1109/PRRS.2016.7867012","DOIUrl":"https://doi.org/10.1109/PRRS.2016.7867012","url":null,"abstract":"Context-based modeling of 3D urban terrain has become increasingly popular in the last two decades. Typically, orthophotos are used for texturing ground. In order to increase locational awareness, it is useful to eliminate from the orthophotos those object instances which frequently appear and disappear in the terrain. Vehicles are good examples of such instances. Assuming that vehicles were detected at a previous stage of the algorithm, we developed a simple, easily parallelizable procedure on orthophoto inpainting while considering classes of the surrounding objects. This is done by reducing the weights of pixels which should not be used for inpainting. The benefits of our approach are demonstrated for two datasets by comparison with a state-of-the-art method and by integrating the corrected orthophoto into the already existing urban terrain model which moreover includes the ground surface as well as 3D models of buildings, trees and vehicles.","PeriodicalId":327270,"journal":{"name":"2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116301057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Menghan Xia, Jian Yao, Renping Xie, Xiaohu Lu, Li Li
{"title":"Robust alignment for UAV images based on adaptive adjustment","authors":"Menghan Xia, Jian Yao, Renping Xie, Xiaohu Lu, Li Li","doi":"10.1109/PRRS.2016.7867017","DOIUrl":"https://doi.org/10.1109/PRRS.2016.7867017","url":null,"abstract":"Abstract—In this paper, we investigate thoroughly the problem of aligning sequential images taken from low-altitude unmanned aerial vehicles (UAV). It is difficult because the photographic scene is no longer an approximate plane from the UAVs at low flight altitude. On this occasion, we assume the ground regions of the scene share the dominant plane, so sequential images are aligned based on the registration of the ground as a landmark, which precludes the obstruct of other objects of various altitudes. Specially, considering the occasional accumulating error and deformation degree, an adaptive bundle adjustment based on the updated reference image is creatively designed to guarantee the quality of incremental alignment in an efficient way. Finally, the global refinement on transformation models is performed under the constraint of no perspective distortion engendered, to improve the aligning accuracy further. Experimental results on several challenging datasets illustrate the superiority of our approach.","PeriodicalId":327270,"journal":{"name":"2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134367748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data","authors":"Aurélien Ducournau, Ronan Fablet","doi":"10.1109/PRRS.2016.7867019","DOIUrl":"https://doi.org/10.1109/PRRS.2016.7867019","url":null,"abstract":"In this paper, we propose to address the downscaling of ocean remote sensing data using image super-resolution models based on deep learning, and more particularly Convolutional Neural Networks (CNNs). The goal of this study, for which we focus on satellite-derived Sea Surface Temperature (SST) data, is to evaluate the efficiency and the relevance of deep learning architectures applied to oceanographic remote sensing data. By using a CNN architecture, namely SRCNN (Super Resolution CNN), on a large-scale dataset of SST fields, we show that it allows a considerable gain in terms of PSNR compared to classical downscaling techniques. These results point out the relevance of deep learning models specifically trained for ocean remote sensing data and advocate for other applications to the reconstruction of high-resolution sea surface geophysical fields from multi-sensor satellite observations.","PeriodicalId":327270,"journal":{"name":"2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131239808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}