2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)最新文献

{"title":"An Encoding-Based Back Projection Algorithm for Underground Holes Detection via Ground Penetrating Radar","authors":"Shaokun Zhang, Zhiyou Hong, Yiping Chen, Zejian Kang, Zhipeng Luo, Jonathan Li","doi":"10.1109/PRRS.2018.8486182","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486182","url":null,"abstract":"As underground cavities can cause ground collapse, which will make serious threat to people's safety and property. It is of great significance to implement underground cavity inspection on urban streets and roads subgrade. In the practical application of engineering, the ground penetrating radar (GPR) has shown promising for detection of underground cavities. In this paper, we propose a novel encoding-based back projection (EBP) algorithm to detect underground holes. Our proposed method has a natural filtering function and avoids the effect of trailing, which makes the target localization more accurate. The experiments use the simulation data derived from the GPR numerical simulation software (GprMax) and the measured data collected from the Latvia radar system. And the results demonstrate that the proposed method has superior performance.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129917713","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":"Integration of Heterogeneous Features from Co-registered Hyperspectral and LiDAR Data for Land Cover Classification","authors":"Farah Jahan, Jun Zhou, M. Awrangjeb, Yongsheng Gao","doi":"10.1109/PRRS44410.2018.9396733","DOIUrl":"https://doi.org/10.1109/PRRS44410.2018.9396733","url":null,"abstract":"Exploiting the multi-source data is an effective but challenging problem for land cover classification. Popular remote sensor data, e.g., hyperspectral (HS) and light detection and ranging (LiDAR), contain complementary information for land cover if they are co-registered. In this paper, we aim to integrate information extracted from these data sources for land cover classification. At first, we propose a novel feature extraction method by calculating the inverse coefficient of variation (ICV) using the Gaussian probability of neighbourhood between every pair of bands in HS data. This is calculated for each band with respect to every other band to form an ICV cube. We reduce the number of planes in the cube by applying principal component analysis (PCA) on it and spatial features are then extracted for significant principal components. The spectral information from HS data, their ICV responses, and spatial information from ICV responses have complementary information; that is why we fuse them together by layer stacking to generate discriminant features. Secondly, we also derive height and spatial features from LiDAR Digital Elevation Model (DSM), which are later concatenated with the HS derived features. Finally, these features are classified using linear discriminant analysis (LDA) classifier. The classification results prove the effectiveness of the derived features from both data sources.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126092497","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":"Fine Registration of Mobile and Airborne LiDAR Data Based on Common Ground Points","authors":"Yanming Chen, Xiaoqiang Liu, Mengru Yao, Liang Cheng, Manchun Li","doi":"10.1109/PRRS.2018.8486181","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486181","url":null,"abstract":"Light Detection and Ranging (LiDAR), as an active remote sensing technology, can be mounted on satellite, aircraft, vehicle, tripod and other platforms to acquire three-dimensional information of the earth surface efficiently. However, it is difficult to obtain omnidirectional three-dimensional information of the earth surface using a LiDAR system from a single platform. So the integration of multi-platform LiDAR data, in which data registration is a core part, has become an important topic in geospatial information processing. In this paper, the iterative closest common ground points registration method is proposed. Firstly, the possible common ground points of mobile and airborne LiDAR data are extracted. And then the adaptive octree structure is utilized to thin the LiDAR ground points, which make mobile and airborne LiDAR ground points have the same point density. Finally, the fine registration parameters are calculated by the iterative closest point (ICP) method, in which the thinned ground points from two sources are input data. The innovation of this method is that the common ground points and adaptive octree structure are used to optimize the input data of iterative closest point, which overcomes the registration difficulty caused by different perspectives and resolutions of mobile and airborne LiDAR. The proposed method was tested in this paper and can effectively realize the fine registration of mobile and airborne LiDAR data and make the façade points acquired by mobile LiDAR and the roof points acquired by airborne LiDAR fitter.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114220181","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":"Monitoring and Assessment of the Space Pattern of Ports Based on GF-1 Satellite Remote-Sensing Images","authors":"Xuchun Li, Huimin Xu, Fushan Zhang, A. Suo","doi":"10.1109/PRRS.2018.8486388","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486388","url":null,"abstract":"In this paper, domestic GF-1 satellite remote sensing imagery is used to analyze the internal spatial pattern of the port and the characteristics of its constituent elements, and an object-oriented remote sensing monitoring method and process for port space pattern is established. The dock shoreline index and the dock coastline utilization index are explored and constructed. Dock index, storage yard index and dock basin index were used to evaluate the intensive use of port space patterns, and an empirical study was conducted in the Yingkou Bayuquan port area. The results showed that the dock shoreline index of Yingkou Bayuquan Port area was 0.51, the dock coastline utilization index was 15.08 million tons/km, the dock index was 12.23 hm2/km, the dock basin index was 242.76 hm2/km, and the storage yard index was 108.46 hm2/km. The utilization index of the dock coastline is basically 150 million tons/km, and the basic ratio of the dock and dock area, storage yard area and dock basin area is 1.00:12.00:108.00:250.00. Yingkou Bayuquan Port still has a potential of 88.21 million tons of throughput per year.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126116483","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-Based Enhancement of Hyperspectral Images Using Simulated Ground Truth","authors":"A. Nikonorov, M. Petrov, Sergei Bibikov, V. Kutikova, P. Yakimov, A. Morozov, R. Skidanov, N. Kazanskiy","doi":"10.1109/PRRS.2018.8486408","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486408","url":null,"abstract":"The paper addresses the problem of imaging quality enhancement for the Offner hyperspectrometer using a convolutional neural network. We use a deep convolutional neural network with residual training and PReLU activation, inspired by the super-resolution task for RGB images. In the case of hyperspectral imaging, it is often a problem to find a large enough ground truth dataset for training a neural network from scratch. Transfer learning using the network pretrained for RGB images with some pre- and postprocessing is one of the possible workarounds. In this paper, we propose to simulate the necessary ground truth data using non-imaging spectrometer. The obtained dataset with partially simulated ground truth is then used to train the convolutional neural network directly for hyperspectral image quality enhancement. The proposed training approach also allows to incorporate distortions specific for hyperspectral images into the enhancement procedure. It allows to successfully remove the striping distortions inherent to the Offner scheme of image acquisition. The experimental results of the proposed approach show a significant quality gain.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127644935","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":"Neighbor Consistency Baced Unsupervised Manifold Alignment for Classification of Remote Sensing Image","authors":"Chuang Luo, Li Ma","doi":"10.1109/PRRS.2018.8486168","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486168","url":null,"abstract":"We perform unsupervised domain adaptation for classification of remote sensing images by exploiting unsupervised manifold alignment approach. Manifold alignment method utilized corresponding points between domains to align data manifolds of source and target domains, where the corresponding points can be constructed by labeled information. Supposing labeled samples are not available in target domain, we proposed neighbor consistency (NC) constraint to select some target points that have reliable predictions. These points and labeled source data are then used to construct corresponding relationships, resulting in unsupervised manifold alignment. The neighbor consistency based unsupervised manifold alignment is denoted as NCUMA in this paper. Both multispectral and hyperspectral remote sensing data have been used to demonstrate the effectiveness of the NCUMA approach.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116998237","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":"Integrating Active Learning and Contextually Guide for Semantic Labeling of LiDAR Point Cloud","authors":"Tengping Jiang, Yongjun Wang, Shuaibing Tao, Yunli Li, Shan Liu","doi":"10.1109/PRRS.2018.8486166","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486166","url":null,"abstract":"To alleviate the difficulties in obtaining training data sets of 3D point clouds, an active learning (AL) framework is proposed to iteratively select a small portion of unlabeled points to query their labels, and creates a minimum manually-annotated training set. To handle the biased sampling problem caused by category imbalance and local similarities, a neighbor-consistency prior is used to conduct an unbiased sampling for selecting the value samples into the training set. Additionally, to reduce the number of categories used in labeling, a higher-order MRF containing a regional label cost term, is exploited to refine the labeling results.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115432416","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":"Instance Segmentation of Trees in Urban Areas from MLS Point Clouds Using Supervoxel Contexts and Graph-Based Optimization","authors":"Yusheng Xu, Zhenghao Sun, L. Hoegner, Uwe Stilla, W. Yao","doi":"10.1109/PRRS.2018.8486220","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486220","url":null,"abstract":"In this paper, an instance segmentation method for tree extraction from MLS data sets in urban scenes is developed. The proposed method utilizes a supervoxel structure to organize the point clouds, and then extracts the detrended geometric features from the local context of supervoxels. Combined with the detrended features of the local context, the Random Forest (RF) classifier will be adopted to obtain the initial semantic labeling results of trees from point clouds. Afterwards, a local context-based regularization is iteratively performed to achieve global optimum on a global graphical model, in order to spatially smoothing the semantic labeling results. Finally, a graph-based segmentation is conducted to separate individual trees according to the semantic labeling results. The use of supervoxel structure can preserve the geometric boundaries of objects in the scene, and compared with point-based solutions, the supervoxel-based method can largely decrease the number of basic elements during the processing. Besides, the introduction of supervoxel contexts can extract the local information of an object making the feature extraction more robust and representative. Detrended geometric features can get over the redundant and in-salient information in the local context, so that discriminative features are obtained. Benefiting from the regularization process, the spatial smoothing is obtained based on initial labeling results from classic classifications such as RF classification. As a result, misclassification errors are removed to a large degree and semantic labeling results are thus smoothed. Based on the constructed global graphical model during the spatially smoothing process, a graph-based segmentation is applied to partition the graphical model for the clustering the instances of trees. The experiments on two test datasets have shown promising results, with an accuracy of the semantic labeling of trees reaching around 0.9. The segmentation of trees using graph-based algorithm also show acceptable results, with trees having simple structures and sparse distributions correctly separated, but for those cramped trees with complex structures, the points are over- or under-segmented.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114851195","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":"Sensitivity Analysis on Performance of Different Unsupervised Threshold Selection Methods in Hyperspectral Change Detection","authors":"Mahdi Hasanlau, S. T. Seydi","doi":"10.1109/PRRS.2018.8486355","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486355","url":null,"abstract":"This paper investigated the performance of different automatic binary threshold selection methods on hyperspectral change detection. For this purpose, 10 recent and most common algorithm for binary threshold selection implemented namely and evaluated. To evaluate these methods first, the sub-space based hyperspectral change detection method applied on the multi-temporal hyperspectral dataset. In the second part, the gray level change map converts to binary change map by mentioned thresholding methods. In this work, real-world hyperspectral dataset utilized to evaluate the related performance of threshold selection methods. The results show that Active-Contour method has high efficiency in comparison to other methods with overall accuracy more than 93.53% and a kappa coefficient of 0.851.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129640519","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":"Multi-Modal Human Detection from Aerial Views by Fast Shape-Aware Clustering and Classification","authors":"Csaba Beleznai, Daniel Steininger, G. Croonen, Elisabeth Broneder","doi":"10.1109/PRRS.2018.8486236","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486236","url":null,"abstract":"Recognizing humans from aerial views represents an increasingly relevant endeavor; a trend mainly driven by the widespread use of unmanned aerial vehicles (UAVs). An accurate and real-time visual human recognition task, however, represents a scientific challenge because typical UAV imaging and computational capabilities and conditions introduce complexities and constraints. Motion blur, the non-specific top-view appearance of humans, low-image resolution and limited onboard computational resources are among the most important limiting factors to be considered. In this paper we propose a run-time-efficient multi-modal detection framework performing clustering and recognition on thermal infrared, passive stereo depth and intensity channels in order to cope with the above complexities and to achieve accurate human detection results. Thermal infrared and depth data are used to generate proposals in combination with an explicit, tree-structured shape representation driven clustering scheme. Generated proposals are used as an input for a discriminatively trained deep classification step to recognize humans. The proposed clustering and classification scheme is validated in qualitative and quantitative terms on four large aerial datasets representing complex (small objects, clutter, occlusions) situations.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125746746","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}