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

Multi-Modal Remote Sensing Image Registration Based on Multi-Scale Phase Congruency 基于多尺度相位一致性的多模态遥感图像配准

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486287

Song Cui, Yanfei Zhong

引用次数: 6

Reconstructing Lattices from Permanent Scatterers on Facades 从立面上的永久散射体重建网格

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486322

E. Michaelsen, U. Soergel

引用次数: 1

Using a VGG-16 Network for Individual Tree Species Detection with an Object-Based Approach 基于对象方法的VGG-16网络树种检测

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486395

M. Rezaee, Yun Zhang, Rakesh K. Mishra, Fei Tong, Hengjian Tong

引用次数: 18

Collaborative Classification of Hyperspectral and LIDAR Data Using Unsupervised Image-to-Image CNN 使用无监督图像对图像CNN的高光谱和激光雷达数据协同分类

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486164

Mengmeng Zhang, Wei Li, Xueling Wei, Xiang Li

引用次数: 3

The UAV Image Classification Method Based on the Grey-Sigmoid Kernel Function Support Vector Machine 基于灰色- s型核函数支持向量机的无人机图像分类方法

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486193

Pel Pengcheng, Shi Yue, Wan ChengBo, Ma Xinming, Guo Wa, Qiao Rongbo

引用次数: 0

Deep Learning Integrated with Multiscale Pixel and Object Features for Hyperspectral Image Classification 基于多尺度像素和目标特征的深度学习高光谱图像分类

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486304

Meng Zhang, L. Hong

{"title":"Deep Learning Integrated with Multiscale Pixel and Object Features for Hyperspectral Image Classification","authors":"Meng Zhang, L. Hong","doi":"10.1109/PRRS.2018.8486304","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486304","url":null,"abstract":"The spectral resolution and spatial resolution of hyperspectral images are continuously improving, providing rich information for interpreting remote sensing image. How to improve the image classification accuracy has become the focus of many studies. Recently, Deep learning is capable to extract discriminating high-level abstract features for image classification task, and some interesting results have been acquired in image processing. However, when deep learning is applied to the classification of hyperspectral remote sensing images, the spectral-based classification method is short of spatial and scale information; the image patch-based classification method ignores the rich spectral information provided by hyperspectral images. In this study, a multi-scale feature fusion hyperspectral image classification method based on deep learning was proposed. Firstly, multiscale features were obtained by multi-scale segmentation. Then multiscale features were input into the convolution neural network to extract high-level features. Finally, the high-level features were used for classification. Experimental results show that the classification results of the fusion multi-scale features are better than the single-scale features and regional feature classification results.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"15 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":"133976399","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}

引用次数: 2

Fine Registration of Mobile and Airborne LiDAR Data Based on Common Ground Points 基于公共地面点的移动和机载激光雷达数据精细配准

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486181

Yanming Chen, Xiaoqiang Liu, Mengru Yao, Liang Cheng, Manchun Li

{"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}

引用次数: 2

Automatic Identification of Soil Layer from Borehole Digital Optical Image and GPR Based on Color Features 基于颜色特征的钻孔数字光学图像与探地雷达的土层自动识别

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486325

L. Li, C. Yu, T. Sun, Z. Han, X. Tang

{"title":"Automatic Identification of Soil Layer from Borehole Digital Optical Image and GPR Based on Color Features","authors":"L. Li, C. Yu, T. Sun, Z. Han, X. Tang","doi":"10.1109/PRRS.2018.8486325","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486325","url":null,"abstract":"For the high-resolution borehole image obtained by digital panoramic borehole camera system, a method for recognizing soil layer based on color features is proposed. Due to the obvious difference in color between soil layer and common rock layer, a soil layer detection model based on HSV color space is established. The binarized image of soil layer is obtained by using this model. Secondly, the binary image is filtered to depress the noise effects. Then, the binarized image of the soil layer is divided and the density of pixels in each segmentation is calculated to determine the depth, area and direction of the soil layer, so that the identification of soil layer in the digital borehole image can be achieved. Through verifying this method with many actual borehole images and comparing them with the corresponding borehole radar images, the result illustrate that this method can identify all of the soil layer throughout the whole borehole digital optical image automatically and quickly. It provides a new reliable method for the automatic identification of borehole structural planes in engineering application.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"69 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":"115272104","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}

引用次数: 1

DispNet Based Stereo Matching for Planetary Scene Depth Estimation Using Remote Sensing Images 基于disnet的遥感影像行星景深立体匹配

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486195

Qingling Jia, Xue Wan, Baoqin Hei, Shengyang Li

引用次数: 4

An Encoding-Based Back Projection Algorithm for Underground Holes Detection via Ground Penetrating Radar 一种基于编码的探地雷达地下孔探测反投影算法

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486182

Shaokun Zhang, Zhiyou Hong, Yiping Chen, Zejian Kang, Zhipeng Luo, Jonathan Li

引用次数: 0