2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)最新文献

Fast extraction of dominant planes in MLS-data of urban areas 城市mls数据优势面快速提取

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867021

M. Gordon

引用次数: 0

Evaluating imaging quality of the offner hyperspectrometer offner高光谱仪成像质量评价

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867020

A. Nikonorov, M. Petrov, P. Yakimov, V. Blank, S. Karpeev, R. Skidanov, N. Kazanskiy

引用次数: 2

River sediment yield classification using remote sensing imagery 基于遥感影像的河流产沙量分类

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867014

R. Pisani, K. Costa, Gustavo de Rosa, D. Pereira, J. Papa, J.M.R.S. Tavares

引用次数: 3

Segmentation of 3D outdoor scenes using hierarchical clustering structure and perceptual grouping laws 基于层次聚类结构和感知分组规律的三维户外场景分割

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867013

Yusheng Xu, S. Tuttas, Uwe Stilla

引用次数: 14

Towards vegetation species discrimination by using data-driven descriptors 基于数据驱动描述符的植被物种识别研究

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867024

Keiller Nogueira, J. A. dos Santos, Tamires Fornazari, Thiago Sanna Freire Silva, L. Morellato, R. D. S. Torres

引用次数: 21

A comprehensive study on object proposals methods for vehicle detection in aerial images 对航拍图像中目标的综合研究提出了航拍图像中车辆检测的方法

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867018

L. Sommer, T. Schuchert, J. Beyerer

{"title":"A comprehensive study on object proposals methods for vehicle detection in aerial images","authors":"L. Sommer, T. Schuchert, J. Beyerer","doi":"10.1109/PRRS.2016.7867018","DOIUrl":"https://doi.org/10.1109/PRRS.2016.7867018","url":null,"abstract":"Detecting vehicles in aerial images is an important task in many applications such as traffic monitoring or screening of large areas. In general, vehicle detection in aerial images is performed by applying classifiers or a cascade of classifiers within a sliding window algorithm. However, detecting vehicles in a real-time system is limited by the huge number of windows to classify, especially in case of varying object scales, aspect ratios or object orientations. To reduce the high number of windows, we propose to apply so called object proposals methods. In recent years, several object proposals methods have been proposed for generating candidate windows in detection frameworks. However, aerial images differ considerably from datasets that are typically used for exploring such methods. To examine the applicability of such methods for aerial images, we evaluate 11 state-of-the-art object proposals methods on the publicly available DLR 3K Munich Vehicle Aerial Image Dataset. First, we manually modified the provided ground truth data to enable comparison to the generated object proposals. To compensate for the differing characteristics of the aerial images, we adapted seven methods by examining different parameter settings and extensions for each method separately. Finally, we demonstrate the potential of such methods for a detection framework for aerial images as significantly fewer candidate windows are generated in comparison to sliding window.","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":"124618568","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: 8

Semisupervised classification of hyperspectral remote sensing images with spatial majority voting 基于空间多数投票的高光谱遥感图像半监督分类

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867023

Deok Han, Q. Du, N. Younan

引用次数: 2

Discriminative archetypal self-taught learning for multispectral landcover classification 多光谱土地覆盖分类的判别原型自学

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867022

R. Roscher, Susanne Wenzel, B. Waske

{"title":"Discriminative archetypal self-taught learning for multispectral landcover classification","authors":"R. Roscher, Susanne Wenzel, B. Waske","doi":"10.1109/PRRS.2016.7867022","DOIUrl":"https://doi.org/10.1109/PRRS.2016.7867022","url":null,"abstract":"Self-taught learning (STL) has become a promising paradigm to exploit unlabeled data for classification. The most commonly used approach to self-taught learning is sparse representation, in which it is assumed that each sample can be represented by a weighted linear combination of elements of a unlabeled dictionary. This paper proposes discriminative archetypal self-taught learning for the application of landcover classification, in which unlabeled discriminative archetypal samples are selected to build a powerful dictionary. Our main contribution is to present an approach which utilizes reversible jump Markov chain Monte Carlo method to jointly determine the best set of archetypes and the number of elements to build the dictionary. Experiments are conducted using synthetic data, a multi-spectral Landsat 7 image of a study area in the Ukraine and the Zurich benchmark data set comprising 20 multispectral Quickbird images. Our results confirm that the proposed approach can learn discriminative features for classification and show better classification results compared to self-taught learning with the original feature representation and compared to randomly initialized archetypal dictionaries.","PeriodicalId":327270,"journal":{"name":"2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)","volume":"9 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":"114053760","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

Point cloud registration by combining shape and intensity contexts 结合形状和强度上下文的点云配准

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867025

F. Wang, Y. Ye, Xiangyun Hu, J. Shan

引用次数: 5

4D change detection based on persistent scatterer interferometry 基于持续散射干涉法的四维变化检测

2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) Pub Date : 2016-12-04 DOI: 10.1109/PRRS.2016.7867016

Chia-Hsiang Yang, B. Kenduiywo, U. Soergel

{"title":"4D change detection based on persistent scatterer interferometry","authors":"Chia-Hsiang Yang, B. Kenduiywo, U. Soergel","doi":"10.1109/PRRS.2016.7867016","DOIUrl":"https://doi.org/10.1109/PRRS.2016.7867016","url":null,"abstract":"We propose a novel time series analysis based on persistent scatterer interferometry (PSI) to detect spatial big changes (3D) such as construction along with their occurrence times (1D). PSI detects and analyses persistent scatterer (PS) points, which are characterized by strong and coherent signals throughout time-series SAR images and usually form building-shaped patterns in urban areas. Hence, potential PS points that disappear or emerge at a specific date because of big changes are discarded. We define such points as big change (BC) points. In our approach, pixels with high temporal coherences are first detected as PS points by a standard PSI processing. We introduce change index sequence for each pixel, which are computed from its temporal coherences in different image subsets defined by time-series break dates, to quantify its probabilities of being BC points at different dates. The change indices of the pixels are used to design an automatic thresholding method to extract BC points. Afterwards, the disappearing or emerging date of each BC point is detected from the break dates based on temporal variation in its change index sequence. The simulation test proves the overall, producer's and user's accuracies better than 99%. In the real data test, the patterns of the disappearing and emerging buildings are successfully recognized in Berlin, Germany along with the occurrence dates.","PeriodicalId":327270,"journal":{"name":"2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)","volume":"62 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":"128204817","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