2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)最新文献_第7页

Tree species classification with hyperspectral imaging and lidar 利用高光谱成像和激光雷达进行树种分类

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071665

Ø. Rudjord, Ø. Trier

引用次数: 4

A regularized multi-metric active learning framework for hyperspectral image classification 高光谱图像分类的正则化多度量主动学习框架

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071724

Zhou Zhang, M. Crawford

引用次数: 0

Urban-industrial emissions monitoring with airborne longwave-infrared hyperspectral imaging 利用机载长波红外高光谱成像技术监测城市工业排放

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071711

D. Tratt, K. Buckland, E. Keim, P. Johnson

引用次数: 12

An iterative enhancement of higher order nonlinear mixture model for accurate hyperspectral unmixing 高精度高光谱解混高阶非线性混合模型的迭代增强

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071776

A. Marinoni, J. Plaza, A. Plaza, P. Gamba

引用次数: 0

Using image pyramids for the acceleration of spectral unmixing based on nonnegative matrix factorization 基于非负矩阵分解的图像金字塔加速光谱解混

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071705

S. Bauer, F. P. León

{"title":"Using image pyramids for the acceleration of spectral unmixing based on nonnegative matrix factorization","authors":"S. Bauer, F. P. León","doi":"10.1109/WHISPERS.2016.8071705","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071705","url":null,"abstract":"In the last couple of years, methods based on nonnegative matrix factorization (NMF) have been used for spectral unmixing of hyperspectral images. We propose a meta-method based on image pyramids for the acceleration of the unmixing calculation. Starting the factorization from a spatially coarse level, neighboring pixel spectra are averaged and considered as new pixel spectra. In the subsequent iterations, the resolution is increased step by step, which means that the previous lower resolution outcomes can be regarded as close-to-optimal starting points for the higher resolution iterations. By performing many steps on lower resolution levels, only few steps have to be calculated on the original size data. We will demonstrate the application of the new method, showing that for both spatial and spectral extensions of NMF, the proposed method in most cases leads to equal objective function values in less time. The unmixing calculation can be accelerated up to several times. Due to the fact that the objective functions of different NMF algorithms exhibit more or less local minima, not all NMF-based unmixing algorithms are equally well-suited for the application of the proposed method.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130073505","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

Total carbon mapping with hyperspectral unmixing techniques 用高光谱分解技术绘制全碳图

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071709

H. Soydan, A. Koz, H. S. Düzgün, Aydin Alatan

{"title":"Total carbon mapping with hyperspectral unmixing techniques","authors":"H. Soydan, A. Koz, H. S. Düzgün, Aydin Alatan","doi":"10.1109/WHISPERS.2016.8071709","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071709","url":null,"abstract":"Depending on the ground sampling distance of a remote sensor, a pixel of a spectral data cube is represented as a combination of the reflected signals of the materials which constitutes the observed pixel. Hyperspectral unmixing algorithms model the pixel of a data cube to determine and extract the spectral signatures of its components, namely endmembers, with their corresponding abundance fractions. This study first reviews the interaction and mitigation mechanisms of heavy metals with carbon content in soil, specifically due to coal mining activities and thermal plants. Such mechanism is then investigated with hyperspectral unmixing techniques by producing total carbon maps for an abandoned coal mine site. The utilized data for the study area is obtained on August 2013 with multispectral Worldview-2 satellite sensor. The acquired image is orthorectified and atmospherically corrected for radiance to reflectance conversion prior to the analysis. The soil samples are mainly collected from the problematic regions in terms of soil pollution. The samples are analyzed with LECO TrueSpec CHN_S device to measure total carbon levels, which are employed as ground truth to assess the performance of unmixing algorithms. The resulting abundance maps for carbon content are found to have a high compatibility with each other and the ground truth data, which effectively point out the regions of high carbon content.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131330895","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: 0

Performance evaluation of rotation forest for svm-based recursive feature elimination using hyperspectral imagery 基于svm的旋转森林高光谱图像递归特征消除性能评价

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071792

I. Colkesen, T. Kavzoglu

{"title":"Performance evaluation of rotation forest for svm-based recursive feature elimination using hyperspectral imagery","authors":"I. Colkesen, T. Kavzoglu","doi":"10.1109/WHISPERS.2016.8071792","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071792","url":null,"abstract":"Hyperspectral images provide important information for addressing complex classification problems required for a detailed characterization of spectral behavior of the target objects. Classification of such datasets into meaningful land use and land cover classes (LULC) has been the most concentrated topic in remote sensing arena. Rotation forest (RotFor), a new ensemble learning method, has been recently proposed as an alternative to conventional classifiers in the context of multispectral and hyperspectral image classification. In this study, the use of RotFor was investigated for the classification of hyperspectral imagery, specifically an AVIRIS image data. Support vector machine (SVM) was also used as a benchmark classifier. In order to select the best contributing bands of AVIRIS, support vector machine-recursive feature elimination (SVM-RFE) approach was applied. Results of this study showed that RotFor algorithm provided more accurate classification results than the SVM classifier with the use of smaller size data sets selected by SVM-RFE. Based on the Wilcoxon's signed-rank test, the performance difference between RotFor and SVM was found to be statistically significant.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115047641","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: 3

Random-projection-based nonnegative least squares for hyperspectral image unmixing 基于随机投影的非负最小二乘高光谱图像解混

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071796

V. Menon, Q. Du, J. Fowler

引用次数: 3

Sparse filtering based hyperspectral unmixing 基于稀疏滤波的高光谱解混

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071765

H. Aggarwal, A. Majumdar

引用次数: 2

Joint low rank and sparse representation-based hyperspectral image classification 基于联合低秩和稀疏表示的高光谱图像分类

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071748

Mengmeng Zhang, Wei Li, Q. Du

引用次数: 3