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

Spatial pattern of soil organic carbon acquired from hyperspectral imagery at reynolds creek critical zone observatory (RC-CZO) 雷诺溪临界带观测站(RC-CZO)高光谱影像土壤有机碳空间格局研究

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071812

Aihua Li, Ryan Will, N. Glenn, S. Benner, L. Spaete

{"title":"Spatial pattern of soil organic carbon acquired from hyperspectral imagery at reynolds creek critical zone observatory (RC-CZO)","authors":"Aihua Li, Ryan Will, N. Glenn, S. Benner, L. Spaete","doi":"10.1109/WHISPERS.2016.8071812","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071812","url":null,"abstract":"Soil Organic Carbon (SOC) is a key soil property and is important for understanding carbon storage and soil-vegetation dynamics. Hyperspectral imagery (imaging spectroscopy) providing detailed spectral signatures of vegetation and soil make it possible to continuously map SOC content over a watershed scale. In this paper, the Next Generation Airborne Visible / Infrared Imaging Spectrometer (AVTPJSng) was used with an unmixing algorithm, the Multiple Endmember Spectral Mixture Analysis, to differentiate fractional cover of healthy vegetation, stressed vegetation and soil at the Reynolds Creek Critical Zone Observatory (PC-CZO). The fractional cover information was used to remove noisy spectra and the resulting residual spectra were used to predict SOC by Partial Least Squares Regression (PLSP). The results showed that the root mean standard error and mean bias of the predicted SOC (%) are 0.75 and 2.4, respectively. We found the best relationship between SOC and spectra after filtering out the influence of green vegetation from mixed spectra. The resulting residual, spectra comprised of stressed vegetation and soil, contained enough information for mapping SOC distribution within the shrub dominated regions of the watershed. This may provide a method to better understand the interaction of soil and vegetation in semiarid ecosystems.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"40 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":"127128360","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

Investigation of the impact of dimensionality reduction and feature selection on the classification of hyperspectral EnMAP data 降维和特征选择对高光谱EnMAP数据分类的影响研究

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071759

S. Keller, A. Braun, S. Hinz, M. Weinmann

{"title":"Investigation of the impact of dimensionality reduction and feature selection on the classification of hyperspectral EnMAP data","authors":"S. Keller, A. Braun, S. Hinz, M. Weinmann","doi":"10.1109/WHISPERS.2016.8071759","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071759","url":null,"abstract":"In this paper, we address the classification of hyperspectral data which is comparable to the data acquired with the Environmental Mapping and Analysis Program (EnMAP) mission, a hyperspectral satellite mission supposed to be launched into space in the near future. While simulated EnMAP data has already been released, only relatively few studies have focused on investigating the performance of approaches for classifying such EnMAP data. Hence, in a recent paper, a contest for classifying EnMAP data has been initiated to foster research about possible exploitation strategies. Based on the dataset presented therein, we present a framework involving techniques of dimensionality reduction, feature selection and classification. We involve several classifiers for pixelwise classification based on different learning principles and investigate the impact of approaches for dimensionality reduction and feature selection on the classification results. The derived results clearly reveal the potential of respective techniques and provide the basis for further improvements in different research directions.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"8 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":"127175217","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: 17

Mineral absorption feature extraction in vegetation covered region based on reference spectral background removal 基于参考光谱背景去除的植被覆盖区矿物吸收特征提取

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071702

Hengqian Zhao, Lifu Zhang, Xuesheng Zhao

引用次数: 1

Oriented Triplet Markov fields for hyperspectral image segmentation 面向三重态马尔可夫场的高光谱图像分割

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071755

Jean-Baptiste Courbot, E. Monfrini, V. Mazet, C. Collet

引用次数: 4

A temperature and emissivity retrieval algorithm based on atmospheric absorption feature from hyperspectral thermal infrared data 基于大气吸收特征的高光谱热红外数据温度和发射率检索算法

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071785

Chen Mengshuo, Qian Yonggang, Wu Hua, Wang Ning, Ma Lingling, Li Chuanrong, Tang Lingli

引用次数: 0

Classification and anomaly detection algorithms for weak hyperspectral signal processing 弱高光谱信号处理的分类与异常检测算法

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071758

P. Lahaie

{"title":"Classification and anomaly detection algorithms for weak hyperspectral signal processing","authors":"P. Lahaie","doi":"10.1109/WHISPERS.2016.8071758","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071758","url":null,"abstract":"In applications involving weak light signal like hyperspectral or time distributed signals obtained in applications involving laser induced fluorescence spectral detection, fluorescence lifetime imaging, Raman Spectroscopy or hyperspectral imaging in low light environment, the photons arrive at such a rate that they can be counted or have to be intensified to obtain a usable signal. Detection and classification algorithms need to be designed and evaluated for weak hyperspectral signal processing. A new algorithm, Adaptive Shot Noise (ASN) based on the assumption that a signal respects the Poisson multivariate distribution has been developed using the method of the maximum likelihood. This algorithm demonstrates the capability to be used for detection and classification. Using Monte Carlo simulations its performances are compared with the Adaptive Coherence Estimator (ACE) classification and with an Integrated Signal Algorithm (ISA) and ACE for detection. This new algorithm provides a small increase in performance compared to ACE in very weak signal conditions for classification and in some conditions better performance over both ACE and ISA in detection. The algorithm behavior like ACE shows sensitivity to assumption on the spectral characteristics of the source for the detection, which is not the case for ISA.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"6 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":"132353288","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

Subsurface linear unmixing on a controlled underwater enviroment 在受控的水下环境下进行地下线性分解

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071788

E. Carpena-Colon, Luis O. Jimenez-Rodriguez, Emmanuel Arzuaga, M. Velez-Reyes

引用次数: 0

Hyperspectral image destriping using unmixing-based kriging interpolation 基于非混合克里格插值的高光谱图像去条纹

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071669

Cencen Pan, Kun Tan, Q. Du, Qinwu Yan, Jianwei Ding

引用次数: 3

Quality improvement of hyperspectral remote sensing images: A technical overview 高光谱遥感影像质量改进技术综述

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071695

Huifang Li, Huanfeng Shen, Q. Yuan, Hongyan Zhang, Lefei Zhang, Liangpei Zhang

引用次数: 1

Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields 基于光谱角的一元能量函数的空间光谱高光谱马尔可夫随机场分类

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI: 10.1109/WHISPERS.2016.8071716

Utsav B. Gewali, S. Monteiro

引用次数: 4