Hyperspectral Feature Research Articles

Tree species classification using plant functional traits from LiDAR and hyperspectral data

Plant functional traits have been extensively used to describe, rank and discriminate species according to their variability between species in classical plant taxonomy. However, the utility of plant functional traits for tree species classification from remote sensing data in natural forests has not been clearly established. In this study, we integrated three selected plant functional traits (i.e. equivalent water thickness (Cw), leaf mass per area (Cm) and leaf chlorophyll (Cab)) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species in a natural forest in Germany. Our results showed that when plant functional traits were combined with spectral features and LiDAR metrics, an overall accuracy of 83.7% was obtained, which was statistically significantly higher than using LiDAR (65.1%) or hyperspectral (69.3%) data alone. The results of our study demonstrate that plant functional traits retrieved from hyperspectral data using radiative transfer models can be used in conjunction with hyperspectral features and LiDAR metrics to further improve individual tree species classification in a mixed temperate forest.

Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat.

Hyperspectral remote sensing is a rapid non-destructive method for diagnosing nitrogen status in wheat crops. In this study, a quantitative correlation was associated with following parameters: leaf nitrogen accumulation (LNA), raw hyperspectral reflectance, first-order differential hyperspectra, and hyperspectral characteristics of wheat. In this study, integrated linear regression of LNA was obtained with raw hyperspectral reflectance (measurement wavelength = 790.4 nm). Furthermore, an exponential regression of LNA was obtained with first-order differential hyperspectra (measurement wavelength = 831.7 nm). Coefficients (R2) were 0.813 and 0.847; root mean squared errors (RMSE) were 2.02 g·m−2 and 1.72 g·m−2; and relative errors (RE) were 25.97% and 20.85%, respectively. Both the techniques were considered as optimal in the diagnoses of wheat LNA. Nevertheless, the better one was the new normalized variable (SDr − SDb)/(SDr + SDb), which was based on vegetation indices of R2 = 0.935, RMSE = 0.98, and RE = 11.25%. In addition, (SDr − SDb)/(SDr + SDb) was reliable in the application of a different cultivar or even wheat grown elsewhere. This indicated a superior fit and better performance for (SDr − SDb)/(SDr + SDb). For diagnosing LNA in wheat, the newly normalized variable (SDr − SDb)/(SDr + SDb) was more effective than the previously reported data of raw hyperspectral reflectance, first-order differential hyperspectra, and red-edge parameters.

SUBPIXEL MAPPING OF HYPERSPECTRAL IMAGE BASED ON LINEAR SUBPIXEL FEATURE DETECTION AND OBJECT OPTIMIZATION

Abstract. Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Feature Adaptation and Augmentation for Cross-Scene Hyperspectral Image Classification

Cross-scene hyperspectral image (HSI) classification has recently become increasingly popular due to its crucial use in various applications. It poses great challenges to existing domain adaptation methods because of the data set shift, that is, two scenes exhibit huge distribution discrepancy. To tackle this problem, we propose a new domain adaptation method called hyperspectral feature adaptation and augmentation (HFAA) for cross-scene HSI classification. The proposed HFAA method learns a common subspace by introducing two different projection matrices to extract the transferable knowledge from the source domain to the target domain. To further enhance the common subspace representation, we propose to augment it by the feature selection strategy. HFAA can make full use of the original features from both source and target domains, and increase the similarity of the samples with the same label from the two domains. Our proposed HFAA method achieves compact but discriminative feature representations, which make it well suited for data sets with a large number of classes and huge interclass ambiguity. Experimental results on the Earth Observing 1 hyperspectral data set show that HFAA can produce state-of-the-art performance and surpass previous methods.

Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host–Pathogen Interaction of Yellow Rust on Wheat

Understanding the progression of host–pathogen interaction through time by hyperspectral features is vital for tracking yellow rust (Puccinia striiformis) development, one of the major diseases of wheat. However, well-designed features are still open issues that impact the performance of relevant models to nondestructively detect pathological progress of wheat rust. The aim of this paper is (1) to propose a novel wavelet-based rust spectral feature set (WRSFs) to uncover wheat rust-related processes; and (2) to evaluate the performance and robustness of the proposed WRSFs and models for retrieving the progression of host–pathogen interaction and tracking rust development. A hyperspectral dataset was collected by analytical spectral devices (ASD) spectroradiometer and Headwall spectrograph, along with corresponding physiological measurements of chlorophyll index (CHL), nitrogen balance index (NBI), anthocyanin index (ANTH), and percentile dry matter (PDM) from the 7th to 41st day after inoculation (dai) under controlled conditions. The resultant findings suggest that the progression of yellow rust on wheat is better characterized by the proposed WRSFs (R2 > 0.7). The WRSFs-based PLSR model provides insight into specific leaf biophysical variations in the rust pathological progress. To evaluate the efficiency of the proposed WRSFs on yellow rust discrimination during different infestation stages, the identified WRSFs and vegetation indices (VIs) were fed into linear discriminant analysis (LDA) and support vector machine (SVM) classification frames. The WRSFs in conjunction with a SVM classifier can obtain better performance than that of LDA method and the VIs-based models. Overall, synthesizing the biophysical analysis, retrieving accuracy, and classification performance, we recommend the proposed WRSFs for monitoring the progression of the host–pathogen interaction of yellow rust on wheat cross various hyperspectral sensors.

Research on Tomato Nitrogen Content Nondestructive Testing Method Based on Multidimensional Image Processing Technology

This paper is aimed at greenhouse tomato nitrogen detection using hyperspectral imaging combined with three dimensional laser scanning technology. This technology extracts the nitrogen hyperspectral feature image and the plant three dimensional morphological characters, to achieve the rapid quantitative analysis of nitrogen in tomato. The characteristic spectrum of nitrogen was extracted, and the mean intensity characteristic of the image feature was obtained. Then based on the acquisition of the tomato hyperspectral image data cube at different nitrogen levels, the sensitive region stepwise regression combined with correlation analysis was performed. Based on the acquired three dimensional laser scanning data of tomatoes, the stem diameter, the plant height and other biomass characteristics of different nitrogen levels were obtained by establishing the spatial geometric model of tomato three dimensional point cloud. A multi-feature fusion model for tomato nitrogen detection was established by partial least square regression. The results showed that the R2 in the constructed model was 0.94, with the accuracy significantly better than that of the single feature model established by using hyperspectral image and three dimensional laser scanning.

Analysis and Identification of Raw Egg Yolk with the Hyperspectral Characteristics

The source of eggs is very rich and wide varieties, at the same time it is also an important source of nutrition for human body. But there is an important problem how to identify the quality and how to build the traceability system of eggs. This paper studied the classification and the freshness of eggs from hyperspectral characteristics of egg yolks. The spectral data of the yolks were collected by using the high resolution spectrometer and the data processing and comparative analysis operations were carried out. Finally, some beneficial conclusions are obtained, which provides new idea and methods for the quality testing of eggs.

Multisensor Analysis of Spectral Dimensionality and Soil Diversity in the Great Central Valley of California.

Planned hyperspectral satellite missions and the decreased revisit time of multispectral imaging offer the potential for data fusion to leverage both the spectral resolution of hyperspectral sensors and the temporal resolution of multispectral constellations. Hyperspectral imagery can also be used to better understand fundamental properties of multispectral data. In this analysis, we use five flight lines from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) archive with coincident Landsat 8 acquisitions over a spectrally diverse region of California to address the following questions: (1) How much of the spectral dimensionality of hyperspectral data is captured in multispectral data?; (2) Is the characteristic pyramidal structure of the multispectral feature space also present in the low order dimensions of the hyperspectral feature space at comparable spatial scales?; (3) How much variability in rock and soil substrate endmembers (EMs) present in hyperspectral data is captured by multispectral sensors? We find nearly identical partitions of variance, low-order feature space topologies, and EM spectra for hyperspectral and multispectral image composites. The resulting feature spaces and EMs are also very similar to those from previous global multispectral analyses, implying that the fundamental structure of the global feature space is present in our relatively small spatial subset of California. Finally, we find that the multispectral dataset well represents the substrate EM variability present in the study area – despite its inability to resolve narrow band absorptions. We observe a tentative but consistent physical relationship between the gradation of substrate reflectance in the feature space and the gradation of sand versus clay content in the soil classification system.

The objects identification on the basis of their hyperspectral features using the SVM classifiers' ensembles

The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers’ ensembles, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction.

Researchers have studied oil spills in open waters using remote sensors, but few have focused on extracting reflectance features of oil pollution on sea ice. An experiment was conducted on natural sea ice in Bohai Bay, China, to obtain the spectral reflectance of oil-contaminated sea ice. The spectral absorption index (SAI), spectral peak height (SPH), and wavelet detail coefficient (DWT d5) were calculated using stepwise multiple linear regression. The reflectances of some false targets were measured and analysed. The simulated false targets were sediment, iron ore fines, coal dust, and the melt pool. The measured reflectances were resampled using five common sensors (GF-2, Landsat8-OLI, Sentinel3-OLCI, MODIS, and AVIRIS). Some significant spectral features could discriminate between oil-polluted and clean sea ice. The indices correlated well with the oil area fractions. All of the adjusted R2 values exceeded 0.9. The SPH model1, based on spectral features at 507–670 and 1627–1746 nm, displayed the best fitting. The resampled data indicated that these multi-spectral and hyper-spectral sensors could be used to detect crude oil on the sea ice if the effect of noise and spatial resolution are neglected. The spectral features and their identified changes may provide reference on sensor design and band selection.

干旱胁迫下雷竹叶片叶绿素的高光谱响应特征及含量估算

干旱胁迫下雷竹叶片叶绿素的高光谱响应特征及含量估算

夜间增温对稻田甲烷排放的影响及其高光谱估算

PDF HTML阅读 XML下载 导出引用 引用提醒 夜间增温对稻田甲烷排放的影响及其高光谱估算 DOI: 10.5846/stxb201707201304 作者: 作者单位: 南京信息工程大学气象灾害预报预警与评估协同创新中心/江苏省农业气象重点实验室,南京信息工程大学气象灾害预报预警与评估协同创新中心/江苏省农业气象重点实验室,南京信息工程大学气象灾害预报预警与评估协同创新中心/江苏省农业气象重点实验室,南京信息工程大学气象灾害预报预警与评估协同创新中心/江苏省农业气象重点实验室,南京信息工程大学气象灾害预报预警与评估协同创新中心/江苏省农业气象重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41375159）；江苏省自然科学基金项目（BK20131430） Methane emission in response to nighttime warming and its hyperspectral estimation in a paddy field Author: Affiliation: Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:昼夜不对称增温是全球气候变化的主要特征之一，有关夜间增温对稻田甲烷（CH4）排放影响的报道尚不多见。通过田间模拟试验，研究了被动式夜间增温下水稻田CH4排放及高光谱的特征，并用高光谱数据对稻田甲烷排放进行定量模拟。田间试验设夜间增温（NW）和对照处理（CK），夜间增温即在整个水稻生育期的夜间（19：00-6：00）用铝箔反射膜覆盖水稻冠层。结果表明，夜间增温显著促进水稻拔节期和抽穗期-灌浆期CH4排放。水稻冠层近红外光谱反射率表现为，在分蘖期和拔节期时，NW>CK；而在抽穗-灌浆期和成熟期时，CK>NW。水稻冠层光谱反射率、一阶导数光谱及光谱特征值均与CH4排放通量显著相关，相关系数最大可达0.8（P < 0.01），其中以"蓝边面积"（SDb）构成的二次多项式模型模拟精度和检验精度综合最佳，决定系数R2分别为0.70和0.72。研究结果对稻田CH4排放通量遥感监测的可行性提供了理论依据和技术支持。 Abstract:Asymmetric diurnal warming is one of the main features of global climate change, but the effects of nighttime warming on CH4emission from paddy fields is unclear. A field experiment with rice was conducted at the Station of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing, China, to investigate the effects of passive nighttime warming on CH4 emission in a paddy field and rice canopy hyperspectral characteristics. CH4 emission flux was estimated with hyperspectral data. The tested rice was hybrid rice cv. Y Liangyou 3399. The tested paddy soil was classified as a Typic Stagnic Anthrosol. The experiment was designed with two levels of warming, i.e., nighttime warming(NW) and control(CK). NW was created by covering the rice canopy with an aluminum foil reflective film at night(19:00-6:00).CH4 emission was measured by the closed chamber method at one-week intervals during the rice-growing period. The results showed that NW significantly increased CH4 emission at the rice jointing and heading-grain filling stages. Compared to CK, NW increased the reflectance of the near-infrared spectrum on the canopy at the tillering and jointing stages, but decreased at the heading-grain filling and maturity stages. Positive correlations were observed in the relationships of CH4 emission with spectral reflectance and the first derivative and the characteristic value of the spectrum. After comparing correlation coefficients (R2) of the fitting models and prediction models, the quadratic polynomial model by 'Area of Blue Edge' (SDb) was found to be the best model(fitting model R2=0.70, prediction model R2=0.72) for estimating CH4 emission. The results of this study provide a theoretical basis for and demonstrate the feasibility of non-destructively monitoring of CH4 emission in paddy fields. 参考文献 相似文献 引证文献

A New Unsupervised Hyperspectral Band Selection Method Based on Multiobjective Optimization

Unsupervised band selection methods usually assume specific optimization objectives, which may include band or spatial relationship. However, since one objective could only represent parts of hyperspectral characteristics, it is difficult to determine which objective is the most appropriate. In this letter, we propose a new multiobjective optimization-based band selection method, which is able to simultaneously optimize several objectives. The hyperspectral band selection is transformed into a combinational optimization problem, where each band is represented by a binary code. More importantly, to overcome the problem of unique solution selection in traditional multiobjective methods, we develop a new incorporated rank-based solution set concentration approach in the process of Tchebycheff decomposition. The performance of our method is evaluated under the application of hyperspectral imagery classification. Three recently proposed band selection methods are compared.

Hyper spectral characteristics and estimation model of leaf chlorophyll content in cotton under waterlogging stress.

In order to rapidly monitor chlorophyll content in cotton functional leaf, and establish the quantitative relationship between chlorophyll content and spectral characteristic parameter of single cotton leaf, cotton was pot cultivated in a rain shelter and subjected to waterlogging at squaring stage. Cotton leaf samples were taken and measured every 3 days after waterlogging. The correlation between chlorophyll content and spectral characteristic parameter was synthetically analyzed, and then the estimation model of chlorophyll content was established and verified. The results showed that the chlorophyll content decreased with increasing waterlogging stress. The original spectral reflectance and first order differential spectral reflectance was negatively correlated with the chlorophyll content in the band near 580 and 697 nm. The estimation model established by difference vegetation index and normalized difference vegetation index performed better than that established by linear model of single band. Furthermore, the estimation model with (DR697-DR738)/(DR697+DR738) as the independent variable fitted the best with the correlation coefficient of 0.814, which could be utilized to estimate chlorophyll content of single leaf under waterlogging stress.

Urban features classification based on objects segmentation and hyperspectral characteristics

Urban features classification based on objects segmentation and hyperspectral characteristics

Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data

Mapping tree species within urban areas is essential for sustainable urban planning as well as to improve our understanding of the role of urban vegetation as an ecological service. Urban trees contribute significantly in mitigating the urban heat island effect and supporting biodiversity. However, accurate and up-to-date mapping of urban tree species is difficult because of the time-consuming nature of field sampling, fine-scale spatial variation, and potentially high species diversity. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) with high pulse density (25point/m2) and hyperspectral imagery offer two different yet complementary approaches to estimating crown structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, we evaluate the potential of these technologies to map 15 common urban tree species using a Random Forest (RF) classifier in the City of Surrey, British Columbia, Canada. LiDAR-derived crown structural information was combined with hyperspectral-derived spectral vegetation indices for species classification. Results indicate an overall accuracy of 51.1%, 61.0%, and 70.0% using hyperspectral, LiDAR and the combined data respectively. The overall accuracy for the two most important and iconic native coniferous species improved markedly from 78.3% up to 91% using the combined data. The results of this research highlight that (1) the combination of structural and spectral information provided an improved classification accuracy than when used separately, and variables derived from LiDAR data contributed more to the accurate prediction of species than hyperspectral features; (2) higher classification accuracies were observed for evergreen species, species with distinguishable crown structure, and species undergoing flowering; (3) and finally the anthocyanin content index and photochemical reflectance index were the most important hyperspectral features for the discrimination of tree species in the spring budburst stage.

Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network

This paper proposes a novel framework for the fusion of hyperspectral and light detection and ranging-derived rasterized data using extinction profiles (EPs) and deep learning. In order to extract spatial and elevation information from both the sources, EPs that include different attributes (e.g., height, area, volume, diagonal of the bounding box, and standard deviation) are taken into account. Then, the derived features are fused via either feature stacking or graph-based feature fusion. Finally, the fused features are fed to a deep learning-based classifier (convolutional neural network with logistic regression) to ultimately produce the classification map. The proposed approach is applied to two datasets acquired in Houston, TX, USA, and Trento, Italy. Results indicate that the proposed approach can achieve accurate classification results compared to other approaches. It should be noted that, in this paper, the concept of deep learning has been used for the first time to fuse LiDAR and hyperspectral features, which provides new opportunities for further research.

Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers

Hyperspectral signatures can provide abundant information regarding health status of crops; however it is difficult to discriminate between biotic and abiotic stress. In this study, the case of simultaneous occurrence of yellow rust disease symptoms and nitrogen stress was investigated by using hyperspectral features from a ground based hyperspectral imaging system. Hyperspectral images of healthy and diseased plant canopies were taken at Rothamsted Research, UK by a Specim V10 spectrograph. Five wavebands of 20 nm width were utilized for accurate identification of each of the stress and healthy plant conditions. The technique that was developed used a hybrid classification scheme consisting of hierarchical self organizing classifiers. Three different architectures were considered: counter-propagation artificial neural networks, supervised Kohonen networks (SKNs) and XY-fusion. A total of 12 120 spectra were collected. From these 3 062 (25.3%) were used for testing. The results of biotic and abiotic stress identification appear to be promising, reaching more than 95% for all three architectures. The proposed approach aimed at sensor based detection of diseased and stressed plants so that can be treated site specifically contributing to a more effective and precise application of fertilizers and fungicides according to specific plant’s needs.

Algorithms of the Objects’ Identification According to Hyperspectral Shooting of the „Resource-P“ Spacecraft

The identification algorithms, which carry out the objects’ identification of the Earth’s surface by means of their hyperspectral features’ analysis, received on the base of the processed space images from the „Resource-P” spacecrafts with application of the similarity measures, have been considered. The identification algorithms on the base of the Euclidean distance similarity measure, the angular similarity measure and the fuzzy similarity measure have been applied. The use expediency of the fuzzy linear regression in the algorithm of objects’ hyperspectral features’ identification has been shown. The results of the hyperspectral information processing with using of the offered algorithms have been presented.

SVM Classifiers: The Objects Identification on the Base of Their Hyperspectral Features

The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.