The Experts below are selected from a list of 13881 Experts worldwide ranked by ideXlab platform
Chenghai Yang - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Hyperspectral Imagery with aerial photography and multispectral Imagery for mapping broom snakeweed
International Journal of Remote Sensing, 2010Co-Authors: Chenghai Yang, James H. EverittAbstract:Broom snakeweed Gutierrezia sarothrae Pursh Britt. & Rusby is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne Hyperspectral Imagery and compare it with aerial colour-infrared CIR photography and multispectral digital Imagery for mapping broom snakeweed infestations. Airborne Hyperspectral Imagery along with aerial CIR photographs and digital CIR images was acquired from a rangeland area in south Texas. The Hyperspectral Imagery was transformed using minimum noise fraction MNF and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper SAM classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image; these were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracies for three-class classification maps snakeweed, mixed woody and mixed herbaceous were 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed Hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne Hyperspectral Imagery along with aerial photography and multispectral Imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands.
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Comparison of airborne multispectral and Hyperspectral Imagery for mapping cotton root rot.
Biosystems Engineering, 2010Co-Authors: Chenghai Yang, James H. Everitt, Carlos J. FernandezAbstract:Cotton root rot, caused by the soilborne fungus Phymatotrichum omnivorum, is a major cotton disease in the south western and south central United States. Accurate delineation of root rot infestations is necessary for site-specific management of the disease. The objective of this study was to compare airborne multispectral and Hyperspectral Imagery for detecting and mapping root rot areas in cotton fields. Two centre-pivot irrigated fields in south Texas were selected for this study. Airborne 3-band multispectral Imagery and 128-band Hyperspectral Imagery were taken from the two fields shortly before harvest when infested areas were fully expressed for the season. Both types of Imagery and the principal component images derived from the Hyperspectral Imagery were classified into 2-10 spectral classes using unsupervised classification techniques. The individual spectral classes were then grouped into infested and non-infested zones. Accuracy assessment on the two-zone classification maps showed that both types of Imagery as well as the principal component Imagery equally accurately identified root rot areas within the fields. These results indicate that both airborne multispectral and Hyperspectral Imagery can be successfully used for assessing root rot infestations within cotton fields. The classification maps and buffer maps as well as the image processing procedures presented in this study can be readily used by cotton growers and consultants for the site-specific cultural and chemical management of this disease.
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Airborne Hyperspectral Imagery for Mapping Crop Yield Variability
Geography Compass, 2009Co-Authors: Chenghai YangAbstract:Information concerning the spatial variation in crop yield has become necessary for site-specific crop management. Traditional satellite Imagery has long been used to monitor crop growing conditions and to estimate crop yields over large geographic areas. However, this type of Imagery has limited use for assessing within-field yield variability because of its coarse spatial resolution. Therefore, high-resolution airborne multispectral and Hyperspectral Imagery has been used for this purpose. This study presents an overview on the use of remote sensing Imagery for mapping crop yield variability and illustrates how airborne Hyperspectral Imagery can be used for crop yield estimation based on the work conducted at the US Department of Agriculture’s Kika de la Garza Subtropical Agricultural Research Center at Weslaco, Texas. Some of the challenges and considerations on the use of Hyperspectral Imagery for yield mapping are discussed.
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Comparison of Airborne Multispectral and Hyperspectral Imagery for Estimating Grain Sorghum Yield
Transactions of the ASABE, 2009Co-Authors: Chenghai Yang, James H. Everitt, Joe M. Bradford, D. MurdenAbstract:Both multispectral and Hyperspectral images are being used to monitor crop conditions and map yield variability, but limited research has been conducted to compare these two types of Imagery for assessing crop growth and yields. The objective of this study was to compare airborne multispectral Imagery with airborne Hyperspectral Imagery for mapping yield variability in grain sorghum fields. Airborne color-infrared (CIR) Imagery and airborne Hyperspectral Imagery along with yield monitor data collected from four fields were used in this study. Three-band Imagery with wavebands corresponding to the collected CIR Imagery and four-band Imagery with wavebands similar to QuickBird satellite Imagery were generated from the 102-band Hyperspectral Imagery. All four types of Imagery (two actual and two simulated) were aggregated to increase pixel size to match the yield data resolution. Principal components and all possible normalized difference vegetation indices (NDVIs) were derived from each type of Imagery and related to yield. Statistical analysis showed that the Hyperspectral Imagery accounted for more variability in yield than the other three types of multispectral Imagery and that the best narrow-band NDVIs among the 5151 NDVIs derived from each Hyperspectral image explained more variability than the best NDVIs derived from any of the actual or simulated multispectral images. These results indicate that Hyperspectral Imagery has the potential for improving yield estimation accuracy.
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Comparison of Airborne Multispectral and Hyperspectral Imagery for Mapping Cotton Root Rot
2009 Reno Nevada June 21 - June 24 2009, 2009Co-Authors: Chenghai Yang, Carlos J. Fernandez, James H. EverittAbstract:Cotton root rot, caused by the soilborne fungus Phymatotrichum omnivorum, is a major cotton disease affecting cotton production in the southwestern and south central U.S. Accurate delineation of root rot infestations is necessary for cost-effective management of the disease. The objective of this study was to compare airborne multispectral and Hyperspectral Imagery for detecting and mapping root rot areas in cotton fields. Two center-pivot irrigated fields in south Texas were selected for this study. Airborne 3-band multispectral and 128-band Hyperspectral Imagery was taken from the two fields shortly before harvest in 2002 when the infested areas were fully expressed for the season. Both types of Imagery and the principal component images derived from the Hyperspectral Imagery were classified into 2-10 spectral classes using unsupervised classification techniques. The individual spectral classes were then grouped into infested and non-infested zones. Accuracy assessment on the two-zone classification maps showed that both types of Imagery as well as the principal component Imagery equally accurately identified root rot areas within the fields. These results indicate that both airborne multispectral and Hyperspectral Imagery can be used for assessing root rot infestations within cotton fields.
James H. Everitt - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Hyperspectral Imagery with aerial photography and multispectral Imagery for mapping broom snakeweed
International Journal of Remote Sensing, 2010Co-Authors: Chenghai Yang, James H. EverittAbstract:Broom snakeweed Gutierrezia sarothrae Pursh Britt. & Rusby is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne Hyperspectral Imagery and compare it with aerial colour-infrared CIR photography and multispectral digital Imagery for mapping broom snakeweed infestations. Airborne Hyperspectral Imagery along with aerial CIR photographs and digital CIR images was acquired from a rangeland area in south Texas. The Hyperspectral Imagery was transformed using minimum noise fraction MNF and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper SAM classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image; these were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracies for three-class classification maps snakeweed, mixed woody and mixed herbaceous were 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed Hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne Hyperspectral Imagery along with aerial photography and multispectral Imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands.
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Comparison of airborne multispectral and Hyperspectral Imagery for mapping cotton root rot.
Biosystems Engineering, 2010Co-Authors: Chenghai Yang, James H. Everitt, Carlos J. FernandezAbstract:Cotton root rot, caused by the soilborne fungus Phymatotrichum omnivorum, is a major cotton disease in the south western and south central United States. Accurate delineation of root rot infestations is necessary for site-specific management of the disease. The objective of this study was to compare airborne multispectral and Hyperspectral Imagery for detecting and mapping root rot areas in cotton fields. Two centre-pivot irrigated fields in south Texas were selected for this study. Airborne 3-band multispectral Imagery and 128-band Hyperspectral Imagery were taken from the two fields shortly before harvest when infested areas were fully expressed for the season. Both types of Imagery and the principal component images derived from the Hyperspectral Imagery were classified into 2-10 spectral classes using unsupervised classification techniques. The individual spectral classes were then grouped into infested and non-infested zones. Accuracy assessment on the two-zone classification maps showed that both types of Imagery as well as the principal component Imagery equally accurately identified root rot areas within the fields. These results indicate that both airborne multispectral and Hyperspectral Imagery can be successfully used for assessing root rot infestations within cotton fields. The classification maps and buffer maps as well as the image processing procedures presented in this study can be readily used by cotton growers and consultants for the site-specific cultural and chemical management of this disease.
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Comparison of Airborne Multispectral and Hyperspectral Imagery for Estimating Grain Sorghum Yield
Transactions of the ASABE, 2009Co-Authors: Chenghai Yang, James H. Everitt, Joe M. Bradford, D. MurdenAbstract:Both multispectral and Hyperspectral images are being used to monitor crop conditions and map yield variability, but limited research has been conducted to compare these two types of Imagery for assessing crop growth and yields. The objective of this study was to compare airborne multispectral Imagery with airborne Hyperspectral Imagery for mapping yield variability in grain sorghum fields. Airborne color-infrared (CIR) Imagery and airborne Hyperspectral Imagery along with yield monitor data collected from four fields were used in this study. Three-band Imagery with wavebands corresponding to the collected CIR Imagery and four-band Imagery with wavebands similar to QuickBird satellite Imagery were generated from the 102-band Hyperspectral Imagery. All four types of Imagery (two actual and two simulated) were aggregated to increase pixel size to match the yield data resolution. Principal components and all possible normalized difference vegetation indices (NDVIs) were derived from each type of Imagery and related to yield. Statistical analysis showed that the Hyperspectral Imagery accounted for more variability in yield than the other three types of multispectral Imagery and that the best narrow-band NDVIs among the 5151 NDVIs derived from each Hyperspectral image explained more variability than the best NDVIs derived from any of the actual or simulated multispectral images. These results indicate that Hyperspectral Imagery has the potential for improving yield estimation accuracy.
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Comparison of Airborne Multispectral and Hyperspectral Imagery for Mapping Cotton Root Rot
2009 Reno Nevada June 21 - June 24 2009, 2009Co-Authors: Chenghai Yang, Carlos J. Fernandez, James H. EverittAbstract:Cotton root rot, caused by the soilborne fungus Phymatotrichum omnivorum, is a major cotton disease affecting cotton production in the southwestern and south central U.S. Accurate delineation of root rot infestations is necessary for cost-effective management of the disease. The objective of this study was to compare airborne multispectral and Hyperspectral Imagery for detecting and mapping root rot areas in cotton fields. Two center-pivot irrigated fields in south Texas were selected for this study. Airborne 3-band multispectral and 128-band Hyperspectral Imagery was taken from the two fields shortly before harvest in 2002 when the infested areas were fully expressed for the season. Both types of Imagery and the principal component images derived from the Hyperspectral Imagery were classified into 2-10 spectral classes using unsupervised classification techniques. The individual spectral classes were then grouped into infested and non-infested zones. Accuracy assessment on the two-zone classification maps showed that both types of Imagery as well as the principal component Imagery equally accurately identified root rot areas within the fields. These results indicate that both airborne multispectral and Hyperspectral Imagery can be used for assessing root rot infestations within cotton fields.
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Evaluating airborne Hyperspectral Imagery for mapping waterhyacinth infestations
Journal of Applied Remote Sensing, 2007Co-Authors: Chenghai Yang, James H. EverittAbstract:Waterhyacinth [Eichhornia crassipes (Mart.) Solms] is an exotic aquatic weed that often invades and clogs waterways in many tropical and subtropical regions of the world. The objective of this study was to evaluate airborne Hyperspectral Imagery and different image classification techniques for mapping waterhyacinth infestations on Lake Corpus Christi in south Texas. Hyperspectral Imagery with bands in the visible to near-infrared region of the spectrum was acquired from two study sites and minimum noise fraction (MNF) transformation was used to reduce the spectral dimensionality of the Imagery. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the MNF-transformed Imagery for distinguishing waterhyacinth from associated plant species (waterlettuce, mixed herbaceous species, and mixed woody species) and other cover types (bare soil and water). Accuracy assessment showed that overall accuracy varied from 79% for SAM to 96% for maximum likelihood for site 1 and from 84% for minimum distance to 95% for maximum likelihood for site 2. Kappa analysis showed that maximum likelihood was significantly better than the other three methods and that there were no significant differences in overall classifications among the other three methods. Producer's and user's accuracies for waterhyacinth based on maximum likelihood were 94% and 100%, respectively, for site 1 and 100% and 95% for site 2. These results indicate that airborne Hyperspectral Imagery incorporated with image transformation and classification techniques can be a useful tool for mapping waterhyacinth infestations.
Sen Jia - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (1) - Three-Dimensional Surface Feature for Hyperspectral Imagery Classification
Neural Information Processing, 2017Co-Authors: Sen Jia, Meng ZhangAbstract:Gabor surface feature (GSF) uses the first order and second order derivatives of Gabor magnitude pictures (GMPs) to jointly represent image. However, GSF can not excavate the contextual information that hides in the spectral-spatial structure of three-dimensional Hyperspectral Imagery since GSF can only deal with spatial relationships. Meanwhile, GSF runs on GMPs with multi-scale and multi-orientation, which leads to dimensional explosion problem. Aiming at these two problems, three-dimensional surface feature (3DSF) approach is proposed for Hyperspectral Imagery in this paper. 3DSF directly deals with the raw Hyperspectral Imagery data and utilizes its first order derivative magnitude to jointly represent Hyperspectral Imagery. Experiments on three real Hyperspectral datasets, including Pavia University, Houston University and Indian Pines, verify the effectiveness of the proposed 3DSF approach.
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ICIP - A Gabor feature fusion framework for Hyperspectral Imagery classification
2017 IEEE International Conference on Image Processing (ICIP), 2017Co-Authors: Sen Jia, Bin Deng, Huimin Xie, Lin DengAbstract:Hyperspectral Imagery acquired by a Hyperspectral sensor contains hundreds of narrow contiguous spectral bands, providing the opportunity to identify the various materials present on the surface. Due to the three-dimensional (3D) nature of Hyperspectral data, 3D filters that could extract joint spatial-spectral features have been recently considered in the literature. In this paper, after the 3D Gabor features with certain orientations have been extracted from the raw Hyperspectral image data, both the Gabor magnitude and phase features have been used for Hyperspectral Imagery classification, which is named as Gabor-MP. Specifically, the confidence score of each test sample is computed by support vector machine for each Gabor magnitude feature cube, while the Hamming distance is calculated based on the quadrant bit coding of each Gabor phase feature cube. Then the label of the test sample is identified by simple calculation between the confidence scores and Hamming distance values. Experimental results on two real Hyperspectral data have demonstrated the effectiveness of the proposed Gabor feature fusion framework for Hyperspectral Imagery classification.
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IGARSS - Three-dimensional local binary patterns for Hyperspectral Imagery classification
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Sen Jia, Lin Deng, Xiuping JiaAbstract:Local binary patterns (LBP) features extracted from Hyperspectral Imagery (HSI) have gained impressive performance in Hyperspectral classification tasks, for which LBP got considerable attention. However, existing LBP-based Hyperspectral Imagery classification methods utilized two-dimensional LBP (2DLBP) that could capture gray variation signal in space, which did not excavate the contextual information that hides in spectral-spatial structure considering that Hyperspectral Imagery characterizes by three dimension. Aimed at this problem, this paper presents a three-dimensional LBP-based (3DLBP) Hyperspectral Imagery classification method where 2DLBP textures histogram on three orthogonal planes are concatenated to form 3DLBP texture features to be classified by sparse representation. A serial of experiments are conducted on the Pavia university dataset, and the experimental results show that the performance of 3DLBP is significantly superior to that of 2DLBP.
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band selection for Hyperspectral Imagery using affinity propagation
Digital Image Computing: Techniques and Applications, 2008Co-Authors: Sen Jia, Yuntao QianAbstract:Hyperspectral Imagery generally contains enormous amounts of data due to hundreds of spectral bands. Band selection is often adopted firstly to reduce computational cost and accelerate knowledge discovery of subsequent classificationand analysis. Recently, a new clustering algorithm, named "affinity propagation," is proposed. Different from the popular k-centers clustering technique, affinity propagation operates by simultaneously considering all data points as potential cluster centers (called "exemplars") and exchanging messages between data points until a good set of exemplars and clusters emerges. In this paper, we apply affinity propagation for band selection of Hyperspectral data. Experimental results demonstrate that, compared with some relevant and recent methods for band selection, the bands chosen by affinity propagation best represent the Hyperspectral Imagery from the pixel image classification standpoint.
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DICTA - Band Selection for Hyperspectral Imagery Using Affinity Propagation
2008 Digital Image Computing: Techniques and Applications, 2008Co-Authors: Sen Jia, Yuntao QianAbstract:Hyperspectral Imagery generally contains enormous amounts of data due to hundreds of spectral bands. Band selection is often adopted firstly to reduce computational cost and accelerate knowledge discovery of subsequent classificationand analysis. Recently, a new clustering algorithm, named "affinity propagation," is proposed. Different from the popular k-centers clustering technique, affinity propagation operates by simultaneously considering all data points as potential cluster centers (called "exemplars") and exchanging messages between data points until a good set of exemplars and clusters emerges. In this paper, we apply affinity propagation for band selection of Hyperspectral data. Experimental results demonstrate that, compared with some relevant and recent methods for band selection, the bands chosen by affinity propagation best represent the Hyperspectral Imagery from the pixel image classification standpoint.
Nasser M. Nasrabadi - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - Sparsity-based classification of Hyperspectral Imagery
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010Co-Authors: Yi Chen, Nasser M. Nasrabadi, Trac D. TranAbstract:In this paper, a new sparsity-based classification algorithm for Hyperspectral Imagery is proposed. This algorithm is based on the concept that a pixel in Hyperspectral Imagery lies in a low-dimensional subspace and thus can be represented by a sparse linear combination of the training samples. The sparse representation (a sparse vector representing the selected training samples) of a test sample can be recovered by solving a constrained optimization problem. Once the sparse vector is obtained, the class of the test sample can be directly determined by the behavior of the vector on reconstruction. In addition to the constraints on sparsity and reconstruction accuracy, we also exploit the fact that Hyperspectral images are usually smooth within a neighborhood. In our proposed algorithm, a smoothness constraint is imposed by forcing the Laplacian of the reconstructed image to be minimum in the optimization process. The proposed sparsity-based algorithm is applied to several Hyperspectral Imagery to classify the pixels into target and background classes. Simulation results show that our algorithm outperforms the classical Hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, and adaptive subspace detectors.
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Kernel Spectral Matched Filter for Hyperspectral Imagery
International Journal of Computer Vision, 2006Co-Authors: Heesung Kwon, Nasser M. NasrabadiAbstract:In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in Hyperspectral Imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on Hyperspectral Imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.
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Hyperspectral Imagery and segmentation
Automatic Target Recognition XII, 2002Co-Authors: Mark C. Wellman, Nasser M. NasrabadiAbstract:Hyperspectral Imagery (HSI), a passive infrared imaging technique which creates images of fine resolution across the spectrum is currently being considered for Army tactical applications. An important tactical application of infra-red (IR) Hyperspectral Imagery is the detection of low contrast targets, including those targets that may employ camouflage, concealment and deception (CCD) techniques [1,2]. Spectral reflectivity characteristics were used for efficient segmentation between different materials such as painted metal, vegetation and soil for visible to near IR bands in the range of 0.46-1.0 microns as shown previously by Kwon et al [3]. We are currently investigating the HSI where the wavelength spans from 7.5-13.7 microns. The energy in this range of wavelengths is almost entirely emitted rather than reflected, therefore, the gray level of a pixel is a function of the temperature and emissivity of the object. This is beneficial since light level and reflection will not need to be considered in the segmentation. We will present results of a step-wise segmentation analysis on the long-wave infrared (LWIR) hyperspectrum utilizing various classifier architectures applied to both the full-band, broad-band and narrow-band features derived from the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) data base. Stepwise segmentation demonstrates some of the difficulties in the multi-class case. These results give an indication of the added capability the Hyperspectral Imagery and associated algorithms will bring to bear on the target acquisition problem.
Zhou Xin - One of the best experts on this subject based on the ideXlab platform.
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Reduced Set Based Support Vector Machine for Hyperspectral Imagery Classification
Computer Science, 2010Co-Authors: Zhou XinAbstract:Aiming at the problem of more computational time needed in Hyperspectral Imagery classification procedure based on support vector machine,a reduced set method was brought forward to heighten Hyperspectral Imagery classification efficiency.The radial basis kernel function was adopted,one-against-one decomposition algorithm was used to construct multi-class Support Vector Machine classifier and cross validation grid search method was applied to select model parameter.The reduced set algorithm was also used to reduce the computational complexity of predication.Through Hyperspectral Imagery classification experiment it can be concluded that it does not need to use all support vectors to keep generalization ability of Support Vector Machine.The reduced set algorithm can improve Hyperspectral Imagery classification predicative efficiency highly and keep classification accuracy at the same time.