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- 2018
- Fruit defects
- Jujube
- Principal component analysis
- Hyperspectral imaging
- hyperspectral images
- spectral and spatial features
- classification
- SVM
- mutual information
- GLCM
- Optoelectronic Information Science and Engineering
- Mohammed V University in Rabat
- Southern Taiwan University of Science and Technology
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - ROBUST PCANet for Hyperspectral Image Change Detection
摘要： Deep learning is an effective tool for handling high-dimensional data and modeling nonlinearity, which can tackle the hyper-spectral data well. Usually deep learning methods need a large number of training samples. However, there is no labeled data for training in change detection (CD). Considering these, this paper develops an unsupervised Robust PCA network (RPCANet) for hyperspectral image CD task. The main contributions of this work are twofold: 1) An unsupervised convolutional neural networks named RPCANet is proposed to handle the hyperspectral image CD; 2) An effective CD framework using the RPCANet and change vector analysis (CVA) is designed to achieve better CD performance with more powerful features. Experimental results on real hyperspectral datasets demonstrate the effectiveness of the proposed method.
关键词： change detection (CD),Robust PCA network (RPCANet),Hyperspectral image,change vector analysis (CVA)
更新于2025-09-09 09:28:46
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Introduction to Special issue on Geologic Remote Sensing
摘要： Herein we provide an overview of science and technology involved in remote sensing, and outlines some practical constraints in applications to geological problems. We further summarize diagnostic spectral features of important geological material that can be detected using satellite- and air-borne remote sensing. Finally, the papers contained in the special issue are briefly introduced.
关键词： Geologic Remote Sensing,Spectral Features,Hyperspectral Remote Sensing,LANDSAT,ASTER
更新于2025-09-09 09:28:46
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A Constrained Sparse Representation Model for Hyperspectral Anomaly Detection
摘要： In this paper, we propose a novel sparsity-based algorithm for anomaly detection in hyperspectral imagery. The algorithm is based on the concept that a background pixel can be approximately represented as a sparse linear combination of its spatial neighbors while an anomaly pixel cannot if the anomalies are removed from its neighborhood. To be physically meaningful, the sum-to-one and nonnegativity constraints are imposed to abundance vector based on the linear mixture model, and the upper bound constraint on sparsity level is removed for better recovery of the test pixel. First, the proposed method utilizes the redundant background information to automatically remove anomalies from the background dictionary. Then, the reconstruction error obtained by the new background dictionary is directly used for anomaly detection. Moreover, a kernel version of the proposed method is also derived to completely exploit the nonlinear feature of hyperspectral data. An important advantage of the proposed methods is their capability to adaptively model the background even when some anomaly pixels are involved. Extensive experiments have been conducted on three real hyperspectral data sets. It is demonstrated that the proposed detectors achieve a promising detection performance with a relatively low computational cost.
关键词： hyperspectral imagery (HSI),linear mixture model (LMM),Anomaly detection,constrained sparse representation (SR)
更新于2025-09-09 09:28:46
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Mapping “Broken” Dark Modes Using Cathodoluminescence in a Scanning Electron Microscope
摘要： Dark plasmon modes are modes that lack a net dipolar character, and hence do not radiate efficiently. They possess a smaller spectral linewidth and longer lifetimes, making them attractive for many applications in sensing and for high-Q cavities. A focussed high-energy electron beam can be employed as a local, broad-spectrum excitation source, as the evanescent electric field associated with the fast electrons excites resonances in a polarizable material. The light that is radiated to the far-field can be collected in cathodoluminescence (CL), or the energy loss of the electrons can be measured in electron energy loss spectroscopy (EELS). The spatial resolution of these techniques in the scanning electron microscope (SEM) or scanning/transmission electron microscope (S/TEM) is typically limited by the spatial extent of the evanescent field (order of 10~nm depending on beam energy), making them ideal techniques for mapping the spatial distribution of plasmon modes. The energy lost to a dark mode can be detected in EELS, but since these modes are not radiative, in the strictest sense, these modes cannot be detected in CL. However, due to either inherent or introduced asymmetries in the structure, these dark modes acquire a net dipole resonance and do radiate. We performed hyperspectral CL on gold nanorod trimers fabricated using electron beam lithography using a beam energy of 30 keV and current of 500 pA (FEI Nova Nanosem). The CL was collected using a Delmic SPARC system equipped with an Andor Shamrock 303i spectrometer and Andor iVac spectral camera. We show the concurrent SEM images of a symmetric trimer and a trimer with an introduced asymmetry of an inter-trimer angle of 40 degrees. The trimers show two resonances, a high-energy symmetric mode and a low-energy mode composed of two degenerate dipole modes. We mapped these modes by mapping the average intensity between 350-500 nm and 600-700 nm, respectively. While it is clear that the trimer with broken symmetry has a prominent high-energy mode involving all three rods, the symmetric trimer appears to have enough inherent asymmetry to also display this mode. This inherent asymmetry can also be seen in the map of the dipole resonances. Dark modes that are “broken” by asymmetry acquire a dipolar component that allows them to be detected and mapped in CL. Hyperspectral mapping using CL is a powerful method for understanding the effect of nanoscale defects on plasmonic devices.
关键词： cathodoluminescence,plasmonic devices,Dark plasmon modes,scanning electron microscope,hyperspectral mapping
更新于2025-09-09 09:28:46
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Urban Land Use/Land Cover Discrimination Using Image-Based Reflectance Calibration Methods for Hyperspectral Data
摘要： Irrespective of substantial research in land use/land cover (LULC) monitoring of urban area, hyperspectral data is not yet exploited effectively because of lack of local spectral resources and a practical reflectance calibration method. The objective of this research is to develop an effective methodology for urban LULC classification using image-based reflectance calibration methods: especially Vegetation-Impervious-Soil classes (VIS), using hyperspectral data. We used EO-1 Hyperion image of Pune City, India and assessed the suitability of different land covers as reflectance calibration surfaces. Furthermore, we performed LULC classification using different reflectance calibration methods such as Internal Area Relative Reflectance, Flat Field Relative Reflectance, and 6S for comparative analysis. Urban VIS signatures extracted from Hyperion image show distinct spectral curves at broader level. Flat Field Relative Reflectance method provides above 90 percent average overall accuracy. An advanced physics-based method such as 6S does not provide any added advantage over image-based calibration methods.
关键词： urban LULC classification,hyperspectral data,Vegetation-Impervious-Soil classes,EO-1 Hyperion,reflectance calibration
更新于2025-09-09 09:28:46
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Hyperspectral Unmixing with Bandwise Generalized Bilinear Model
摘要： Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.
关键词： alternative direction method of multipliers (ADMM),bandwise generalized bilinear model (BGBM),hyperspectral images (HSIs),additive white Gaussian noise (AWGN),mixed noise
更新于2025-09-09 09:28:46
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HYPERSPECTRAL IMAGE DENOISING USING MULTIPLE LINEAR REGRESSION AND BIVARIATE SHRINKAGE WITH 2-D DUAL-TREE COMPLEX WAVELET IN THE SPECTRAL DERIVATIVE DOMAIN
摘要： In this paper, a new denoising method is proposed for hyperspectral remote sensing images, and tested on both the simulated and the real-life datacubes. Predicted datacube of the hyperspectral images is calculated by multiple linear regression in the spectral domain based on the strong spectral correlation of the useful signal and the inter-band uncorrelation of the random noise terms in hyperspectral images. A two dimensional dual-tree complex wavelet transform is performed in the spectral derivative domain, where the noise level is elevated temporarily to avoid signal deformation during the wavelet denoising, and then the bivariate shrinkage is used to shrink the wavelet coefficients. Simulated experimental results demonstrate that the proposed method obtains better results than the other denoising methods proposed in the reference, improves the signal to noise ratio up to 0.5dB to 10dB. The real-life data experiment shows that the proposed method is valid and effective.
关键词： denoising,Hyperspectral imagery,complex wavelet,bivariate shrinkage,multiple linear regression
更新于2025-09-09 09:28:46
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Hyperspectral Unmixing with Robust Collaborative Sparse Regression
摘要： Recently, sparse unmixing (SU) of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear effects (i.e., nonlinearity). In this paper, we propose a new method named robust collaborative sparse regression (RCSR) based on the robust LMM (rLMM) for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM) is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is ef?cient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.
关键词： hyperspectral data,outlier,robust collaborative sparse regression (RCSR),robust LMM (rLMM),sparse unmixing (SU)
更新于2025-09-09 09:28:46
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Fast reconstruction of hyperspectral radiative transfer simulations by using small spectral subsets: application to the oxygen A band
摘要： Hyperspectral radiative transfer simulations are a versatile tool in remote sensing but can pose a major computational burden. We describe a simple method to construct hyperspectral simulation results by using only a small spectral subsample of the simulated wavelength range, thus leading to major speedups in such simulations. This is achieved by computing principal components for a small number of representative hyperspectral spectra and then deriving a reconstruction matrix for a specific spectral subset of channels to compute the hyperspectral data. The method is applied and discussed in detail using the example of top-of-atmosphere radiances in the oxygen A band, leading to speedups in the range of one to two orders of magnitude when compared to radiative transfer simulations at full spectral resolution.
关键词： hyperspectral radiative transfer simulations,oxygen A band,principal component analysis,remote sensing,computational efficiency
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Weed Classification in Hyperspectral Remote Sensing Images Via Deep Convolutional Neural Network
摘要： Automatic weed detection and mapping are critical for site-speci?c weed control in order to reduce the cost of farming as well as the impact of herbicides on human health. In this paper, we investigate patch-based weed identi?cation using hyperspectral images. Convolutional Neural Network (CNN) is evaluated and compared with the Histogram of Oriented Gradients (HoG) for this purpose. Suitable patch sizes are investigated. The limitation of RGB imagery is demonstrated. The experimental results indicate that the overall accuracy of the weed classi?cation using CNN increases with the increasing number of bands used. With more bands, CNN extracts more powerful and discriminative features and leads to improved classi?cation as compared to the traditional HoG feature extraction method. The computational load of CNN, however, is slightly increased with the increasing number of bands.
关键词： Histogram of Oriented Gradients (HoG),weed mapping,Hyperspectral images,Convolutional Neural Network (CNN)
更新于2025-09-09 09:28:46