- 标题
- 摘要
- 关键词
- 实验方案
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[IEEE 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Chengdu, China (2018.7.15-2018.7.18)] 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Spectral-Spatial Graph Convolutional Networks for Semel-Supervised Hyperspectral Image Classification
摘要： Collecting label samples is quite costly and time consuming for hyperspectral image (HSI) classification tasks. Semi-supervised learning framework, which combines the intrinsic information of labeled and unlabeled samples can alleviate the deficient labeled samples and increase the accuracy of HSI classification. In this paper, we propose a novel framework for semi-supervised learning on multiple spectral-spatial graphs that is based on graph convolutional networks (SGCN). In the filtering operation on graphs we consider the spatial information and spectral signatures of HSI simultaneously. The experimental results on three real-life HSI data sets, i.e. Botswana Hyperion, Kennedy Space Center, and Indian Pines, show that the proposed SGCN can significantly improve the classification accuracy. For instance, the over accuracy on Indian Pine data is increased from 78% to 93%.
关键词： Hyperspectral image classification,Graph fourier transform,Graph convolutional,Neural networks,Semi-supervised learning
更新于2025-09-23 15:21:01
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KNN-Based Representation of Superpixels for Hyperspectral Image Classification
摘要： Superpixel segmentation has been demonstrated to be a powerful tool in hyperspectral image (HSI) classification. Each superpixel region can be regarded as a homogeneous region, which is composed of a series of spatial neighboring pixels. However, a superpixel region may contain the pixels from different classes. To further explore the optimal representations of superpixels, a new framework based on two k selection rules is proposed to find the most representative training and test samples. The proposed method consists of the following four steps: first, a superpixel segmentation algorithm is performed on the HSI to cluster the pixels with similar spectral features into the same superpixel. Then, a domain transform recursive filtering is used to extract the spectral–spatial features of the HSI. Next, the k nearest neighbor (KNN) method is utilized to select k1 representative training samples and k2 test pixels for each superpixel, which can effectively overcome the within-class variations and between-class interference, respectively. Finally, the class label of superpixels can be determined by measuring the averaged distances among the selected training and test samples. Experiments conducted on four real hyperspectral datasets show that the proposed method provides competitive classification performances with respect to several recently proposed spectral–spatial classification methods.
关键词： superpixel segmentation,hyperspectral image classification,k nearest neighbor (KNN),Domain transform recursive filtering (RF)
更新于2025-09-23 15:21:01
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Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm
摘要： Hyperspectral image classi?cation is a hot issue in the ?eld of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classi?cation method that is used to process image data. In this paper, an ef?cient hyperspectral image classi?cation method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classi?er to improve the classi?cation ef?ciency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classi?cation. Then, Q-statistic is used to select base classi?ers. Finally, the results are obtained by using the voting method. Three simulation examples, classi?cation of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method.
关键词： extreme learning machine,rotation forest,hyperspectral image classi?cation,Q-statistic
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - JeJu, Korea (South) (2018.6.24-2018.6.26)] 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - Iterative Normalized Matched Filtering for Detection of Chemical Agents in Hyperspectral Imaging
摘要： Hyperspectral imaging (HSI) can be used to detect a harmful chemical agents’ (CAs’) cloud from a long distance. A normalized matched filter (NMF) is one of the best algorithms to detect CAs in the atmosphere with perfectly known statistics of the background. However, if the background are affected by a CA’s signal, (that is a contamination condition) the performance of the NMF detector is degraded. To design an NMF detector that is robust to contamination, we propose an iterative normalized matched filter (INMF). The proposed algorithm extracts CA-off spectra from the contaminated background spectra dataset using a contaminated NMF detector. And the NMF detector is designed using the extracted CA-off background spectra and this procedure repeats until convergence. Simulation results demonstrate that the proposed algorithm significantly improves the detection performance.
关键词： Gas detection,Normalized Matched Filter,Hyperspectral Image
更新于2025-09-23 15:21:01
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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 - Evaluation of Different Regularization Methods for the Extreme Learning Machine Applied to Hyperspectral Images
摘要： During recent years, many regularization techniques have been proposed to deal with ill-posed problems related to hyperspectral image classification, in which the limited number of training samples contrasts with the very high spectral dimensionality. However, the intrinsic structure of a hyperspectral image often depends on the specific scene and spectrometer, although regularizers like Ridge, LASSO, etc, have been widely used in practical applications. Instead of imposing these regularizers to the probabilistic output of a classifier, this work evaluates the use of extreme learning machines (ELM) with output weights of a single-hidden layer feed-forward neural network (SLFN) regularized with Ridge and LASSO priors, respectively. Experimental results with several real hyperspectral images are conducted to compare the performance and adaptation of these two regularizers with the original ELM in classification scenarios.
关键词： Ridge,LASSO,regularization,hyperspectral image classification,extreme learning machine
更新于2025-09-23 15:21:01
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Simple Cosolvent-Treated PEDOT:PSS Films on Hybrid Solar Cells With Improved Efficiency
摘要： This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multi-sensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.
关键词： multimodal-,multisource-data fusion,thermal imaging,landcover classification,multiresolution-,Hyperspectral,image analysis and data fusion (IADF)
更新于2025-09-23 15:19:57
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Estimation of solar photovoltaic energy curtailment due to volta??watt control
摘要： Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.
关键词： nonlocal means,Hyperspectral image,spectral and spatial information,restoration,low rank (LR)
更新于2025-09-23 15:19:57
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A low-rank estimation method for CTIS image reconstruction
摘要： The computed tomography image spectrometer (CTIS) is a snapshot hyperspectral imaging technique, which enables hyperspectral image acquisition in a dynamic scene. However, traditional image reconstruction methods with no explicit constraints will introduce high-frequency noise. The low-rank property has been used in hyperspectral image denoising and achieved great effects. We develop an effective method of low-rank estimation (LRE) for CTIS image reconstruction, which shows significant improvements in both the image quality and the spectral quality of the reconstructed image. Compared with the traditional methods, the peak signal-to-noise ratio of the LRE hyperspectral image can be increased by 8 dB, and the spectral-angular mapping can be decreased by 4 times.
关键词： computed tomography image spectrometers,image reconstruction,low-rank estimation,hyperspectral image
更新于2025-09-19 17:15:36
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Online Deconvolution for Industrial Hyperspectral Imaging Systems
摘要： This paper proposes a hyperspectral image deconvolution algorithm for the online restoration of hyperspectral images as provided by whiskbroom and pushbroom scanning systems. We introduce a least-mean-squares (LMS)-based framework accounting for the convolution kernel noncausality and including nonquadratic (zero attracting and piecewise constant) regularization terms. This results in the so-called sliding block regularized LMS (SBR-LMS), which maintains a linear complexity compatible with real-time processing in industrial applications. A model for the algorithm mean and mean-squares transient behavior is derived and the stability condition is studied. Experiments are conducted to assess the role of each hyper-parameter. A key feature of the proposed SBR-LMS is that it outperforms standard approaches in low SNR scenarios such as ultra-fast scanning.
关键词： hyperspectral image,LMS,ZA-LMS,online deconvolution
更新于2025-09-19 17:15:36
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Quantitative Analysis of Cadmium Content in Tomato Leaves Based on Hyperspectral Image and Feature Selection
摘要： In order to ensure that safe and healthy tomatoes can be provided to people, a method for quantitative determination of cadmium content in tomato leaves based on hyperspectral imaging technology was put forward in this study. Tomato leaves with seven cadmium stress gradients were studied. Hyperspectral images of all samples were firstly acquired by the hyperspectral imaging system, then the spectral data were extracted from the hyperspectral images. To simplify the model, three algorithms of competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS) were used to select the feature wavelengths ranging from 431 to 962 nm. Final results showed that BOSS can improve prediction performance and greatly reduce features when compared with the other two selection methods. The BOSS model got the best accuracy in calibration and prediction with R2c of 0.9907 and RMSEC of 0.4257mg/kg, R2p of 0.9821, and RMSEP of 0.6461 mg/kg. Hence, the method of hyperspectral technology combined with the BOSS feature selection is feasible for detecting the cadmium content of tomato leaves, which can potentially provide a new method and thought for cadmium content detection of other crops.
关键词： Regression model,Feature selection,Tomato leaves,Hyperspectral image technology,Non-destructive analysis
更新于2025-09-19 17:15:36