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- 关键词
- 实验方案
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过滤筛选
- 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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Autonomous marine hyperspectral radiometers for determining solar irradiances and aerosol optical properties
摘要： We have developed two hyperspectral radiometer systems which require no moving parts, shade rings or motorised tracking, making them ideally suited for autonomous use in the inhospitable remote marine environment. Both systems are able to measure direct and diffuse hyperspectral irradiance in the wavelength range 350–1050 nm at 6 nm (Spectrometer 1) or 3.5 nm (Spectrometer 2) resolution. Marine field trials along a 100? transect (between 50? N and 50? S) of the Atlantic Ocean resulted in close agreement with existing commercially available instruments in measuring (1) photosynthetically available radiation (PAR), with both spectrometers giving regression slopes close to unity (Spectrometer 1: 0.960; Spectrometer 2: 1.006) and R2 ～ 0.96; (2) irradiant energy, with R2～ 0.98 and a regression slope of 0.75 which can be accounted for by the difference in wavelength integration range; and (3) hyperspectral irradiance where the agreement on average was between 2 and 5 %. Two long duration land-based field campaigns of up to 18 months allowed both spectrometers to be well calibrated. This was also invaluable for empirically correcting for the wider field of view (FOV) of the spectrometers in comparison with the current generation of sun photometers (～ 7.5? compared with ～ 1?). The need for this correction was also confirmed and independently quantified by atmospheric radiative transfer modelling and found to be a function of aerosol optical depth (AOD) and solar zenith angle. Once Spectrometer 2 was well calibrated and the FOV effect corrected for, the RMSE in retrievals of AOD when compared with a CIMEL sun photometer were reduced to ～ 0.02–0.03 with R2 > 0.95 at wavelengths 440, 500, 670 and 870 nm. Corrections for the FOV as well as ship motion were applied to the data from the marine field trials. This resulted in AOD500 nm ranging between 0.05 in the clear background marine aerosol regions and ～ 0.5 within the Saharan dust plume. The RMSE between the handheld Microtops sun photometer and Spectrometer 2 was between 0.047 and 0.057 with R2 > 0.94.
关键词： hyperspectral radiometer,autonomous measurement,marine environment,solar irradiance,aerosol optical depth
更新于2025-09-04 15:30:14
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Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation
摘要： Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI’s discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art.
关键词： Superpixel segmentation,Hyperspectral images,Denoising,PSSV
更新于2025-09-04 15:30:14
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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 - Fast Sample Generation with Variational Bayesian for Limited Data Hyperspectral Image Classification
摘要： Labeling data for hyperspectral remote sensing image classification is a tedious and cost-intensive task. As a consequence, it is oftentimes necessary to perform classification when only very limited number of labeled training data is available. Several approaches have been proposed to address this problem. A recent proposal is to generate additional synthetic samples from a Gaussian Mixture Model for each class. One challenge with this approach lies in determining the number of components in the GMM. In this paper, we propose an approximation algorithm to select the number of components, namely Variational Bayesian (VB). The main advantage of VB is that it does not require multiple clustering computations in advance. Variational Bayesian not only greatly decreases the computational cost, but also generates comparable or better results in comparison to other methods.
关键词： synthetic data,hyperspectral remote sensing image classification,limited training data,Gaussian mixture model (GMM),Variational Bayesian (VB)
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Image Refined Plant Classification By Graph Based Composite Kernel
摘要： Recently, the popularity of using hyperspectral image to study and monitor plant characteristics and conditions has been increased. The use of hyperspectral image improves the breeding process and increases profits. In the case of hyper-spectral data with high spectral resolution characteristics suitable for intraclass classification, this paper focuses on the application of hyperspectral image analysis in distin-guishing among different plant species. Plant intraclass clas-sification is sophisticated due to its small spectral differ-ences. Hence, a refined hyperspectral image classification method for plant, referred as SI-GCK which uses Spectral Index (SI) to represent plant spectral, and take advantage of semi-supervised graph-based composite kernel (GCK) method to combine spectral information and spatial location of pixels for classification is presented in this paper. As a comparison, sequential floating forward selection (SFFS) is used to select spectral bands for SVM learning. Its accuracy of plant classification is nearly equal to result by means of SI, and the proposed method in this paper is better than afore-mentioned.
关键词： spectral index,plant classification,graph-based composite kernel,hyperspectral image
更新于2025-09-04 15:30:14
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Trilateral Smooth Filtering for Hyperspectral Image Feature Extraction
摘要： Traditional bilateral ?ltering (BF) cannot extract hyperspectral image (HSI) features well when the center pixel of the neighborhood pixel set is a noise point in the process of ?ltering the HSI. In this letter, a trilateral smooth ?ltering (TRSF) is presented. The proposed algorithm avoids the above-mentioned limitation problem in the BF algorithm. TRSF is successfully applied to the feature extraction of three actual HSIs. To prove the effectiveness of the proposed algorithm, support vector machines are used to classify the extracted features. Experimental results show that the proposed feature extraction method is simple and effective.
关键词： hyperspectral image (HSI),feature extraction,Bilateral ?ltering (BF),trilateral smooth
更新于2025-09-04 15:30:14
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Supervised band selection in hyperspectral images using single-layer neural networks
摘要： Hyperspectral images provide fine details of the scene under analysis in terms of spectral information. This is due to the presence of contiguous bands that make possible to distinguish different objects even when they have similar colour and shape. However, neighbouring bands are highly correlated, and, besides, the high dimensionality of hyperspectral images brings a heavy burden on processing and also may cause the Hughes phenomenon. It is therefore advisable to make a band selection pre-processing prior to the classification task. Thus, this paper proposes a new supervised filter-based approach for band selection based on neural networks. For each class of the data set, a binary single-layer neural network classifier performs a classification between that class and the remainder of the data. After that, the bands related to the biggest and smallest weights are selected, so, the band selection process is class-oriented. This process iterates until the previously defined number of bands is achieved. A comparison with three state-of-the-art band selection approaches shows that the proposed method yields the best results in 43.33% of the cases even with greatly reduced training data size, whereas the competitors have achieved between 13.33% and 23.33% on the Botswana, KSC and Indian Pines datasets.
关键词： supervised learning,neural networks,Hyperspectral images,band selection,filter-based approach
更新于2025-09-04 15:30:14
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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 - Fuzzy Fusion of Change Vector Analysis and Spectral Angle Mapper for Hyperspectral Change Detection
摘要： Change Vector Analysis (CVA) is one of the most widely used approaches for change detection in multispectral and hyperspectral images. Although, in CVA, the spectral change vector (CV) comprises the angle as well as the magnitude of the change, typically only the magnitude measure is used as change criterion. On the other hand, the spectral angle mapper (SAM) uses only the angle measure as criterion for change detection. It is envisaged that combining the angle and magnitude for change detection (i.e. combining SAM and magnitude CVA) can improve the change detection performance, yet only a limited number of approaches have been proposed in the literature so far. This paper presents a novel fuzzy inference combination strategy that combines the angle and magnitude distances, referred to as Fuzzy CVA (FuzCVA), and is shown that the proposed approach can provide improved change detection performance by effectively combining magnitude and angle measures.
关键词： Hyperspectral Imaging,change detection,spectral angle mapper,fuzzy inference,change vector analysis
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Can We Generate Good Samples for Hyperspectral Classification? — A Generative Adversarial Network Based Method
摘要： The insufficiency of training samples is really a great challenge for hyperspectral image (HSI) classification. Samples generation is a commonly used technique in deep learning based remote sensing field which can extend the training set. However, previous methods ignore the real distribution of the training samples in the feature space and thus can hardly ensure that the generated samples possess the same patterns with the real ones. In this paper, we propose a generative adversarial network based method (SpecGAN) to handle this problem. Different from traditional GAN framework where the generated samples have no categories, for the first time we take the label information into consideration for hyperspectral images. Feeding a random noise z and a class label vector y into the generator, we can get a spectral sample of the corresponding category. The experiments on the Pavia University data set demonstrate the potential of the proposed SpecGAN in spectral samples generation.
关键词： hyperspectral image classification,generative adversarial network,Sample generation,deep learning
更新于2025-09-04 15:30:14
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Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications
摘要： In recent years, with the rapid development of unmanned aerial vehicles (UAVs) and lightweight hyperspectral imaging (HSI) sensors, mini-UAV-borne hyperspectral remote sensing (HRS) systems have been developed and demonstrate great value and application potential. Compared to spaceborne and airborne HSI systems, mini-UAV-borne HSI systems come with relatively low manufacturing and running costs and have thus become a new research focus in the field of HRS. This article focuses on recent developments in UAV-borne HRS, including UAV platforms, miniaturized hyperspectral sensors, system integration, data observation, and preprocessing. In addition, successful application cases in the domains of agriculture, forestry, geology, and environmental monitoring are introduced, and we discuss current UAV-borne systems and their developing trends.
关键词： forestry,environmental monitoring,hyperspectral remote sensing,HSI,geology,agriculture,UAV
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Symposium on Technologies for Homeland Security (HST) - Woburn, MA, USA (2018.10.23-2018.10.24)] 2018 IEEE International Symposium on Technologies for Homeland Security (HST) - Three-Dimensional Radiative Transfer for Hyperspectral Imaging Classification and Detection
摘要： Hyperspectral image exploitation algorithms typically require inputs of re?ectance spectra, which must be retrieved from the observed radiance spectra. This retrieval process is very challenging under the complex illumination conditions typical of urban settings due the in?uence of three-dimensional structure in the form of shadows and re?ections, which must be taken into account by the algorithms. In order to advance the state of the art on this problem, MIT Lincoln Laboratory recently conducted an airborne data collection experiment in a light urban environment that included hyperspectral, laser radar, and pan-chromatic modalities. A comprehensive ground truth data set was collected and extensive efforts were directed at sensor characterization to enable the development of hyper-spectral exploitation algorithms. Additionally, the laboratory is developing an extremely compact but high performance imaging spectrometer that will be ideal for the data collections required by this new image processing paradigm.
关键词： Remote sensing,Hyperspectral imaging,Ladar imaging
更新于2025-09-04 15:30:14