- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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[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 - Subspace Multinomial Logistic Regression Ensemble for Classification of Hyperspectral Images
摘要： Exploiting multiple complementary classifiers in an ensemble framework has shown to be effective for improving hyperspectral image classification results, specially when the training samples are limited. With a different principle and based on this assumption that hyperspectral feature vectors effectively lie in a low-dimensional subspace, the subspace-based techniques have shown great classification performance. In this work, we propose a new ensemble method for accurate classification of hyperspectral images, which exploits the concept of subspace projection. For this purpose, we extend the subspace multinomial logistic regression classifier (MLRsub) to learn from multiple random subspaces for each class. More specifically, we impose diversity in constructing MLRsub by randomly selecting bootstrap samples from the training set and subsets of the original hyperspectral feature space, which lead to generate different class subspace features. Experimental results, conducted on two real hyperspectral datasets, indicate that the proposed method provides significant classification results in comparison with other state-of-the-art approaches.
关键词： Hyperspectral images,subspace multinomial logistic regression,ensemble-based approaches,remote sensing,classification
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 - Spectral-Spatial Hyperspectral Image Classification via Locality and Structure Constrained Low-Rank Representation
摘要： Low-rank representation (LRR) has been applied widely in most fields due to its considerable ability to explore the low-dimensional subspace embedding in high-dimensional data. However, there are still some problems that LRR can’t effectively exploit the local structure and the representation for the given data is not discriminative enough. To tackle the above issues, we propose a novel locality and structure constrained low-rank representation (LSLRR) for hyperspectral image (HSI) classification. First, a distance metrics, which combines spectral and spatial similarity, is proposed to constrain the local structure. This makes two pixels in HSI with small distance have high similarity. Second, we exploit the classwise block-diagonal structure for the training data to learn the more discriminative representation for the testing data. And the experimental results verify the effectiveness and superiority of LSLRR comparing with other state-of-the-art methods.
关键词： low-rank representation,block-diagonal structure,hyperspectral image classification
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 - Spectral Identification of Native and Non-Native Plant Species for Biodiversity Assessments
摘要： Invasive species are one of the main drivers of biodiversity loss. In the past decade, the development of environmental spectroscopy, both field spectrometers and airborne imaging spectrometers, has allowed progress in identifying individual species from remote sensing data. However, use of environmental spectroscopy for species identification needs understanding at a more fundamental level, especially the development of generalized methodologies and rules for detection and mapping, which is an area of active research today. These issues will be explored using examples from a wide range of habitats and site conditions, towards the development of a robust methodology to identify native and non-native species.
关键词： invasive species,ecosystem function,biodiversity,assessment,hyperspectral
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 - A New Hyperspectral Pansharpening Method With Intrisic Image Decomposition
摘要： The component substitution (CS) and multiresolution analysis (MRA) based methods have been well adopted in hyperspectral pansharpening. The major contribution of this paper is a novel MRA and CS hybrid framework based on the intrinsic image decomposition. First, the weighted least squares (WLS) filter is performed on the sharpened panchromatic (P) image to extract the high-frequency component. Then, the intrinsic image decomposition (IID) is adopted to decompose the interpolated hyperspectral (H) image into the illumination and reflectance components. Finally, the detail map is generated by making a proper compromise between the high-frequency component of the P image and the illumination component of the H image. The detail map further refined by the information ratio of different bands of the H image is injected into each band of the interpolated H image. Experimental results indicate that the proposed method achieves a better fusion result than several state-of-the-art hyperspectral pansharpening methods.
关键词： intrinsic image decomposition (IID),panchromatic (P) image,hyperspectral (H) image,pansharpening
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 (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deriving Hyper Spectral Reflectance Spectra from UAV Data Collected in Changeable Illumination Conditions to Assess Vegetationcondition
摘要： Hyperspectral imaging is a recent development in the evolving field of UAV remote sensing and a new avenue for habitat condition monitoring. We present preliminary results of a pilot study evaluating the use of UAV hyperspectral imaging to detect early stages of Acute Oak Decline (AOD) in a broadleaved forest in the UK. Field observations revealed that, compared to asymptomatic trees, leaves of symptomatic trees show lower levels of water and higher reflectance in the near-infrared part of the spectrum. The observed changes in leaf level reflectance spectra were subtle but statistically significant. Normalised hyperspectral UAV canopy radiance spectra suggest the opposite is occurring: symptomatic trees have lower near-infrared radiances. UAV campaigns suffer from changing illumination conditions and in our case normalizing between image frames is not sufficient. We plan to derive reflectance spectra to enable us to adequately evaluate the observed differences between leaves and canopies.
关键词： Acute Oak Decline,UAV,Hyperspectral
更新于2025-09-09 09:28:46
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A Liquid Crystal Tunable Filter-Based Hyperspectral LiDAR System and Its Application on Vegetation Red Edge Detection
摘要： In this letter, a hyperspectral light detection and ranging (HSL) with 10-nm spectral resolution was designed and tested using a supercontinuum laser source. The major difference between the prototyped HSL and similar instruments was that a liquid crystal tunable ?lter (LCTF) was installed before the avalanche photodiode detector and utilized as a spectroscopic device. The design allowed continuous wavelength selection of the backscattered echoes in the time dimension. Moreover, for general accuracy evaluation of range measurement and spectral measurement, laboratory experiments for vegetation red edge detection were performed using the prototyped HSL to assess its feasibility on agriculture application. Yellow and green leaves from aloe and dracaena plants were measured by the LCTF-HSL for detecting the corresponding “red edge” position. Spectral pro?les measured by an SVC-HR-1024 spectrometer which is designed by SVC company were used as a reference to evaluate the measurements of HSL. The comparison results showed that the red edge positions extracted from the two individual measurements were similar, thus indicating that the LCTF-based high-resolution HSL was effective for this application.
关键词： red edge,liquid crystal tunable ?lter (LCTF),Hyperspectral light detection and ranging (LiDAR) (HSL)
更新于2025-09-09 09:28:46
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Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification
摘要： Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models typically limit their application in on-board remote sensing scenarios. In this context, low-power consumption architectures are promising platforms that may provide acceptable on-board computing capabilities to achieve satisfactory classification results with reduced energy demand. For instance, the new NVIDIA Jetson Tegra TX2 device is an efficient solution for on-board processing applications using deep-learning (DL) approaches. So far, very few efforts have been devoted to exploiting this or other similar computing platforms in on-board remote sensing procedures. This letter explores the use of low-power consumption architectures and DL algorithms for HSI classification. The conducted experimental study reveals that the NVIDIA Jetson Tegra TX2 device offers a good choice in terms of performance, cost, and energy consumption for on-board HSI classification tasks.
关键词： hyperspectral image (HSI) classification,Deep learning (DL),low-power consumption architectures,embedded computing
更新于2025-09-09 09:28:46
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A Low-rank Tensor Model for Hyperspectral Image Sparse Noise Removal
摘要： Hyperspectral image (HSI) has been widely used in target detection and classification. However, various kinds of noise in HSIs affect the applications of HSIs. In this paper, we propose a low-rank (LR) tensor recovery model to remove noise. Considering that the HSI is a 3-D HSI data, and the underlying LR tensor property is used in the model. Then, according to the similarity of adjacent bands images, the regularization on the difference of adjacent bands images is considered. The experiments of removing noise from different noisy HSIs show that our method can achieve better performance on removing sparse noise, especially for strips removal.
关键词： low-rank,Hyperspectral image,tensor,sparse noise removal
更新于2025-09-09 09:28:46
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[ACM Press the 3rd International Conference - Seoul, Republic of Korea (2018.08.22-2018.08.24)] Proceedings of the 3rd International Conference on Biomedical Signal and Image Processing - ICBIP '18 - Application of Hyperspectral Imaging for Surface Defects Detection of Jujube
摘要： Hyperspectral imaging system with the range of 450–990 nm was developed to obtain the reflection spectral of "Kaohsiung 11" jujube with surface defects. Principal component analysis (PCA) was used to reduce the spectral dimensionality of hyperspectral image data and determine the wavebands used by band ratio method for quick detection of jujube surface defects. Two-band ratio (Q550/680) images were successfully used to differentiate surfaces with defects such as decay, rusty, fungus infection and insect bites from the sound surface. Due to the fact that rusty surface of "Kaohsiung 11" has no effect on the quality of flavor and texture, a threshold value for slope of reflectance spectra between 700 nm and 710 nm was used to differentiate the rusty region from other defect regions. The glare due to specular reflection from smooth and waxy surface of jujube may lead to error when the differentiation of surfaces with defects and sound surfaces was performed. The findings of this study can be used as a basis for developing effective algorithm to identify different types of defects on jujube surface.
关键词： Fruit defects,Jujube,Principal component analysis,Hyperspectral imaging
更新于2025-09-04 15:30:14
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Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier
摘要： Hyperspectral remote sensing inversion models utilize spectral information over optically shallow waters to retrieve optical properties of the water column, bottom depth and re?ectance, with the latter used in benthic classi?cation. Accuracy of these retrievals is dependent on the spectral endmember(s) used to model the bottom re?ectance during the inversion. Without prior knowledge of these endmember(s) current approaches must iterate through a list of endmember—a computationally demanding task. To address this, a novel lookup table classi?cation approach termed HOPE-LUT was developed for selecting the likely benthic endmembers of any hyperspectral image pixel. HOPE-LUT classi?es a pixel as sand, mixture or non-sand, then the latter two are resolved into the three most likely classes. Optimization subsequently selects the class (out of the three) that generated the best ?t to the remote sensing re?ectance. For a coral reef case, modeling results indicate very high benthic classi?cation accuracy (>90%) for depths less than 4 m of common coral reef benthos. These accuracies decrease substantially with increasing depth due to the loss of bottom information, especially the spectral signatures. We applied this technique to hyperspectral airborne imagery of Heron Reef, Great Barrier Reef and generated benthic habitat maps with higher classi?cation accuracy compared to standard inversion models.
关键词： hyperspectral,remote sensing,benthic classi?cation,coral reef,heron reef
更新于2025-09-04 15:30:14