- 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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Hyperspectral Image Classification Based on Belief Propagation with Multi-features and Small Sample Learning
摘要： In order to solve the "massive information but low accuracy" problem of hyperspectral image (HSI) classification, a novel HSI classification method MFSSL-BPMRF based on belief propagation (BP) Markov random field (MRF) using multi-features and small sample learning (MFSSL) is proposed in this paper. Firstly, an extended morphological multi-attributes profiles algorithm is used to extract spatial information of HSI, and a spatial–spectral multi-features fusion model is established to improve classification results. Then, BPMRF is used for image segmentation and classification because of its superiority in the spatial–spectral combination classification. MRF can describe the spatial distribution features of ground objects based on neighborhood model, and the spectral information of pixels can be integrated into the calculation of conditional probability. BP is used to learn the marginal probability distributions from the multi-features fusion information. Finally, the small sample training set is selected to enhance the computational efficiency. In the experiments of several hyperspectral images, the proposed method provides higher classification accuracy than other methods, and it is efficient for the classification with limited labeled training samples.
关键词： Features fusion,Belief propagation,Hyperspectral image,Classification
更新于2025-09-23 15:23:52
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Classification of Urban Hyperspectral Remote Sensing Imagery Based on Optimized Spectral Angle Mapping
摘要： Hyperspectral remote sensing imagery provides highly precise spectral information. Thus, it is suitable for the land use classification of urban areas that are composed of complicated structures. In this study, a new spectral angle and vector mapping (SAVM) classification method, which adds a factor based on ''the differences in the spectral vector lengths'' among image pixels to the spectral angle mapping (SAM) classification method, is proposed. The SAM and SAVM methods were applied to classify the aerial hyperspectral digital imagery collection experiment imagery acquired from the business district of Washington, DC, USA. The results demonstrated that the overall classification accuracy of the SAM was 64.29%, with a Kappa coefficient of 0.57, while the overall classification accuracy of the SAVM was 81.06%, with a Kappa coefficient of 0.76. The overall classification accuracy was improved by 16.77% by the SAVM, indicating that the use of a SAVM classification method that considers both the spectral angle between the reference spectrum and the test spectrum and the differences in the spectral vector lengths among image pixels can improve the classification accuracy of urban area with hyperspectral remote sensing imagery.
关键词： Hyperspectral imagery,Spectral angle and vector mapping (SAVM),Classification,Spectral angle mapping (SAM)
更新于2025-09-23 15:23:52
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Distinguishing between closely related species of Allium and of Brassicaceae by narrowband hyperspectral imagery
摘要： Classification of crop species is an actively studied topic in remote sensing using multi-spectral image sensors. Unfortunately, the spectral bands available in the multispectral imagery are broad and limited in number to classify the crop species. In this paper, we propose optimal spectral bands to classify Allium (garlic and onion) and Brassicaceae (Chinese cabbage and radish) by using higher-dimensional data from hyperspectral imagery. A decision-tree classifier was used to determine the optimal method to use the high-dimensional data. The high-dimensional data were analysed for all growth stages and considering bandwidths with different full width at half maximum (FWHM) values at 25, 40, 50 and 80 nm. The spectral bands selected for Allium were differentiated into green, blue, and NIR bands for each growth stage. The results show that Allium can be classified clearly as overall accuracy (OA) 1 and kappa coefficient 1 for all FWHM based on March 22 data. For each April 19 and May 12 data, the decision-tree classifier with each 80 nm FWHM and 50 nm FWHM yielded a better classification accuracy of more than OA 0.921 and kappa coefficient 0.839 than other FWHM. The spectral bands selected for Brassicaceae were found to be similar to blue band for all growth stages. Brassicaceae was classified clearly for all FWHM based on October 27 data. Also, Brassicaceae was classified clearly for 25 nm FWHM based on November 25 data and OA, kappa coefficient for 40 nm FWHM and 50 nm FWHM are high as 0.974, 0.947 respectively.
关键词： Decision-tree classifier,Hyperspectral imagery,Classification,Full width at half maximum,Spectral band
更新于2025-09-23 15:23:52
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Visual detection of the moisture content of tea leaves with hyperspectral imaging technology
摘要： Hitherto, the rapid and nondestructive determination of the moisture content of tea leaves is still an unresolved issue because the upward facing surfaces of tea leaves lying on a conveyor belt are randomly chosen by the collapse of the leaves onto their front side or back side. To study the above issue, hyperspectral images of both the front side and back side of tea leaves on a conveyor belt were captured in the lab to simulate a practical production environment, and LS-SVR models with Rv2 values of 0.951 and 0.918 for the front side and back side, respectively, were established based on their characteristic spectral bands. To ensure that the spectrum of each pixel can be correctly imported into its corresponding model, a logistic regression classifier with a correct classification rate of 100 % was designed to identify the front side and back side of the leaves. Finally, a distribution map of the moisture content of the tea leaves was generated successfully according to the following steps: (1) Extracting the average spectrum of each leaf; (2) Identifying which side of the leaf the spectrum belongs to; (3) Importing the adjusted spectrum of each pixel into its corresponding regression model; and (4) Generating a distribution map of the moisture content. This research creatively provides a scheme for detecting the moisture content of tea leaves.
关键词： moisture content,front side,hyperspectral imaging,tea leaf,back side
更新于2025-09-23 15:23:52
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CubeDMA – Optimizing three-dimensional DMA transfers for hyperspectral imaging applications
摘要： Onboard computing is one of the principal needs in space-related technology in the recent years. In particular, onboard hyperspectral imaging (HSI) processing has advanced significantly. Due to advances in sensor technology, onboard HSI processing continuously meets new challenges related to increasing dataset size, limited processing time and limited communication links. High throughput and data reduction are crucial for satisfying real-time constraint and for preserving transmission bandwidth. For systems capable of accommodating a wide range of processing algorithms, there is a need for a flexible communication infrastructure that can provide fast access to/from memory in different access patterns. In this paper, existing FPGA-related Direct Memory Access (DMA) solutions have been evaluated, and a new DMA solution tailored for hyperspectral images has been proposed. Results show that the proposed DMA core, CubeDMA, handles targeted memory access patterns in more efficient manner than existing solutions while being resource efficient.
关键词： HSI cube,DMA,On-board processing,Direct memory access,Hyperspectral imaging
更新于2025-09-23 15:23:52
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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 - Hyper-Laplacian Regularized Low-Rank Tensor Decomposition for Hyperspectral Anomaly Detection
摘要： This paper presents a novel method for hyperspectral anomaly detection considering the spectral redundancy and exploiting spectral-spatial information at the same time. We proposed a Hyper-Laplacian regularized low-rank tensor decomposition method combing with dimensionality reduction framework. Firstly, k-means++ algorithm is implemented to spectral bands and centers of each group are selected to reduce the HSI dimensionality in spectral direction. To jointly utilize spectral-spatial information, the cubic data (two spatial dimensions and one spectral dimension) is treated as a 3-order tensor. Then the non-local self-similarity is fully explored in our method. For the reason to reduce the ringing artifacts caused by over-lapped segmentation in exploring the non-local self-similarity, we introduce the hyper-Laplacian constrained low-rank tensor decomposition and we get the separated background and residual parts. Finally, to eliminate the effect of Gaussian noise, we use local-RX basic detector to detect the residual matrix. Experimental results on two real hyperspectral data sets verified the effectiveness of the proposed algorithms for HSI anomaly detection.
关键词： low-rank tensor decomposition,hyperspectral anomaly detection,Dimensionality reduction
更新于2025-09-23 15:23:52
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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 Anomaly Detection Using Compressed Columnwise Robust Principal Component Analysis
摘要： This paper proposes a compressed columnwise robust principal component analysis (CCRPCA) method for hyperspectral anomaly detection. The CCRPCA improves the regular RPCA by using the Hadamard random projection and constraining the columnwise structure of sparse anomaly matrix. The Hadamard random projection reduces the computational cost of the hyperspectral data, and the columnwise sparse structure alleviates negative effects from the anomalies on the columns of the background. The sparse anomaly matrix and the background matrix are estimated by optimizing a convex program, and the anomalies are estimated from nonzero columns of the compressed sparse matrix. Preliminary experiment result from the San Diego dataset shows that the CCRPCA outperforms four state-of-the-art detection methods in both the receiver operating characteristic curve and the area under curve.
关键词： anomaly detection,Hyperspectral imagery,columnwise robust principal component analysis,Hadamard random projection
更新于2025-09-23 15:23:52
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Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground Biomass of Wetland Vegetation Suaeda salsa
摘要： Suaeda salsa (S. salsa) has a significant protective effect on salt marshes in coastal wetlands. In this study, the abilities of airborne multispectral images, spaceborne hyperspectral images, and LiDAR data in spatial distribution extraction and aboveground biomass (AB) estimation of S. salsa were explored for mapping the spatial distribution of S. salsa AB. Results showed that the increasing spectral and structural features were conducive to improving the classification accuracy of wetland vegetation and the AB estimation accuracy of S. salsa. The fusion of hyperspectral and LiDAR data provided the highest accuracies for wetlands classification and AB estimation of S. salsa in the study. Multispectral images alone provided relatively high user's and producer's accuracies of S. salsa classification (87.04% and 88.28%, respectively). Compared to multispectral images, hyperspectral data with more spectral features slightly improved the Kappa coefficient and overall accuracy. The AB estimation reached a relatively reliable accuracy based only on hyperspectral data (R2 of 0.812, root-mean-square error of 0.295, estimation error of 24.56%, residual predictive deviation of 2.033, and the sums of squares ratio of 1.049). The addition of LiDAR data produced a limited improvement in the process of extraction and AB estimation of S. salsa. The spatial distribution of mapped S. salsa AB was consistent with the field survey results. This study provided an important reference for the effective information extraction and AB estimation of wetland vegetation S. salsa.
关键词： multispectral image,Suaeda salsa,LiDAR data,fine classification,Aboveground biomass,hyperspectral image
更新于2025-09-23 15:23:52
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Low-rank Bayesian tensor factorization for hyperspectral image denoising
摘要： In this paper, we present a low-rank Bayesian tensor factorization approach for hyperspectral image (HSI) denoising problem, where zero-mean white and homogeneous Gaussian additive noise is removed from a given HSI. The approach is based on two intrinsic properties underlying a HSI, i.e., the global correlation along spectrum (GCS) and nonlocal self-similarity across space (NSS). We first adaptively construct the patch-based tensor representation for the HSI to extract the NSS knowledge while preserving the property of GCS. Then, we employ the low rank property in this representation to design a hierarchical probabilistic model based on Bayesian tensor factorization to capture the inherent spatial-spectral correlation of HSI, which can be effectively solved under the variational Bayesian framework. Furthermore, through incorporating these two procedures in an iterative manner, we build an effective HSI denoising model to recover HSI from its corruption. This leads to a state-of-the-art denoising performance, consistently surpassing recently published leading HSI denoising methods in terms of both comprehensive quantitative assessments and subjective visual quality.
关键词： Hyperspectral image denoising,Global correlation along spectrum,Full Bayesian CP factorization,Nonlocal self-similarity,Variational Bayesian inference,Tensor rank auto determination
更新于2025-09-23 15:23:52
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A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification
摘要： Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features.
关键词： deep metric learning,determinantal point process (DPP),image classification,multiscale features,Convolutional neural network (CNN),hyperspectral image
更新于2025-09-23 15:23:52