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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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Domain Adaptation With Discriminative Distribution and Manifold Embedding for Hyperspectral Image Classification
摘要： Hyperspectral remote sensing image classification has drawn a great attention in recent years due to the development of remote sensing technology. To build a high confident classifier, the large number of labeled data is very important, e.g., the success of deep learning technique. Indeed, the acquisition of labeled data is usually very expensive, especially for the remote sensing images, which usually needs to survey outside. To address this problem, in this letter, we propose a domain adaptation method by learning the manifold embedding and matching the discriminative distribution in source domain with neural networks for hyperspectral image classification. Specifically, we use the discriminative information of source image to train the classifier for the source and target images. To make the classifier can work well on both domains, we minimize the distribution shift between the two domains in an embedding space with prior class distribution in the source domain. Meanwhile, to avoid the distortion mapping of the target domain in the embedding space, we try to keep the manifold relation of the samples in the embedding space. Then, we learn the embedding on source domain and target domain by minimizing the three criteria simultaneously based on a neural network. The experimental results on two hyperspectral remote sensing images have shown that our proposed method can outperform several baseline methods.
关键词： neural network,hyperspectral image classification,maximum mean discrepancy (MMD),remote sensing,Domain adaptation,manifold embedding
更新于2025-09-23 15:22:29
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Hyperspectral Image Denoising Based on Spectral Dictionary Learning and Sparse Coding
摘要： Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise assumption was studied. Considering the spectral information of HSI data, a novel dictionary learning method based on an online method is proposed to train the spectral dictionary for denoising. With the spatial–contextual information in the noisy HSI exploited as a priori knowledge, the total variation regularizer is introduced to perform the sparse coding. Finally, sparse reconstruction is implemented to produce the denoised HSI. The performance of the proposed approach is better than the existing algorithms. The experiments illustrate that the denoising result obtained by the proposed algorithm is at least 1 dB better than that of the comparison algorithms. The intrinsic details of both spatial and spectral structures can be preserved after significant denoising.
关键词： image processing,hyperspectral image,spectral dictionary,image denoising,sparse coding
更新于2025-09-23 15:22:29
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Global and Local Real-Time Anomaly Detectors for Hyperspectral Remote Sensing Imagery
摘要： Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. A well-known algorithm for hyperspectral anomaly detection is the RX detector. A number of variations have been studied since then, including global and local versions for different type of anomalies. Aiming at a real-time requirement for practical applications, this paper extends the concept of global and local anomaly detectors to be real-time detectors. The algorithms exploit the fact that a true real-time detector must produce its output in a causal manner and at the same time as an input comes in. Taking advantage of the Woodbury matrix identity, the global and local real-time detectors can be implemented and processed pixel-by-pixel in real time. Both synthetic and real hyperspectral imagery are conducted to demonstrate their performance.
关键词： sliding local window,Woodbury matrix identity,hyperspectral remote sensing,anomaly detection,real-time
更新于2025-09-23 15:22:29
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Fluorescence Hyperspectral Imaging of Oil Samples and Its Quantitative Applications in Component Analysis and Thickness Estimation
摘要： The fast response and analysis of oil spill accidents is important but remains challenging. Here, a compact fluorescence hyperspectral system based on a grating-prism structure able to perform component analysis of oil as well as make a quantitative estimation of oil film thickness is developed. The spectrometer spectral range is 366–814 nm with a spectral resolution of 1 nm. The feasibility of the spectrometer system is demonstrated by determining the composition of three types of crude oil and various mixtures of them. The relationship between the oil film thickness and the fluorescent hyperspectral intensity is furthermore investigated and found to be linear, which demonstrates the feasibility of using the fluorescence data to quantitatively measure oil film thickness. Capable of oil identification, distribution analysis, and oil film thickness detection, the fluorescence hyperspectral imaging system presented is promising for use during oil spill accidents by mounting it on, e.g., an unmanned aerial vehicle.
关键词： K-means clustering,principal component analysis,fluorescence hyperspectral imaging,oil detection
更新于2025-09-23 15:22:29
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Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
摘要： With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.
关键词： classification,feature extraction,multi-sensor fusion,remote sensors,deep learning,hyperspectral image
更新于2025-09-23 15:22:29
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HCKBoost: Hybridized composite kernel boosting with extreme learning machines for hyperspectral image classification
摘要： Utilization of contextual information on the hyperspectral image (HSI) analysis is an important fact. On the other hand, multiple kernels (MKs) and hybrid kernels (HKs) in connection with kernel methods have significant impact on the classification process. Activation of spatial information via composite kernels (CKs) and exploiting hidden features of the spectral information via MKs and HKs have been shown great successes on hyperspectral images separately. In this work, it is aimed to aggregate composite and hybrid kernels to obtain high classification success with a boosting based community learner. Spatial and spectral hybrid kernels are constructed using weighted convex combination approach with respect to individual success of the predefined kernels. Composite kernel formation is realized with certain proportions of the obtained spatial and spectral HKs. Computationally fast and effective extreme learning machine (ELM) classification algorithm is adopted. Since, main objective is to obtain optimal kernel during ensemble formation operation, unlike the standard MKL methods, proposed method disposes off the complex optimization processes and allows multi-class classification. Pavia University, Indian Pines, and Salinas hyperspectral scenes that have ground truth information are used for simulations. Hybridized composite kernels (HCK) are constructed using Gaussian, polynomial, and logarithmic kernel functions with various parameters and then obtained results are presented comparatively along with the state-of-the-art MKL, CK, sparse representation, and single kernel based methods.
关键词： Hyperspectral images,Composite kernels,Adaptive boosting,Extreme learning machines,Hybrid kernels
更新于2025-09-23 15:22:29
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Extended attribute profiles on GPU applied to hyperspectral image classification
摘要： Extended pro?les are an important technique for modelling the spatial information of hyperspectral images at different levels of detail. They are used extensively as a pre-processing stage, especially in classi?cation schemes. In particular, attribute pro?les, based on the application of morphological attribute ?lters to the connected components of the image, have been shown to provide very good results. In this paper we present a parallel implementation of the attribute pro?les in CUDA for multispectral and hyperspectral imagery considering the attributes area and standard deviation. The pro?le computation is based on the max-tree approach but without building the tree itself. Instead, a matrix-based data structure is used along with a recursive ?ooding (component merging) and ?lter process. Additionally, a previous feature extraction stage based on wavelets is applied to the hyperspectral image in order to extract the most valuable spectral information, reducing the size of the resulting pro?le. This scheme ef?ciently exploits the thousands of available threads on the GPU, obtaining a considerable reduction in execution time as compared to the OpenMP CPU implementation.
关键词： Remote sensing,Attribute pro?les,GPU,Real-time,Hyperspectral,Supervised classi?cation
更新于2025-09-23 15:22:29
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GPU Acceleration of Clustered DPCM for Lossless Compression of Hyperspectral Images
摘要： With the development of remote sensing technology, spatial and spectral resolutions of hyperspectral images have become increasingly dense. In order to overcome difficulties in the storage, transmission and manipulation of hyperspectral images, an effective compression algorithm is requisite. The Clustered Differential Pulse Code Modulation (C-DPCM), which is a prediction-based hyperspectral lossless compression algorithm, can achieve a relatively high compression ratio, but its efficiency still requires improvement. This paper presents a parallel implementation of the C-DPCM algorithm on Graphics Processing Units (GPUs) with the Compute Unified Device Architecture (CUDA), which is a parallel computing platform and programming model developed by NVIDIA. Three optimization strategies are utilized to implement the C-DPCM algorithm in parallel, including a version that uses shared memory and registers, a version that employs multi-stream, and a version that uses multi-GPU. In addition, we studied how to assign all classes to each GPU to minimize the processing time. Finally, we reduced the compression time from approximately half an hour to an hour to several seconds, with almost no loss in accuracy.
关键词： C-DPCM,GPU,CUDA,Hyperspectral image lossless compression
更新于2025-09-23 15:22:29
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Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution
摘要： This paper presents a hyperspectral image (HSI) super-resolution method which fuses a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t-product) based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and LR-HSI is built using t-product which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, Alternating Direction Method of Multipliers (ADMM) is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods.
关键词： tensor dictionary learning,Hyperspectral image,nonlocal patch tensor,tensor sparse coding,super-resolution
更新于2025-09-23 15:22:29
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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 - Anomaly Preserving Content-Aware Hyperspectral Image Size Reduction
摘要： With the increase in the number and availability of imaging sensors, data size or dimensionality reduction, compression, archiving and retrieval algorithms are gaining importance. Whereas in the past, for hyperspectral images, the size reduction has been mostly concerned with the spectral domain, the increase in the number and spatial sizes of the hyperspectral images has raised the question whether a spatial reduction is also feasible. However, this reduction should be conducted in a content-aware fashion and should preserve the important and relevant information in the scene whether for archiving and retrieval purposes, or for following image processing tasks. In this work, an anomaly-preserving content-aware size reduction approach is proposed for hyperspectral images. The approaches utilizes seam carving with a spatial homogeneity energy function to preserve anomalies while performing reduction. Synthetic and real datasets are used to validate the proposed methodology.
关键词： size reduction,Anomaly,content-aware,hyperspectral,seam carving
更新于2025-09-23 15:21:21