- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Hierarchical Sparse Bayesian Learning with Beta Process Priors for Hyperspectral Imagery Restoration
摘要： Restoration is an important area in improving the visual quality, and lays the foundation for accurate object detection or terrain classification in image analysis. In this paper, we introduce Beta process priors into hierarchical sparse Bayesian learning for recovering underlying degraded hyperspectral images (HSI), including suppressing the various noises and inferring the missing data. The proposed method decomposes the HSI into the weighted summation of the dictionary elements, Gaussian noise term and sparse noise term. With these, the latent information and the noise characteristics of HSI can be well learned and represented. Solved by Gibbs sampler, the underlying dictionary and the noise can be efficiently predicted with no tuning of any parameters. The performance of the proposed method is compared with state-of-the-art ones and validated on two hyperspectral datasets, which are contaminated with the Gaussian noises, impulse noises, stripes and dead pixel lines, or with a large number of data missing uniformly at random. The visual and quantitative results demonstrate the superiority of the proposed method.
关键词： restoration,beta process,hyperspectral image,hierarchical sparse Bayesian learning
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 - Hyperspectral Band Selection Using Pair-Wise Constraint and Band-Wise Correlation
摘要： In this paper, a novel supervised band selection (BS) method based on pair-wise constraint and band-wise correlation information is proposed for the dimension reduction of hyperspectral images. On the one hand, the band-wise correlation information, is used for selecting band-subset with lower redundancy and higher representation. This process is achieved by first partitioning all spectral bands into continuous groups and then calculate a band-wise correlation matrix within each group, which is used later for selecting bands of more representation and lower redundancy. On the other hand, pair-wise supervised information (i.e., whether a pair of labeled samples are from the same class) is exploited for selecting band-subsets to better discriminate different classes. That is, a few bands are adaptively chosen for each pair of labeled samples according to spectral-similarity, to ensure that the distance between samples from different classes is far and keep sample-pair from same class close. By the joint use of both pair-wise constraint information and band-wise correlation information, the proposed BS method can lead to select optimal band-subsets with low-redundancy, high-representation and high-discrimination. Experimental results demonstrate the effectiveness of the proposed BS method.
关键词： Band selection,Hyperspectral image,Pair-wise constraint,Band-wise correlation
更新于2025-09-09 09:28:46
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Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification
摘要： Hyperspectral image (HSI), with hundreds of narrow and adjacent spectral bands, supplies plentiful information to distinguish various land-cover types. However, these spectral bands ordinarily contain a lot of redundant information, leading to the Hughes phenomenon and an increase in computing time. As a popular dimensionality reduction technology, band/feature selection is indispensable for HSI classification. Based on improved subspace decomposition (ISD) and the artificial bee colony (ABC) algorithm, this paper proposes a band selection technique known as ISD-ABC to address the problem of dimensionality reduction in HSI classification. Subspace decomposition is achieved by calculating the correlation coefficients between adjacent bands and using the visualization result of the HSI spectral curve. The artificial bee colony algorithm is first applied to optimize the combination of selected bands with the guidance of ISD and maximum entropy (ME). Using the selected band subset, support vector machine (SVM) with five-fold cross validation is applied for HSI classification. To evaluate the effectiveness of the proposed method, experiments are conducted on two AVIRIS datasets (Indian Pines and Salinas) and a ROSIS dataset (Pavia University). Three indices, namely, overall accuracy (OA), average accuracy (AA) and kappa coefficient (KC), are used to assess the classification results. The experimental results successfully demonstrate that the proposed method provides good classification accuracy compared with six other state-of-the-art band selection techniques.
关键词： dimensionality reduction,band selection,Hyperspectral image,ABC algorithm,subspace decomposition
更新于2025-09-09 09:28:46
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Self-Supervised Feature Learning With CRF Embedding for Hyperspectral Image Classification
摘要： The challenges in hyperspectral image (HSI) classification lie in the existence of noisy spectral information and lack of contextual information among pixels. Considering the three different levels in HSIs, i.e., subpixel, pixel, and superpixel, offer complementary information, we develop a novel HSI feature learning network (HSINet) to learn consistent features by self-supervision for HSI classification. HSINet contains a three-layer deep neural network and a multifeature convolutional neural network. It automatically extracts the features such as spatial, spectral, color, and boundary as well as context information. To boost the performance of self-supervised feature learning with the likelihood maximization, the conditional random field (CRF) framework is embedded into HSINet. The potential terms of unary, pairwise, and higher order in CRF are constructed by the corresponding subpixel, pixel, and superpixel. Furthermore, the feedback information derived from these terms are also fused into the different-level feature learning process, which makes the HSINet-CRF be a trainable end-to-end deep learning model with the back-propagation algorithm. Comprehensive evaluations are performed on three widely used HSI data sets and our method outperforms the state-of-the-art methods.
关键词： self-supervision,feature learning,convolutional neural network (CNN),Conditional random field (CRF),hyperspectral image (HSI) classification
更新于2025-09-09 09:28:46
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[IEEE 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Beijing (2018.8.19-2018.8.20)] 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Collaborative Classification of Hyperspectral and LIDAR Data Using Unsupervised Image-to-Image CNN
摘要： Currently, how to efficiently exploit useful information from multi-source remote sensing data for better Earth observation becomes an interesting but challenging problem. In this paper, we propose an collaborative classification framework for hyperspectral image (HSI) and Light Detection and Ranging (LIDAR) data via image-to-image convolutional neural network (CNN). There is an image-to-image mapping, learning a representation from input source (i.e., HSI) to output source (i.e., LIDAR). Then, the extracted features are expected to own characteristics of both HSI and LIDAR data, and the collaborative classification is implemented by the deep CNN. Experimental results on two real remote sensing data sets demonstrate the effectiveness of the proposed framework.
关键词： Hyperspectral Image,Convolutional Neural Network,Data Fusion,Deep Learning
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 - ROBUST PCANet for Hyperspectral Image Change Detection
摘要： Deep learning is an effective tool for handling high-dimensional data and modeling nonlinearity, which can tackle the hyper-spectral data well. Usually deep learning methods need a large number of training samples. However, there is no labeled data for training in change detection (CD). Considering these, this paper develops an unsupervised Robust PCA network (RPCANet) for hyperspectral image CD task. The main contributions of this work are twofold: 1) An unsupervised convolutional neural networks named RPCANet is proposed to handle the hyperspectral image CD; 2) An effective CD framework using the RPCANet and change vector analysis (CVA) is designed to achieve better CD performance with more powerful features. Experimental results on real hyperspectral datasets demonstrate the effectiveness of the proposed method.
关键词： change detection (CD),Robust PCA network (RPCANet),Hyperspectral image,change vector analysis (CVA)
更新于2025-09-09 09:28:46
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Subspace-based multitask learning framework for hyperspectral imagery classification
摘要： Subspace-based models have been widely applied for hyperspectral imagery applications, especially for classification. The main principle of these methods is based on the fact that the original image can approximately lie on a lower-dimensional subspace. However, due to the existence of mixed samples, the subspace projection is unstable and affected by the selection of training samples, such that may lead to poor characterization and classification performances. In order to improve the robustness and characterization ability of the subspace-based classification models, this paper proposes a novel subspace-based multitask learning framework. In particular, the original image is first projected to the multiple subspaces in different branches. Then, the support vector machine (SVM) classifier is applied in each branch to deal with the projected data sets. With a consideration of integrating the spatial information, an extended step is provided including the process of a Markov Random Field (MRF) based on the result of SVM. Finally, the classification result is obtained by a decision fusion process. Experimental results on three real hyperspectral data sets demonstrate the improvements on classification performance of the proposed methods over other related methods.
关键词： Classification,Subspace projection,Support vector machine,Hyperspectral image
更新于2025-09-09 09:28:46
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Hyperspectral image classification using a spectral–spatial random walker method
摘要： This article proposes a spectral–spatial method for classification of hyperspectral images (HSIs) by modifying traditional random walker (RW). The proposed method consists of suggesting two main modifications. First, to construct a spatial edge weighting function, low-frequency edge weighting function is proposed. In this function, the detail weights are removed. Second, to enhance the classification accuracy, a fusion of spectral and spatial Laplacian matrix in RW is suggested. This fusion can improve the classification performances compared to traditional RW using only spatial Laplacian matrix. In comparison with some of the state-of-the-art RW and spectral–spatial classifier methods, the experimental results of the proposed method (spectral–spatial RW) show that the proposed method significantly increases the classification accuracy of HSI.
关键词： spectral–spatial random walker,edge weighting function,Hyperspectral image classification,Laplacian matrix
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 - Selecting Band Subsets from Hyperspectral Image Through a Novel Evolutionary-Based Strategy
摘要： Hyperspectral dimensionality reduction by optimal band selection attracts wide attention recently because a few pivotal and physically meaningful bands can not only represent the whole image cube without losing effectiveness but also mitigate the computational burden. In this paper, we construct an efficient searching strategy based on the clonal selection principle to optimize a geometry-based criterion named maximum ellipsoid volume (MEV). The main contributions are two-fold: 1) a subtle relationship that can accelerate the calculation of the criterion and 2) an evolutionary strategy to relieve the heavy computational burden of obtaining the desired bands from numerous quality candidates. The experimental result on a real hyperspectral data demonstrates that the proposed method is effective.
关键词： maximum ellipsoid volume,Band selection,hyperspectral image,clonal selection principle
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 - Combination of Band Selection and Weighted Spatial-Spectral Method for Hyperspectral Image Classification
摘要： In this paper we propose a new method for land cover classification in hyperspectral remote sensing images by combining band selection with weighted spatial-spectral feature fusion. Spectral information for each pixel is represented by a spectral curve over all the bands. Spatial information is represented by a Bag of visual Words model within a small region around each pixel. A cluster-based band selection method is used before spatial feature extraction to reduce the computation complexity. Then spectral and spatial feature weights are learnt under a Support Vector Machine framework, obtaining a balance between the two basis features for each class. Classification results on three popular hyperspectral remote sensing images demonstrate that the proposed method can yield a higher accuracy and a lower false alarm rate compared with the other similar classifiers.
关键词： bag of words,Hyperspectral image,spatial-spectral,classification,band select
更新于2025-09-09 09:28:46