- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Nonlocal Coupled Tensor CP Decomposition for Hyperspectral and Multispectral Image Fusion
摘要： Hyperspectral (HS) super-resolution, which aims at enhancing the spatial resolution of hyperspectral images (HSIs), has recently attracted considerable attention. A common way of HS super-resolution is to fuse the HSI with a higher spatial-resolution multispectral image (MSI). Various approaches have been proposed to solve this problem by establishing the degradation model of low spatial-resolution HSIs and MSIs based on matrix factorization methods, e.g., unmixing and sparse representation. However, this category of approaches cannot well construct the relationship between the high-spatial-resolution (HR) HSI and MSI. In fact, since the HSI and the MSI capture the same scene, these two image sources must have common factors. In this paper, a nonlocal tensor decomposition model for hyperspectral and multispectral image fusion (HSI-MSI fusion) is proposed. First, the nonlocal similar patch tensors of the HSI are constructed according to the MSI for the purpose of calculating the smooth order of all the patches for clustering. Then, the relationship between the HR HSI and the MSI is explored through coupled tensor canonical polyadic (CP) decomposition. The fundamental idea of the proposed model is that the factor matrices in the CP decomposition of the HR HSI’s nonlocal tensor can be shared with the matrices factorized by the MSI’s nonlocal tensor. Alternating direction method of multipliers is used to solve the proposed model. Through this method, the spatial structure of the MSI can be successfully transferred to the HSI. Experimental results on three synthetic data sets and one real data set suggest that the proposed method substantially outperforms the existing state-of-the-art HSI-MSI fusion methods.
关键词： nonlocal tensor,multispectral images (MSIs),Coupled canonical polyadic (CP) decomposition,data fusion,hyperspectral images (HSIs)
更新于2025-09-16 10:30:52
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Unsupervised Feature Extraction in Hyperspectral Images Based on Wasserstein Generative Adversarial Network
摘要： Feature extraction (FE) is a crucial research area in hyperspectral image (HSI) processing. Recently, due to the powerful ability of deep learning (DL) to extract spatial and spectral features, DL-based FE methods have shown great potentials for HSI processing. However, most of the DL-based FE methods are supervised, and the training of them suffers from the absence of labeled samples in HSIs severely. The training issue of supervised DL-based FE methods limits their application on HSI processing. To address this issue, in this paper, a novel modified generative adversarial network (GAN) is proposed to train a DL-based feature extractor without supervision. The designed GAN consists of two components, which are a generator and a discriminator. The generator can focus on the learning of real probability distributions of data sets and the discriminator can extract spatial–spectral features with superior invariance effectively. In order to learn upsampling and downsampling strategies adaptively during FE, the proposed generator and discriminator are designed based on a fully deconvolutional subnetwork and a fully convolutional subnetwork, respectively. Moreover, a novel min–max cost function is designed for training the proposed GAN in an end-to-end fashion without supervision, by utilizing the zero-sum game relationship between the generator and discriminator. Besides, the proposed modified GAN replaces the original Jensen–Shannon divergence with the Wasserstein distance, aiming to mitigate the unstability and difficulty of the training of GAN frameworks. Experimental results on three real data sets validate the effectiveness of the proposed method.
关键词： Convolutional neural network (CNN),hyperspectral images (HSIs),feature extraction (FE),generative adversarial network (GAN)
更新于2025-09-11 14:15:04
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[IEEE 2018 International Conference on Machine Learning and Cybernetics (ICMLC) - Chengdu, China (2018.7.15-2018.7.18)] 2018 International Conference on Machine Learning and Cybernetics (ICMLC) - Spectral-Spatial Sparse Subspace Clustering Based On Three-Dimensional Edge-Preserving Filtering For Hyperspectral Image
摘要： Due to the 3-D property of raw HSI cubes, 3-D spectral-spatial ?lter becomes an effective way for extracting spectral and spatial signatures from HSI. In this paper, a new spectral-spatial sparse subspace clustering framework based on 3-D edge-preserving ?ltering is proposed to improve the clustering accuracy of HSI. First, the initial sparse coef?cient matrix is obtained in the s-parse representation process of the classical SSC model. Then, a 3-D edge-preserving ?ltering is conducted on the initial sparse coef?cient matrix to get a more accurate one, which is used to build the similarity graph. Finally, the clustering result of H-SI data is achieved by employing the spectral clustering algorithm to the similarity graph. Speci?cally, the ?ltered matrix can not only capture the spectral-spatial features but the inten-sity differences. Experimental results demonstrate the poten-tial of including the proposed 3-D edge-preserving ?ltering in-to the SSC framework can improve the clustering accuracy.
关键词： Hyperspectral images (HSIs),Sparse subspace clustering (SSC),3-D edge-preserving ?lters (3-D EPFs),Intensity differences
更新于2025-09-10 09:29:36
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Hyperspectral Unmixing with Bandwise Generalized Bilinear Model
摘要： Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.
关键词： alternative direction method of multipliers (ADMM),bandwise generalized bilinear model (BGBM),hyperspectral images (HSIs),additive white Gaussian noise (AWGN),mixed noise
更新于2025-09-09 09:28:46