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- 摘要
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- 实验方案
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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 Image Super-Resolution via Local Low-Rank and Sparse Representations
摘要： Remotely sensed hyperspectral images (HSIs) usually have high spectral resolution but low spatial resolution. A way to increase the spatial resolution of HSIs is to solve a fusion inverse problem, which fuses a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) of the same scene. In this paper, we propose a novel HSI super-resolution approach (called LRSR), which formulates the fusion problem as the estimation of a spectral dictionary from the LR-HSI and the respective regression coefficients from both images. The regression coefficients are estimated by formulating a variational regularization problem which promotes local (in the spatial sense) low-rank and sparse regression coefficients. The local regions, where the spectral vectors are low-rank, are estimated by segmenting the HR-MSI. The formulated convex optimization is solved with SALSA. Experiments provide evidence that LRSR is competitive with respect to the state-of-the-art methods.
关键词： Hyperspectral image super-resolution,low rank,superpixels
更新于2025-09-23 15:22:29
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[IEEE 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Amsterdam, Netherlands (2019.9.24-2019.9.26)] 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - A Pixel Level Scaled Fusion Model to Provide High Spatial-Spectral Resolution for Satellite Images Using LSTM Networks
摘要： Pixel-level fusion of satellite images coming from multiple sensors allows for an improvement in the quality of the acquired data both spatially and spectrally. In particular, multispectral and hyperspectral images have been fused to generate images with a high spatial and spectral resolution. In literature, there are several approaches for this task, nonetheless, those techniques still present a loss of relevant spatial information during the fusion process. This work presents a multi scale deep learning model to fuse multispectral and hyperspectral data, each with high-spatial-and-low-spectral resolution (HSaLS) and low-spatial-and-high-spectral resolution (LSaHS) respectively. As a result of the fusion scheme, a high-spatial-and-spectral resolution image (HSaHS) can be obtained. In order of accomplishing this result, we have developed a new scalable high spatial resolution process in which the model learns how to transition from low spatial resolution to an intermediate spatial resolution level and finally to the high spatial-spectral resolution image. This step-by-step process reduces significantly the loss of spatial information. The results of our approach show better performance in terms of both the structural similarity index and the signal to noise ratio.
关键词： hyperspectral image,Super resolution,Data Fusion,Long Short Term Memory,Pixel level,multispectral image
更新于2025-09-12 10:27:22
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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 - Local Similarity Regularized Sparse Representation for Hyperspectral Image Super-Resolution
摘要： Recently, performance of hyperspectral image super-resolution (SR) has been significantly improved via sparse representation. However, most of these existing methods fail to consider the local geometrical structure of the sparse coefficients. To take this crucial issue into account, this paper proposes an effective method, which exploits the location related constraint about the sparse coefficients and incorporates their local similarity into the sparse coding process. Thus, the proposed method can preserve the properties of the aforementioned local geometrical structures. Based on the experimental results, the proposed method is demonstrated to be more effective than previous efforts in the task of hyperspectral image SR.
关键词： Local similarity,Sparse representation,Hyperspectral image,Super-resolution
更新于2025-09-04 15:30:14