A Novel Unmixing-Based Hypersharpening Method via Convolutional Neural Network

Lu, Xiaochen; Li, Tong; Zhang, Junping; Jia, Fengde

doi:10.1109/tgrs.2021.3063105

Cited by 5 publications

(5 citation statements)

References 49 publications

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“…(1) Utilize the extrapolation results of ground-based cloud images for the next 5 min; these have been obtained from our previous work [20];…”

Section: Model Hyperparameter Tuningmentioning

confidence: 99%

Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather

Lu,

Chen,

Yan

et al. 2023

Sustainability

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Haze constitutes a pivotal meteorological variable with notable implications for photovoltaic power forecasting. The presence of haze is anticipated to lead to a reduction in the output power of photovoltaic plants. Therefore, achieving precise forecasts of photovoltaic power in hazy conditions holds paramount significance. This study introduces a novel approach to forecasting photovoltaic power under haze conditions, leveraging ground-based cloud images. Firstly, the aerosol scattering coefficient is introduced as a pivotal parameter for characterizing photovoltaic power fluctuations influenced by haze. Additionally, other features, such as sky cloud cover, color attributes, light intensity, and texture characteristics, are considered. Subsequently, the Spearman correlation coefficient is applied to calculate the correlation between feature sequences and photovoltaic power. Effective features are then selected as inputs and three models—LSTM, SVM, and XGBoost—are employed for training and performance analysis. After comparing with existing technologies, the predicted results have achieved the best performance. Finally, using actual data, the effectiveness of the aerosol scattering coefficient is confirmed, by exhibiting the highest correlation index, as a pivotal parameter for forecasting photovoltaic output under the influence of haze. The results demonstrate that the aerosol scattering coefficient enhances the forecast accuracy of photovoltaic power in both heavy and light haze conditions by 1.083% and 0.599%, respectively, while exerting minimal influence on clear days. Upon comprehensive evaluation, it is evident that the proposed forecasting method in this study offers substantial advantages for accurately predicting photovoltaic power output in hazy weather scenarios.

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“…(1) Utilize the extrapolation results of ground-based cloud images for the next 5 min; these have been obtained from our previous work [20];…”

Section: Model Hyperparameter Tuningmentioning

confidence: 99%

Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather

Lu,

Chen,

Yan

et al. 2023

Sustainability

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“…Suppose that the desired high-resolution HS image is denoted by Y ∈ R Λ×N , where Λ is the number of spectral bands, and N denotes the pixel number. According to the linear spectral mixture model [47], we have…”

Section: Observation Modelsmentioning

confidence: 99%

“…Unfortunately, only a few efforts have been made so far. Recently, we proposed an HS and MS image fusion method based on CNN and spectral unmixing models [47], which fully exploits the characteristics of high spectral and spatial resolution of HS and MS images, respectively, and aims to better estimate the abundances for HR HS images. The fusion method achieves state-of-the-art performance in terms of spectral fidelity.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, this method still needs MS images to provide auxiliary HR spatial information. To overcome this deficiency, inspired by the existing single HS SR methods [26,36] and the unmixingbased fusion method [47], in this paper, a CNN-based single image super-resolution approach is proposed, via the classical linear spectral mixture model, to spatially improve the resolution of HS images without auxiliary image sources. The proposed approach, named the unmixing CNN (UCNN) SR approach, assumes that the differences between high-and low-resolution HS images concentrate on the abundances of endmembers for the same scene, thereby employing a CNN to solve the HR abundance maps.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

Yang

Zhang

et al. 2021

Remote Sensing

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Super-resolution (SR) technology has emerged as an effective tool for image analysis and interpretation. However, single hyperspectral (HS) image SR remains challenging, due to the high spectral dimensionality and lack of available high-resolution information of auxiliary sources. To fully exploit the spectral and spatial characteristics, in this paper, a novel single HS image SR approach is proposed based on a spatial correlation-regularized unmixing convolutional neural network (CNN). The proposed approach takes advantage of a CNN to explore the collaborative spatial and spectral information of an HS image and infer the high-resolution abundance maps, thereby reconstructing the anticipated high-resolution HS image via the linear spectral mixture model. Moreover, a dual-branch architecture network and spatial spread transform function are employed to characterize the spatial correlation between the high- and low-resolution HS images, aiming at promoting the fidelity of the super-resolved image. Experiments on three public remote sensing HS images demonstrate the feasibility and superiority in terms of spectral fidelity, compared with some state-of-the-art HS image super-resolution methods.

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“…Spatial information offers valuable insights into object boundaries and the spatial distribution of neighboring pixels, improving the filtering of spatially inconsistent noise and artifacts, thereby enhancing unmixing accuracy and robustness. The convolutional neural networks (CNNs) are the most widely utilized method to catch spatial information from HSI to assist HSU [24], [29], [30]. By operating the convolution kernel on the HSI patches, the feature of the target pixel and its surrounding pixels are equally extracted to serve for target pixel unmixing, which ignores the correlation between the target pixel and surrounding pixels.…”

Section: Introductionmentioning

confidence: 99%

Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing

Wang,

Zhang,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spectral unmixing is a significant challenge in hyperspectral image processing. Existing unmixing methods utilize prior knowledge about the abundance distribution to solve the regularization optimization problem, where the difficulty lies in choosing appropriate prior knowledge and solving the complex regularization optimization problem. To solve these problems, we propose a hyperspectral conditional generative adversarial network (HyperGAN) method as a generic unmixing framework based on the following assumption: the unmixing process from pixel to abundance can be regarded as a transformation of two modalities with an internal specific relationship. The proposed HyperGAN is composed of a generator and a discriminator, the former completes the modal conversion from mixed hyperspectral pixel patch to the abundance of corresponding endmember of the central pixel and the latter is used to distinguish whether the distribution and structure of generated abundance are the same as the true ones. Additionally, we propose hyperspectral image (HSI) Patch Transformer as the main component of the generator, which utilizes adaptive attention score to capture the internal pixels correlation of the HSI patch and leverages the spatialspectral information in a fine-grained way to achieve optimization of the unmixing process. Furthermore, we propose a data synthesis method based on superpixel segmentation and random split to get synthetic data with a spatial structure to further evaluate the effectiveness of our model for unmixing. Experiments on synthetic data and real hyperspectral data achieve impressive results compared to state-of-the-art competitors.

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A Novel Unmixing-Based Hypersharpening Method via Convolutional Neural Network

Cited by 5 publications

References 49 publications

Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather

Photovoltaic Power Forecasting Approach Based on Ground-Based Cloud Images in Hazy Weather

Hyperspectral Image Super-Resolution Based on Spatial Correlation-Regularized Unmixing Convolutional Neural Network

Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing

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