Spectral Fusion(光谱融合)研究综述
Spectral Fusion 光谱融合 - The spectral fusion of laser-induced breakdown spectroscopy and mid-infrared spectroscopy data was coupled with a random forest technique for the quantitative for the quantitative analysis of soil pH. [1] We compare our DL results to our previous work with normalized difference vegetation index (NDVI) and IR region-based spectral fusion, and to traditional machine learning approaches. [2] Then, to take full advantage of all 21 available OLCI bands of the Sentinel-3 images, the extended image pair-based spatio-spectral fusion (EIPSSF) method is proposed in this paper to downscale the other 17 bands. [3] Hyperspectral fusion is more complex than traditional panchromatic and multispectral fusion. [4] We present a novel encryption method for multiple images in a discrete multiple-parameter fractional Fourier transform scheme, using complex encoding, theta modulation and spectral fusion. [5] Pansharpening refers to a spatio-spectral fusion of a lower spatial resolution multispectral (MS) image with a high spatial resolution panchromatic image, aiming at obtaining an image with a corresponding high resolution both in the domains. [6] We formulate multi-spectral fusion and denoising for the luminance channel as a maximum a posteriori estimation problem in the wavelet domain. [7] The experiments show that this method can make the fused image acquire high color fidelity and sharpness, it is robust to different sensors and features, and it can be applied to the panchromatic and multi-spectral fusion of high-resolution optical satellites. [8] This study proposes a novel deep residual network of spatial and spectral Fusion to merge the two available images for hyperspectral image super-resolution. [9] In this Note, spectral fusion is proposed as a concept to overcome this problem. [10] The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithm is utilized to diagnose thyroid dysfunction serum, and finds the spectrum with the highest sensitivity to further advance diagnosis speed. [11] Therefore, in this paper, the method of spatial–spectral fusion based on conditional random fields (SSF-CRF) for the fine classification of crops in UAV-borne hyperspectral remote sensing imagery is presented. [12] The proposed method is tested on two different problems: hyperspectral fusion/sharpening and fusion of blurred-noisy image pairs. [13] To solve this problem, the paper propose multi-band remote sensing image change detection method by spectral fusion and Markov Random Field. [14] We employ gradient domain guided image filtering (GGIF) to enforce effective spatial fusion of panchromatic and multispectral images, and the proposed scheme shows better spatial and spectral fusion than other fusion methods, such as projection substitution, detail injection and weighted combination models. [15] Final experiments are provided to show the satisfactory performance of the proposed method in spatial and spectral fusion, and the proposed method outperforms several pan-sharpening methods in both subjective results and objective assessments. [16] These two paths are further fused together using spatial-spectral fusion to give multiscaled RS image which is further given to a pretrained network for feature extraction. [17] In the field of spatial–spectral fusion, the variational model-based methods and the deep learning (DL)-based methods are state-of-the-art approaches. [18]激光诱导击穿光谱和中红外光谱数据的光谱融合与随机森林技术相结合，用于定量分析土壤 pH 值。 [1] 我们将我们的 DL 结果与我们之前使用归一化差异植被指数 (NDVI) 和基于 IR 区域的光谱融合的工作以及传统的机器学习方法进行比较。 [2] 然后，为了充分利用 Sentinel-3 图像的所有 21 个可用 OLCI 波段，本文提出了基于扩展图像对的空间光谱融合 (EIPSSF) 方法来缩小其他 17 个波段。 [3] 高光谱融合比传统的全色和多光谱融合更复杂。 [4] 我们提出了一种在离散多参数分数傅里叶变换方案中使用复编码、θ 调制和光谱融合的多幅图像加密方法。 [5] 全色锐化是指将较低空间分辨率的多光谱（MS）图像与高空间分辨率的全色图像进行空间光谱融合，旨在获得在两个域中都具有相应高分辨率的图像。 [6] 我们将亮度通道的多光谱融合和去噪公式化为小波域中的最大后验估计问题。 [7] 实验表明，该方法可以使融合图像获得较高的色彩保真度和清晰度，对不同的传感器和特征具有鲁棒性，可应用于高分辨率光学卫星的全色和多光谱融合。 [8] 本研究提出了一种新颖的空间和光谱融合深度残差网络，用于合并两个可用图像以实现高光谱图像超分辨率。 [9] 在本说明中，提出了光谱融合作为克服此问题的概念。 [10] 利用拉曼光谱和傅里叶红外光谱的光谱融合结合模式识别算法对甲状腺功能障碍血清进行诊断，找到灵敏度最高的光谱，进一步提高诊断速度。 [11] 因此，本文提出了一种基于条件随机场的空间-光谱融合方法（SSF-CRF），用于无人机载高光谱遥感影像农作物精细分类。 [12] 所提出的方法在两个不同的问题上进行了测试：高光谱融合/锐化和模糊-噪声图像对的融合。 [13] 针对这一问题，本文提出了光谱融合和马尔可夫随机场的多波段遥感影像变化检测方法。 [14] 我们采用梯度域引导图像滤波（GGIF）来执行全色和多光谱图像的有效空间融合，并且所提出的方案显示出比投影替换、细节注入和加权组合模型等其他融合方法更好的空间和光谱融合。 [15] 最后的实验证明了所提出的方法在空间和光谱融合方面的令人满意的性能，并且所提出的方法在主观结果和客观评估方面都优于几种全色锐化方法。 [16] 使用空间光谱融合将这两条路径进一步融合在一起，以提供多尺度 RS 图像，该图像进一步提供给预训练网络以进行特征提取。 [17] 在空间光谱融合领域，基于变分模型的方法和基于深度学习（DL）的方法是最先进的方法。 [18]
spectral fusion method 光谱融合法
To further improve the detection performance, we analyzed the existing multispectral fusion methods and proposed a novel multispectral channel feature fusion (MCFF) module for integrating the features from the color and thermal streams according to the illumination conditions. [1] Based on this, this paper proposes a heterogeneous spatio-temporal-spectral fusion method based on deep learning. [2] The proposed method is based on a physical degradation model, and combines polarization recovery and spectral fusion methods. [3]为了进一步提高检测性能，我们分析了现有的多光谱融合方法，并提出了一种新颖的多光谱通道特征融合（MCFF）模块，用于根据光照条件整合来自颜色和热流的特征。 [1] 基于此，本文提出了一种基于深度学习的异构时空谱融合方法。 [2] 所提出的方法基于物理退化模型，并结合了偏振恢复和光谱融合方法。 [3]
spectral fusion cnn
Experiment results on benchmark datasets validate that the proposed multi-level and multi-scale spatial and spectral fusion CNNs outperforms the state-of-the-art methods in both quantitative values and visual qualities. [1] This chapter provides a comprehensive description of not only the conventional optimization-based methods but also the recently investigated DCNN-based learning methods for HS image super-resolution, which mainly include spectral reconstruction CNN and spatial and spectral fusion CNN. [2]基准数据集的实验结果验证了所提出的多层次和多尺度空间和光谱融合 CNN 在数量值和视觉质量方面都优于最先进的方法。 [1] 本章不仅全面介绍了传统的基于优化的方法，还全面介绍了最近研究的基于 DCNN 的 HS 图像超分辨率学习方法，主要包括光谱重建 CNN 和空间和光谱融合 CNN。 [2]
spectral fusion approach
However, the traditional spatial–spectral fusion approach is to use data in the same swath width that covers the same area and only considers the mutually constrained conditions between the spectral resolution and spatial resolution. [1] The main objectives of the study are developing of spatial-temporal-spectral fusion approach for multi-source data collected from the same geographical site; creating a new method for single image reconstruction from non-complementary information scene. [2]然而，传统的空间-光谱融合方法是使用覆盖相同区域的相同条带宽度的数据，只考虑光谱分辨率和空间分辨率之间的相互约束条件。 [1] 该研究的主要目标是开发从同一地理位置采集的多源数据的时空光谱融合方法；从非互补信息场景中创建单图像重建的新方法。 [2]