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Variational PCA fusion for pan-sharpening very high resolution imagery

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Science China(Information Sciences) 2014年第11期57卷 100-109页

作者： ZHOU ZeMing MA Ning LI YuanXiang YANG PingL ZHANG Peng LI YunYing Institute of Meteorology and Oceanography PLA University of Science and Technology School of Aeronautics Shanghai JiaoTong University

The pan-sharpening approach based on principle component analysis(PCA) is affected by severe spectral distortion. To address this problem, a new pan-sharpening model based on PCA and variational technique is proposed to construct the substitute image of the first principal component(PC1). The energy functional consists of three terms. The first term injects PC1 with the geometric structure of the panchromatic(pan) image. The second term preserves the spectral pattern of the multi-spectral image in the merged result.And the third term guarantees the smoothness of the functional optimization solution. The fusion result is given by the minimum of the energy functional, which is computed with the gradient descend flow. The experiments on QuickBird and IKONOS datasets validate the effectiveness of the proposed model. Compared with the stateof-the-art pan-sharpening approaches, this model exhibits a better trade-off between improving spatial quality and preserving spectral signature of the MS image.

关键词： PCA variational PCA fusion remote sensing image pan-sharpening gradient descend flow

A pan-sharpening method based on the ADMM algorithm

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Frontiers of Earth Science 2019年第3期13卷 656-667页

作者： Yingxia CHEN Tingting WANG Faming FANG Guixu ZHANG Department of Computer Science East China Normal UniversityShanghai 200062China School of Computer Science Yangtze UniversityJingzhou 434023China

pan-sharpening is a method of integrating low-resolution multispectral images with corresponding high-resolution panchromatic images to obtain multispectral images with high spectral and spatial resolution. A novel variational model for pan-sharpening is proposed in this paper. The model is mainly based on three hypotheses: 1) the pan-shaipened image can be linearly represented by the corresponding panchromatic image;2) the low-resolution multispectral image is down-sampled from the high-resolution multispectral image through the down-sampling operator;and 3) the satellite image has the low-rank property. Three energy components corresponding to these assumptions are integrated into a variational framework to obtain a total energy function. We adopt the alternating direction method of multipliers (ADMM) to optimize the total energy function. The experimental results show that the proposed method performs better than other mainstream methods in spectral and spatial information preserving aspect.

关键词： pan-sharpening multispectral image panchromatic image variational framework energy function ADMM

pan-sharpening:a fast variational fusion approach

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Science China(Information Sciences) 2012年第3期55卷 615-625页

作者： ZHOU ZeMing1, LI YuanXiang2 , SHI HanQing1, MA Ning1 & SHEN Ji2 1Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China 2School of Aeronautics and Astronautics, Shanghai JiaoTong University, Shanghai 200240, China Institute of Meteorology PLA University of Science and Technology Nanjing China School of Aeronautics and Astronautics Shanghai JiaoTong University Shanghai China

A fast variational fusion model based on partial differential equations (PDEs) is presented for pansharpening. The functional framework consists of several energy terms. The gradient energy term is created by calculating the gradient vector field of the panchromatic image and the geometry of the pan is injected into the multi-spectral bands. The radiometric reduction energy term and the channel correlation energy term are defined to decrease the radiometric distortion and preserve the correlation of multi-spectral channels while enforcing the inter-bands coherence. Inspired by the shock-filtering model, an inverse diffusion term for image enhancement is put to PDEs which are deduced by minimizing the energy functional. In comparison with the state-of-the-art fusion approaches based on `a trous wavelet and non-sampled contourlet, our model can obtain the fused image with a high spatial or spectral quality by adjusting the weight coefficients of the energy terms. It can also achieve a rather good trade-off between the spatial resolution improvement and the spectral quality preserving. Our model's computational complexity for one time step is only O(N).

关键词： remote sensing image pan-sharpening energy functional fast variational fusion

基于深度金字塔网络的pan-sharpening算法

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计算机辅助设计与图形学学报 2019年第10期31卷 1831-1837页

作者：方帅方赛华姚宏亮合肥工业大学计算机与信息学院合肥230009 工业安全与应急技术安徽省重点实验室合肥230009

为了利用高空间分辨率单波段的全色(pan)图像和低空间分辨率的多光谱图像(MS)生成高分辨率的多光谱图像,提出一种基于深度金字塔网络的遥感图像融合(即pan-sharpening)算法,通过图像金字塔的方式逐层上采样来重构高分辨率的多光谱图像.... 详细信息

为了利用高空间分辨率单波段的全色(pan)图像和低空间分辨率的多光谱图像(MS)生成高分辨率的多光谱图像,提出一种基于深度金字塔网络的遥感图像融合(即pan-sharpening)算法,通过图像金字塔的方式逐层上采样来重构高分辨率的多光谱图像.在细节保持方面,针对全色图像和多光谱图像在尺度上跨度过大的问题,采用深度金字塔网络多尺度地融合全色图像的细节信息;在光谱保持方面,使用反卷积层代替传统的超分辨算法来上采样低分率的多光谱图像;最后将这2部分相加,得到最终的融合图像. GeoEye-1数据集上的实验结果表明,文中算法综合性能优于BDSD, PRACS, PNN and panNet算法.

关键词：卷积神经网络图像金字塔 pan-sharpening 超分辨率

Joint AIHS and Particle Swarm Optimization for pan-sharpening

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Journal of Geodesy and Geoinformation Science 2020年第2期3卷 105-113页

作者： Yingxia CHEN Yan CHEN Cong LIU Department of Computer Science East China Normal UniversityShanghai 200062China School of Computer Science Yangtze UniversityJingzhou 434023China School of Computer Science University of Shanghai for Science and TechnologyShanghai 200082China

pan-sharpening is a process of obtaining a high spatial and spectral multispectral image(HMS)by combining a low-resolution multispectral image(LMS)with a high-resolution panchromatic image(pan).In this paper,a pan-sharpening method called PAIHS is proposed,which is based on adaptive intensity-hue-saturation(AIHS)transformation,variational pan-sharpening framework and the two fidelity hypotheses.The suitable objective function is established and optimized by adopting particle swarm optimization(PSO)to obtain the optimal control parameters and minimum value.This value corresponds to the best pan-sharpening quality.The experimental results show that the proposed method has high efficiency and reliability,and the obtained performance index is superior to the four mainstream pan-sharpening methods.

关键词： pan-sharpening multispectral image panchromatic image AIHS transformation particle swarm optimization objective function

A Comprehensive Evaluation of pan-sharpening Algorithms Coupled with Resampling Methods for Image Synthesis of Very High Resolution Remotely Sensed Satellite Data

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Advances in Remote Sensing 2013年第4期2卷 332-344页

作者： Shridhar D. Jawak Alvarinho J. Luis Polar Remote Sensing Department Earth System Sciences Organization (ESSO) National Centre for Antarctic and Ocean Research (NCAOR) Ministry of Earth Sciences Government of India Headland Sada Gao India

The merging of a panchromatic (pan) image with a multispectral satellite image (MSI) to increase the spatial resolution of the MSI, while simultaneously preserving its spectral information is classically referred as pan-sharpening. We employed a recent dataset derived from very high resolution of WorldView-2 satellite (pan and MSI) for two test sites (one over an urban area and the other over Antarctica), to comprehensively evaluate the performance of six existing pan-sharpening algorithms. The algorithms under consideration were the Gram-Schmidt (GS), Ehlers fusion (EF), modified hue-intensity-saturation (Mod-HIS), high pass filtering (HPF), the Brovey transform (BT), and wavelet-based principal component analysis (W-PC). Quality assessment of the sharpened images was carried out by using 20 quality indices. We also analyzed the performance of nearest neighbour (NN), bilinear interpolation (BI), and cubic convolution (CC) resampling methods to test their practicability in the pan-sharpening process. Our results indicate that the comprehensive performance of pan-sharpening methods decreased in the following order: GS > W-PC > EF > HPF > Mod-HIS > BT, while resampling methods followed the order: NN > BI > CC.

关键词： pan-sharpening WorldView-2 Resampling Methods

一种新的基于压缩传感理论解决pan-sharpening问题的算法

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计算机应用与软件 2014年第9期31卷 194-197页

作者：简智涵曹淼科沈超敏张桂戌华东师范大学信息科学技术学院上海200241

pan-sharpening问题是将高分辨率全色图像和低分辨率多光谱图像融合,重构出高分辨率多光谱图像的过程。压缩传感理论解决了相应目标函数的稀疏解问题(求解含有l0范数的极小化问题)。利用压缩传感理论,提出一种新的解决pan-sharpening问... 详细信息

pan-sharpening问题是将高分辨率全色图像和低分辨率多光谱图像融合,重构出高分辨率多光谱图像的过程。压缩传感理论解决了相应目标函数的稀疏解问题(求解含有l0范数的极小化问题)。利用压缩传感理论,提出一种新的解决pan-sharpening问题的算法:以离散余弦变换矩阵作为字典进行重构。然后针对重构过程中产生的块效应、色彩偏差和洗涤效应等问题进行相应的处理。QuickBird卫星图像的实验结果表明:与经典算法进行对比,该算法在效果上有一定的优势。

关键词： pan-sharpening 数字图像处理离散余弦变换压缩传感

基于领域知识驱动的深度学习pan-sharpening方法

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基于领域知识驱动的深度学习Pan-sharpening方法

作者：胡宇雯厦门大学

学位级别：硕士

pan-sharpening是将同一场景的全色(pan)图像和多光谱(MS)图像相互融合以有效提高MS图像的空间分辨率。pan-sharpening在无需升级硬件的情况下有效克服了单一传感器的物理局限性,因而成为遥感领域的研究热点,并已广泛应用于自然资源探... 详细信息

pan-sharpening是将同一场景的全色(pan)图像和多光谱(MS)图像相互融合以有效提高MS图像的空间分辨率。pan-sharpening在无需升级硬件的情况下有效克服了单一传感器的物理局限性,因而成为遥感领域的研究热点,并已广泛应用于自然资源探测、环境监测、军事侦察、和灾害监控等领域。pan-sharpening的主要目标是在光谱基本不失真的前提下尽可能提高MS图像的空间分辨率,其中MS图像具有较高的谱分辨率但是空间分辨率较低,pan图像则具有很高的空间分辨率但缺少光谱分辨能力。由于是对同一场景成像,因而MS图像与pan图像具有相似的图像结构,但是两者成像频段不一致,导致其数值上也存在较大的差异。基于上述领域知识,本文提出了两种遥感图像pan-sharpening方法,主要研究内容与成果如下:1、提出一种基于空间结构一致性和l1/2梯度先验的遥感图像pan-sharpening方法。该方法主要创新点有:1)利用l1范数约束pan图像和高分辨率多光谱(HRMS)图像的梯度图像的差值实现空间结构一致性,以便于从pan图像中获得空间结构信息。梯度信息的引入使得目标函数一方面可以有效利用pan图像与HRMS图像的结构相似性,另一方面又减少了两者数值差异的影响。l1范数的稀疏特性则进一步强化结构相似性先验。2)通过大量统计实验发现,HRMS图像的梯度服从超拉普拉斯分布,利用l1/2范数对其进行刻画相比常用的l1或l2范数更准确。同时,论文利用l1-αl2范数逼近l1/2范数,以便于更有效求解上述变分模型。2、提出一种基于领域知识驱动的深度学习pan-sharpening方法:Deep pan-sharpening。与一般的深度学习pan-sharpening方法直接求解输入图像到输出图像的映射不同,本文所提出的端到端的深度网络充分考虑了 pan-sharpening的领域知识,也即在谱基本不失真的前提下尽量提高MS图像的空间分辨率,具体表现为:1)将上采样的低分辨率多光谱(LRMS)图像以shortcut形式直接添加到网络输出端,从而使得网络映射过程中谱信息尽量不丢失。2)利用残差网络学习由pan图像和LRMS图像的高频结构信息到HRMS图像的高频结构信息的映射,由于是图像高频结构到高频结构的映射,一方面最大可能提高LRMS图像的空间分辨率,另一方面也减少了图像数值差异所带来的谱失真。3、与多种pan-sharpening方法的仿真和真实数据对比实验表明,本文提出两种方法在主客观上都有一定的优势。其中Deep pan-sharpening方法还克服了变分方法中参数设置较多的不足,且具有更强的鲁棒性。

关键词： pan-sharpening 变分模型深度学习

基于卷积神经网络的pan-sharpening方法

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基于卷积神经网络的Pan-sharpening方法

作者：杨骏锋厦门大学

学位级别：硕士

pan-sharpening是多光谱遥感图像处理任务中将原始的多光谱(MS)图像和全色(pan)图像进行融合获得高分辨率多光谱图像的一种方法。论文围绕pan-sharpening的两个主要目标:光谱信息尽可能不损失、空间分辨率尽可能提高,构建一个端到端的... 详细信息

pan-sharpening是多光谱遥感图像处理任务中将原始的多光谱(MS)图像和全色(pan)图像进行融合获得高分辨率多光谱图像的一种方法。论文围绕pan-sharpening的两个主要目标:光谱信息尽可能不损失、空间分辨率尽可能提高,构建一个端到端的深度卷积网络pan-sharpening模型:panNet。panNet模型由谱保持和空间分辨率提高两个分支所构成。为使光谱信息尽可能不损失,panNet模型的谱保持分支首先利用插值方法将原始低分辨率多光谱(MS)图像放大为与高分辨率多光谱图像一致,然后将插值后的多光谱图像直接传输到网络输出中,从而将光谱信息直接传输到重建的高分辨率多光谱图像。为了尽可能提高多光谱图像的空间分辨率,panNet模型的空间分辨率提高分支选择以低分辨率多光谱图像(MS)和全色图像(pan)的高频作为输入,输出则是由理想高分辨率多光谱图像与低分辨率多光谱图像的差值构成,并通过残差网络(ResNet)训练学习上述输入到输出间的映射。相比于一般的深度学习模型直接训练学习低分辨率多光谱图像到高分辨率多光谱图像的映射,panNet模型这种高频到高频的映射学习,使得模型可以关注空间分辨率的提高,而不以谱分辨率信息损失为代价。此外,这种高频到高频的映射学习也有助于简化深度学习模型的学习过程。为了进一步提高panNet模型的性能,论文引入了特征重标定的概念,通过在空间以及特征维度两个方向对panNet模型的各卷积层学习获得的特征图进行重新标定,使得不同通道的特征图或同一特征图的不同空间位置具有不同的关注权重,从而激励网络学习到更有意义的特征映射。相关模拟和真实数据对比实验发现,论文所提的panNet模型及其特征重标定改进模型在各类谱保持和空间分辨率提高的主客观评价指标方面均明显优于传统的基于优化以及已有的基于深度学习的模型,取得了当前最优性能。另外,所提模型具有较强的泛化性能,无需模型参数调整,便能够适用于来自不同卫星的多光谱遥感图像。

关键词： pan-sharpening 深度学习特征重标定