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1.
在遥感图像全色锐化中,传统的成分替换(CS)和多分辨率分析(MRA)方法的线性注入模型没有考虑用于全色锐化传感器的相对光谱响应,而基于深度学习的方法对原图像特征的提取不足会导致融合结果中的光谱和空间信息的丢失。针对以上问题,提出一种结合传统与深度学习方法的全色锐化方法 CMRNet。首先,将CS和MRA与卷积神经网络(CNN)相结合以实现非线性从而提高全色锐化方法性能;其次,设计残差通道(RC)块实现多尺度特征信息的融合提取,并利用通道注意力(CA)自适应地为不同通道的特征图分配不同的权值,从而学习更有效的信息。在QuickBird和GF1卫星数据集上对CMRNet进行训练和测试,实验结果表明,在降尺度QuickBird和GF1数据集上,与经典方法 PanNet相比,CMRNet的峰值信噪比(PSNR)分别提高了5.48%和9.62%,其他指标也均有显著提高。可见,CMRNet能实现较好的全色锐化效果。 相似文献
2.
全色锐化旨在通过一个高分辨率的单通道全色图像(Panchromatic, PAN)锐化一个低分辨率的多通道多光谱图像(Multispectral, MS),得到一个高分辨率的多通道多光谱图像(High Resolution Multispectral, HRMS),这是遥感图像处理中的重要任务。文中提出了一个基于感知损失的反馈网络,首先对PAN图像和MS图像分别提取细节信息和光谱信息,然后将其合并后利用堆叠的上下采样层和密集连接进行信息融合,利用反馈连接使高层次的信息丰富低层次的信息,最后重建HRMS图像。与传统全色锐化算法相比,所提算法将PAN图像和HRMS图像一起作为网络输出的监督,通过求取PAN图像和网络重建HRMS图像的感知损失使输出图像含有更丰富的空间细节信息。无论是在客观指标还是视觉感受方面,与现有广泛使用的算法相比,所提算法都有更好的效果。 相似文献
3.
为验证神经网络方法用于遥感图像融合的有效性,归纳了利用神经网络对遥感数据进行回归来实现融合的3种途径,并提出了一种结合图像数据回归和多光谱遥感图像锐化技术来实现热红外图像的全色锐化新方法。这种热红外图像的全色锐化方法,利用了极限学习机(ELM)这种新型神经网络算法,快速高效地由训练样本得到遥感图像数据间的回归关系;同时,方法注重图像数据本身的物理含义,以提高热红外图像数据的真实质量为目标,是一种定量化的图像融合方法。经这种方法融合得到的热红外数据也能很好地用于定量遥感的物理模型,为遥感的实际应用提供方便。该方法的有效性通过对ETM+图像进行实验得到了证明,而直接对热红外图像数据和全色图像数据进行回归的融合模式,在实验中则无法得到满意的结果。 相似文献
4.
为解决基于遥感图像监测地表水资源变化的问题,在深度学习的框架下,基于卷积神经网络(CNN)提出了用于遥感图像水体提取的模型.利用网络爬虫的方式,搜集遥感图像,并通过随机裁剪、数据清洗等方式构建训练、验证和测试数据集.通过对低层语义特征学习提取抽象的高层特征,基于提取的高层特征进行网络模型训练.实验结果表明:水体提取的精... 相似文献
5.
目的 遥感图像飞机目标分类,利用可见光遥感图像对飞机类型进行有效区分,对提供军事作战信息有重要意义。针对该问题,目前存在一些传统机器学习方法,但这些方法需人工提取特征,且难以适应真实遥感图像的复杂背景。近年来,深度卷积神经网络方法兴起,网络能自动学习图像特征且泛化能力强,在计算机视觉各领域应用广泛。但深度卷积神经网络在遥感图像飞机分类问题上应用少见。本文旨在将深度卷积神经网络应用于遥感图像飞机目标分类问题。 方法 在缺乏公开数据集的情况下,收集了真实可见光遥感图像中的8种飞机数据,按大致4∶1的比例分为训练集和测试集,并对训练集进行合理扩充。然后针对遥感图像与飞机分类的特殊性,结合深度学习卷积神经网络相关理论,有的放矢地设计了一个5层卷积神经网络。 结果 首先,在逐步扩充的训练集上分别训练该卷积神经网络,并分别用同一测试集进行测试,实验表明训练集扩充有利于网络训练,测试准确率从72.4%提升至97.2%。在扩充后训练集上,分别对经典传统机器学习方法、经典卷积神经网络LeNet-5和本文设计的卷积神经网络进行训练,并在同一测试集上测试,实验表明该卷积神经网络的分类准确率高于其他两种方法,最终能在测试集上达到97.2%的准确率,其余两者准确率分别为82.3%、88.7%。 结论 在少见使用深度卷积神经网络的遥感图像飞机目标分类问题上,本文设计了一个5层卷积神经网络加以应用。实验结果表明,该网络能适应图像场景,自动学习特征,分类效果良好。 相似文献
6.
遥感图像场景分类对土地资源管理具有重要意义,然而高分辨率遥感图像中地物分布复杂,图像中存在着与当前场景无关的冗余信息,会对场景的精确分类造成影响.对此,提出一种基于脉冲卷积神经网络(SCNN)稀疏表征的场景分类方法.从稀疏表征出发,利用脉冲神经元的稀疏脉冲输出特性,设计脉冲卷积神经网络,去除遥感图像中与场景无关的冗余信息,实现对图像的稀疏表征;提出基于脉冲输出交叉熵损失函数的反向传播算法,在该算法的基础上利用梯度下降训练脉冲卷积神经网络,优化网络参数,实现遥感图像场景分类;通过实验验证方法的有效性,将所提出方法应用于Google和UCM两个遥感图像数据集,并与传统的卷积神经网络(CNN)进行对比.实验结果表明,所提出方法可以对遥感图像进行稀疏表征,实现场景分类;相对于卷积神经网络,所提出方法在遥感图像场景分类任务上更具有优势. 相似文献
7.
针对前馈卷积神经网络(CNN)感受野较小、获取上下文信息不足、其特征提取卷积层只能提取到浅层特征的问题,提出改进的基于通道注意力反馈网络的遥感图像融合算法.首先,通过两层卷积层分别初步提取全色(PAN)图像的细节特征和低分辨率多光谱(LMS)图像的光谱特征;其次,将提取的特征和网络反馈的深层特征相结合,并将其输入到通道... 相似文献
8.
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. 相似文献
9.
针对卷积神经网络提取特征信息不完整导致图像分类方法分类精度不高等问题,利用深度学习的方法搭建卷积神经网络模型框架,提出一种基于迭代训练和集成学习的图像分类方法。利用数据增强对图像数据集进行预处理操作,在提取图像特征时,采用一种迭代训练卷积神经网络的方式,得到充分有效的图像特征,在训练分类器时,采用机器学习中集成学习的思想。分别在特征提取后训练分类器,根据各分类器贡献的大小,赋予它们不同的权重值,取得比单个分类器更好的性能,提高图像分类的精度。该方法在Stanford Dogs、UEC FOOD-100和CIFAR-100数据集上的实验结果表明了其较好的分类性能。 相似文献
10.
机器学习的JavaScript恶意代码检测方法在提取特征过程中耗费时间和人力,以及这些频繁使用的机器学习方法已经无法满足当今信息大爆炸的实际需要。提出了一种基于卷积神经网络的JavaScript恶意代码检测方法。采用爬虫工具收集良性和恶意的JavaScript脚本代码获得样本数据;将JavaScript样本转换为相对应的灰阶图像,得到图像数据集;通过构建卷积神经网络模型对图像数据集进行训练,使得模型具有检测JavaScript恶意代码的能力。实验结果表明,相对于机器学习,该方法对收集到的5 800条JavaScript代码样本,检测准确率达到98.9%。 相似文献
11.
Pan-sharpening将高分辨率图像全色(Panchromatic, Pan)波段的空间细节注入多光谱(Multispectral, MS)波段, 以生成同时具有高光谱和高空间分辨率的多光谱图像. 为改善融合效果, 需要考虑多光谱和全色波段的调制传输函数(Modulation transfer function, MTF). 本文提出了一个新的基于MTF和变分的Pan-sharpening模型. 该模型的能量泛函包括两项, 第1项为细节注入项, 基于高通滤波器从Pan波段中提取细节信息并注入融合图像;第2项为光谱保真项, 基于MTF设计多孔小波的低通滤波器以保持MS波段的多光谱信息. 在QuickBird、IKONOS和GeoEye数据集上的融合结果表明, 该模型可以生成同时具有高空间和高光谱质量的融合图像, 融合效果优于AWLP、IHS_BT、HPM-CC-PSF、NAWL、快速变分等算法. 相似文献
12.
提出一种基于卷积神经网络的Transformer模型来解决全景分割任务,方法借鉴CNN在图像特征学习方面的先天优势,避免了Transformer被移植到视觉任务中所导致的计算量增加.基于卷积神经网络的Transformer模型由执行特征域变换的映射器和负责特征提取的提取器这两种基本结构构成,映射器和提取器的有效结合构成了该模型的网络框架.映射器由一种Lattice卷积模型实现,通过对卷积滤波器进行设计和优化来模拟图像的空间关系.提取器由链式网络实现,通过链式单元堆叠提高特征提取能力.基于全景分割的结构和功能,构建了基于CNN的全景分割Transformer网络.在MS COCO和Cityscapes数据集的实验结果表明,所提方法具有优异的性能. 相似文献
13.
目的 遥感图像融合是将一幅高空间分辨率的全色图像和对应场景的低空间分辨率的多光谱图像,融合成一幅在光谱和空间两方面都具有高分辨率的多光谱图像。为了使融合结果在保持较高空间分辨率的同时减轻光谱失真现象,提出了自适应的权重注入机制,并针对上采样图像降质使先验信息变得不精确的问题,提出了通道梯度约束和光谱关系校正约束。 方法 使用变分法处理遥感图像融合问题。考虑传感器的物理特性,使用自适应的权重注入机制向多光谱图像各波段注入不同的空间信息,以处理多光谱图像波段间的差异,避免向多光谱图像中注入过多的空间信息导致光谱失真。考虑到上采样的图像是降质的,采用局部光谱一致性约束和通道梯度约束作为先验信息的约束,基于图像退化模型,使用光谱关系校正约束更精确地保持融合结果的波段间关系。 结果 在Geoeye和Pleiades卫星数据上同6种表现优异的算法进行对比实验,本文提出的模型在2个卫星数据上除了相关系数CC(correlation coefficient)和光谱角映射SAM(spectral angle mapper)评价指标表现不够稳定,偶尔为次优值外,在相对全局误差ERGAS(erreur relative globale adimensionnelle de synthèse)、峰值信噪比PSNR(peak signal-to-noise ratio)、相对平均光谱误差RASE(relative average spectral error)、均方根误差RMSE(root mean squared error)、光谱信息散度SID(spectral information divergence)等评价指标上均为最优值。 结论 本文模型与对比算法相比,在空间分辨率提升和光谱保持方面都取得了良好效果。 相似文献
14.
The Convolutional Neural Networks (CNNs) based multi-focus image fusion methods have recently attracted enormous attention. They greatly enhanced the constructed decision map compared with the previous state of the art methods that have been done in the spatial and transform domains. Nevertheless, these methods have not reached to the satisfactory initial decision map, and they need to undergo vast post-processing algorithms to achieve a satisfactory decision map. In this paper, a novel CNNs based method with the help of the ensemble learning is proposed. It is very reasonable to use various models and datasets rather than just one. The ensemble learning based methods intend to pursue increasing diversity among the models and datasets in order to decrease the problem of the overfitting on the training dataset. It is obvious that the results of an ensemble of CNNs are better than just one single CNNs. Also, the proposed method introduces a new simple type of multi-focus images dataset. It simply changes the arranging of the patches of the multi-focus datasets, which is very useful for obtaining the better accuracy. With this new type arrangement of datasets, the three different datasets including the original and the Gradient in directions of vertical and horizontal patches are generated from the COCO dataset. Therefore, the proposed method introduces a new network that three CNNs models which have been trained on three different created datasets to construct the initial segmented decision map. These ideas greatly improve the initial segmented decision map of the proposed method which is similar, or even better than, the other final decision map of CNNs based methods obtained after applying many post-processing algorithms. Many real multi-focus test images are used in our experiments, and the results are compared with quantitative and qualitative criteria. The obtained experimental results indicate that the proposed CNNs based network is more accurate and have the better decision map without post-processing algorithms than the other existing state of the art multi-focus fusion methods which used many post-processing algorithms. 相似文献
15.
图像融合技术旨在将不同源图像中的互补信息整合到单幅融合图像中以全面表征成像场景,并促进后续的视觉任务。随着深度学习的兴起,基于深度学习的图像融合算法如雨后春笋般涌现,特别是自编码器、生成对抗网络以及Transformer等技术的出现使图像融合性能产生了质的飞跃。本文对不同融合任务场景下的前沿深度融合算法进行全面论述和分析。首先,介绍图像融合的基本概念以及不同融合场景的定义。针对多模图像融合、数字摄影图像融合以及遥感影像融合等不同的融合场景,从网络架构和监督范式等角度全面阐述各类方法的基本思想,并讨论各类方法的特点。其次,总结各类算法的局限性,并给出进一步的改进方向。再次,简要介绍不同融合场景中常用的数据集,并给出各种评估指标的具体定义。对于每一种融合任务,从定性评估、定量评估和运行效率等多角度全面比较其中代表性算法的性能。本文提及的算法、数据集和评估指标已汇总至https://github.com/Linfeng-Tang/Image-Fusion。最后,给出了本文结论以及图像融合研究中存在的一些严峻挑战,并对未来可能的研究方向进行了展望。 相似文献
16.
Combining the spectral information of a low-resolution multispectral (LRMS) image and the spatial information of a high-resolution panchromatic (HRP) image to generate a high-resolution multispectral (HRMS) image has become an important and interesting issue. Local dissimilarities between the LRMS image and the HRP image affect the performance of the pan-sharpening technique. This paper presents a model-based pan-sharpening method with global and nonlocal spatial similarity regularisers to reduce the effects of the local dissimilarities. The degraded model relating the LRMS image to the unknown HRMS image is employed as the data-fitting term to keep spectral fidelity. Two spatial similarity constraints are utilized to further enhance the spatial resolution of the unknown HRMS image. The first regularisation term is under the assumption that the high-pass component of each HRMS band has the similar geometry structure with the adjusted high-pass component of the HRP image. A modulation matrix is constructed to reduce the contrast differences. Moreover, nonlocal self-similarity characteristic of the high-pass component extracted from each HRMS band is considered as another regulariser, which is an effective structural prior to improve the local spatial quality of the HRMS image. The weights of nonlocal similarity model are learned from the high-pass component of available HRP image. Experiments conducted on QuickBird and IKONOS data validate that the proposed pan-sharpening method can achieve better performance compared with several traditional and state-of-the-art pan-sharpening algorithms in terms of quantitative evaluation and visual analysis. 相似文献
17.
This paper proposes a novel deep learning-based approach for financial chart patterns classification. Convolutional neural networks (CNNs) have made notable achievements in image recognition and computer vision applications. These networks are usually based on two-dimensional convolutional neural networks (2D CNNs). In this paper, we describe the design and implementation of one-dimensional convolutional neural networks (1D CNNs) for the classification of chart patterns from financial time series. The proposed 1D CNN model is compared against support vector machine, extreme learning machine, long short-term memory, rule-based and dynamic time warping. Experimental results on synthetic datasets reveal that the accuracy of 1D CNN is highest among all the methods evaluated. Results on real datasets also reveal that chart patterns identified by 1D CNN are also the most recognized instances when they are compared to those classified by other methods. 相似文献
18.
Improving the quality of pan-sharpened multispectral (MS) bands is the main aim of the recent research on pan-sharpening. In this article, we present a novel image fusion method based on combining the curvelet transform and independent component analysis (ICA). The idea is to map the MS bands onto a statistically independent domain to determine the intensity component, which contains the common information of the MS bands, and then to pan-sharpen it using curvelets and a modified adaptive fusion rule. The proposed method is evaluated by visual and statistical analyses and compared with the curvelet (CVT)-based method using a context-based decision model, the CVT-based method using the Dempster–Shafer evidence theory, the improved ICA method, and the combined adaptive principle component analysis (PCA)–Contourlet method. The experimental results using QuickBird and WorldView-2 data show that the proposed method effectively reduces the spectral distortion while injecting spatial details into the fused bands as much as possible. 相似文献
19.
为生成兼具高光谱质量与高空间质量的融合图像,本文提出了一种新的Pan-sharpening变分融合模型.通过拟合退化后的全色(Panchromatic,Pan)波段图像与低分辨率多光谱(Multispectral,MS)波段图像间的线性关系得到各波段MS图像的权重系数,计算从Pan图像抽取的空间细节;基于全色波段图像的梯度定义加权函数,增强了图像的强梯度边缘并对因噪声而引入的虚假边缘进行了抑制,有效地保持了全色波段图像中目标的几何结构;基于MS波段传感器的调制传输函数定义低通滤波器,自适应地限制注入空间细节的数量,显著降低了融合MS图像的光谱失真;针对Pan-sharpening模型的不适定性问题,引入L1正则化能量项,保证了数值解的稳定性.采用Split Bregman数值方法求解能量泛函的最优解,提高了算法的计算效率.QuickBird、IKONOS和GeoEye-1数据集上的实验结果表明,模型的综合融合性能优于MTF-CON、AWLP、SparseFI、TVR和MTF-Variational等算法. 相似文献
20.
The tradeoff between efficiency and model size of the convolutional neural network (CNN) is an essential issue for applications of CNN-based algorithms to diverse real-world tasks. Although deep learning-based methods have achieved significant improvements in image super-resolution (SR), current CNN-based techniques mainly contain massive parameters and a high computational complexity, limiting their practical applications. In this paper, we present a fast and lightweight framework, named weighted multi-scale residual network (WMRN), for a better tradeoff between SR performance and computational efficiency. With the modified residual structure, depthwise separable convolutions (DS Convs) are employed to improve convolutional operations’ efficiency. Furthermore, several weighted multi-scale residual blocks (WMRBs) are stacked to enhance the multi-scale representation capability. In the reconstruction subnetwork, a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image. Extensive experiments were conducted to evaluate the proposed model, and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN. 相似文献
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