基于NMF的多光谱图像和全色图像融合方法

提出了一种基于非负矩阵分解的多光谱图像与全色图像的融合算法。在非负矩阵分解过程中,将低分辨率的多光谱图像和高分辨率的全色图像作为原始数据,非负矩阵分解得到的特征基包含了原始图像的整体特征,将高分辨率图像与分解得到的特征基中的第一特征基作直方图匹配,并代替第一特征基。利用特征基进行重构,得到具有较高的空间分辨率和保持原有多光谱图像的光谱信息的融合图像。主观视觉效果分析和客观统计参数评价分析表明,基于非负矩阵分解的图像融合方法在性能上优于传统的其他融合方法。     

光谱重建约束非负矩阵分解的高光谱与全色图像融合

基于光谱重建约束的非负矩阵分解,提出了一种高光谱与全色图像的有效解混方法.首先在高光谱图像的非负矩阵分解中引入光谱重建误差最小化的正则项,通过多目标寻优寻找最佳的正则项参数,以鼓励分解的光谱特征矩阵包含更真实的光谱特征;然后对全色图像进行非负矩阵分解,以获得描述图像细节的丰度矩阵;最后利用光谱特征矩阵和丰度矩阵重建得到...     

多光谱遥感图像与高分辨率全色图像融合研究

介绍了遥感图像融合的一般过程和特点,研究了像素级融合的常用算法,归纳了融合图像的基本步骤,采用四种融合方法对高空间分辨率的全色图像与高光谱分辨率的多光谱图像进行像素级融合实验,发现基于小波变换的图像融合提供更多细节信息,Brovey变换法融合全色图像与多光谱图像目视效果最好,速度最快。     

多光谱图像与全色图像的像素级融合研究

以高空间分辨率的全色图像与高光谱分辨率的多光谱图像进行像素级双源融合为例，详细地总结了卫星多源遥感图像融合领域像素级融合的步骤、基本融合模型和优缺点，重点分析了各种常见像素级融合方法的原理和特点，并归纳了像素级融合结果的主客观评价标准和评价方法，以及像素级融合的主要应用领域，最后讨论了像素级融合目前存在的问题和今后的发展方向。     

基于比值变换的全色与多光谱图像高保真融合方法

随着遥感技术的快速发展,越来越多的成像卫星可采集同时相的全色和多光谱图像。通常,多光谱图像的分辨率低于全色图像,但实际应用中人们更迫切需要提升多光谱图像的分辨率。当前,研究人员已提出了大量的图像融合方法来解决这个问题,但依然存在光谱失真、细节模糊等问题。此外,真实遥感图像的尺寸较大,现有融合方法难以满足实际应用的高时效要求。为此,提出了基于比值变换的全色与多光谱图像高保真融合方法。根据全色与多光谱图像分辨率之比,该方法对全色图像先下采样,再上采样,生成全色降质图像;同时,对多光谱图像进行上采样,得到多光谱降质图像;然后,计算全色与其降质图像的比值,将多光谱降质图像乘以该比值生成融合图像。实验表明,本方法的保真效果好,性能优于对比方法。     

基于脊波的多光谱和全色图像融合方法研究

应用了双线性插值的矩形阵列到径向阵列的变换算法,给出了一个离散脊波变换的实现方法,将其应用于多光谱图像与全色图像的融合算法中,通过清晰度、灰度方差、信息熵三个方面,将算法结果与小波变换的结果进行了对比,实验结果表明,相对于小波变换而言,脊波变换能更好地处理线和面的奇异性,而且由融合的结果来看,脊波变换得到的结果在清晰度等方面要高于小波变换。     

基于2DPCA-NSCT变换的多光谱与全色图像融合

鉴于应用单一主成分分析(PCA)或非下采样Contourlet(NSCT)变换进行多光谱和全色图像融合存在的问题,提出了一种2DPCA-NSCT变换图像融合算法.首先对多光谱图像各波段进行二维PCA变换,视其主成分为信号而少量非主成分为噪声予以忽略;然后对全色图像和第一主成分做NSCT分解,在频域对近似分量和多方向高频分量按不同的融合规则融合;最后通过NSCT反变换得到融合图像.实验结果表明,所提出的融合算法在保持PCA变换良好的空间分辨率的同时改善了其光谱失真的问题.     

调制传递函数对全色多光谱图像融合影响

针对高分辨率影像全色(Panchromatic,Pan)波段和多光谱(Multispectral,MS)波段的pan-sharpening融合后图像光谱失真的问题,基于调制传递函数(Modulation Transfer Function,MTF)的全色多光谱图像融合模型考虑到了多光谱图像的MTF值对融合图像质量的影响,采用了与多光谱图像相同的MTF值所构建的低通滤波器,得到较好的融合结果,但如何选择一个合适的MTF值还没有很好地解决。该文针对不同MTF值对模型融合结果的影响做了详细的分析与实验,并通过线性搜索的方式找出最优的MTF值。实验结果证明了该最优MTF能够同时提高模型融合结果的光谱细节和空间细节。     

基于Directionlets和PCA的多光谱与全色图像融合

基于数字化线段理论和整数栅格理论的Directionlets不仅继承了小波变换维数可分性的特点,而且通过选择变换方向和队列方向来获得灵活的多方向性,从而得到能够更好地捕获图像方向信息的方向各向异性的基函数。首先基于Directionlets和PCA的全色和多光谱图像融合方法,对多光谱图像进行线性PCA变换,并提取出其主分量;然后使用Directionlets提取高空间分辨率的全色图像的空间细节信息,将其"注入"到多光谱图像的主分量中。因此,得到的融合图像具有更多的多光谱图像的光谱信息和全色图像的空间信息。实验结果表明,在UIQI指数、整体图像质量指数Q4、平均梯度等主观视觉效果和客观评价指标上,新方法均优于基于小波变换的方法。     

基于ιαβ空间的多光谱和全色图像融合

提出了一种新的基于ιαβ空间的图像融合方法,该方法可以用来对低分辨率的多光谱图像和高分辨率的全色图像进行融合.该方法通过对多光谱图像和全色图像的融合,得到一幅融合后的图像,该融合后的图像集合了多光谱图像的光谱信息和全色图像的空间信息.实验结果表明该方法效果良好,优于传统的以及改进的IHS方法和PCA方法.     

Nonnegative matrix factorization-based hyperspectral and panchromatic image fusion

The fusion of hyperspectral image and panchromatic image is an effective process to obtain an image with both high spatial and spectral resolutions. However, the spectral property stored in the original hyperspectral image is often distorted when using the class of traditional fusion techniques. Therefore, in this paper, we show how explicitly incorporating the notion of “spectra preservation” to improve the spectral resolution of the fused image. First, a new fusion model, spectral preservation based on nonnegative matrix factorization (SPNMF), is developed. Additionally, a multiplicative algorithm aiming at get the numerical solution of the proposed model is presented. Finally, experiments using synthetic and real data demonstrate the SPNMF is a superior fusion technique for it could improve the spatial resolutions of hyperspectral images with their spectral properties reliably preserved.     

Fusion of multispectral and panchromatic satellite sensor imagery based on tailored filtering in the Fourier domain

A new methodology for fusing satellite sensor imagery, based on tailored filtering in the Fourier domain is proposed. Finite‐duration Impulse Response (FIR) filters have been designed through an objective criterion, which depends on source image characteristics only. The designed filters allow a weighted fusion of the information contained in a fine spatial resolution image (PAN) and in a multispectral image (MULTI), respectively, establishing a trade‐off between spatial and spectral quality of the resulting fused image. This new technique has been tested with Landsat Enhanced Thematic Mapper Plus (ETM+) imagery. Spatial and spectral quality of the fused images was compared with the results provided by Mallat's Wavelet algorithm. The images fused by the proposed method were characterized by a spatial resolution very close to the PAN image, and by the spectral resolution of the MULTI image.     

局部投影可分离的高光谱图像异常检测

针对高光谱异常检测中临近异常像素相互干扰和背景地物复杂的问题,提出基于局部投影可分离的高光谱图像异常检测算法.在归一化的数据中,将待测像素光谱作为参考光谱,构造目标子空间,然后把邻域背景像素投影到该子空间,用投影后向量模值构造异常度计算式.最后将检测到的异常与全局主要背景地物进行比对,肖除部分虚警.利用HyMap高光谱数据进行仿真实验结果表明,本文算法具有克服背景复杂性和干扰点的影响,尤其对异类干扰点的抑制效果更佳.     

Multispectral and panchromatic image fusion based on spatial consistency

Multispectral (MS) and panchromatic (Pan) image fusion, which is used to obtain both high spatial- and spectral-resolution images, plays an important role in many remote-sensing applications such as environmental monitoring, agriculture, and mineral exploration. This article presents an image fusion framework based on the spatial distribution consistency. First, a YUV transform is adopted to separate the luminance component from the colour components of the original MS image. Then, the relationships between the ideal high-resolution multispectral (HRMS) colour components and the Pan band are established based on the spatial distribution consistency, and finally an inverse transform is employed to obtain the fused image. In this article, two types of relationship models are presented. The first model stems from the physical meaning of the assumption and uses a local linear model to describe it. The second model directly uses its algebraic meaning to design the objective cost function and obtains the global optimal solution. The proposed two models are compared with 15 other widely used methods on six real remote-sensing image data sets. Experimental results show that the proposed method outperforms the compared state-of-the-art approaches.     

多特征融合的运动想象脑电特征提取方法

针对单一特征识别率低、自适应性差等问题,提出一种基于希尔伯特-黄变换（HHT）和共同空间模式（CSP）的特征提取方法HCHT。首先,对原始脑电信号（EEG）进行经验模态分解（EMD）得到固有模态函数（IMF）,并将IMF分量合并成新的信号矩阵;然后,对IMF进行希尔伯特谱分析,得到信号的时-频域特征;接着,对构造的信号矩阵进行进一步的CSP分解,将时-频域特征扩展成时-频-空域特征;最后,通过支持向量机（SVM）对特征集进行分类。在BCI Competition II数据集的实验表明,与HHT时-频域和CSP空域特征的方法相比,所提方法的识别准确率分别提高了7.5、10.3和9.2个百分点,且标准差更小。在智能轮椅平台进行在线实验的结果表明,HCHT能有效提高识别准确率和稳定性。     

Autonomous detection of cryospheric change with hyperion on-board Earth Observing-1

On-board detection of cryospheric change in sea ice, lake ice, and snow cover is being conducted as part of the Autonomous Sciencecraft Experiment (ASE), using classifiers developed for the Hyperion hyper-spectral visible/infrared spectrometer on-board the Earth Observing-1 (EO-1) spacecraft. This classifier development was done with consideration for the novel limitations of on-board processing, data calibration, spacecraft targeting error and the spectral range of the instrument. During on-board tests, these algorithms were used to measure the extent of cloud, snow, and ice cover at a global suite of targets. Coupled with baseline imaging, uploaded thresholds were used to detect cryospheric changes such as the freeze and thaw of lake ice and the formation and break-up of sea ice. These thresholds were used to autonomously trigger follow-up observations, demonstrating the capability of the technique for future planetary missions where downlink is a constrained resource and there is high interest in data covering dynamic events, including cryospheric change. Before upload classifier performance was assessed with an overall accuracy of 83.3% as measured against manual labeling of 134 scenes. Performance was further assessed against field mapping conducted at Lake Mendota, Wisconsin as well as with labeling of scenes that were classified during on-board tests.     

Object-based fusion for urban tree species classification from hyperspectral,panchromatic and nDSM data

This study aims at identifying the best object-based fusion strategy that takes advantage of the complementarity of several heterogeneous airborne data sources for improving the classification of 15 tree species in an urban area (Toulouse, France). The airborne data sources are: hyperspectral Visible Near-Infrared (160 spectral bands, spatial resolution of 0.4 m) and Short-Wavelength Infrared (256 spectral bands, 1.6 m), panchromatic (14 cm), and a normalized Digital Surface Model (12.5 cm). Object-based feature and decision level fusion strategies are proposed and compared when applied to a reference site where the species are previously identified during ground truth collection. This allows the best fusion strategy to be selected with a view to introducing the method in an automatic process (tree crown delineation and species classification) on a test site, independent of the reference site used for learning. In particular, a decision level fusion is selected: based on the Support Vector Machine algorithm, Visible Near-Infrared and Short-Wavelength Infrared classifications use Minimum Noise Fraction components at the original spatial resolution, whereas panchromatic and normalized Digital Surface Model classifications use, respectively, Haralick’s and structural features computed at the object scale. After the computation of a decision profile for each source at the object level based on the classification algorithms’ membership probabilities, these decision profiles are combined and a decision rule is applied to predict the species. Focusing on the reference site, the Visible Near-Infrared exhibits the best performances with F-score values higher than 60% for 13 species out of 15. The Short-Wavelength Infrared is the most powerful for three species with F-score greater than 60% for seven common species with the Visible Near-Infrared. The panchromatic and normalized Digital Surface Model contribute marginally. The best fusion strategy (decision fusion) does not improve significantly the overall accuracy with 77% (kappa = 74%) against 75% (kappa = 72%) for the Visible Near-Infrared but in general, it improves the results for cases where complementarities have been observed. When applied to the test site and assessed for the two majority species (Tilia tomentosa and Platanus x hispanica), the selected approach gives consistent results with an overall accuracy of 63% against 55% for the Visible Near-Infrared.     

线性模型的遥感图像时空融合

目的 时空融合是解决当前传感器无法兼顾遥感图像的空间分辨率和时间分辨率的有效方法。在只有一对精细-粗略图像作为先验的条件下,当前的时空融合算法在预测地物变化时并不能取得令人满意的结果。针对这个问题,本文提出一种基于线性模型的遥感图像时空融合算法。方法 使用线性关系表示图像间的时间模型,并假设时间模型与传感器无关。通过分析图像时间变化的客观规律,对模型进行全局和局部约束。此外引入一种多时相的相似像素搜寻策略,更灵活地选取相似像素,消除了传统算法存在的模块效应。结果 在两个数据集上与STARFM（spatial and temporal adaptive reflectance fusion model）算法和FSDAF（flexible spatiotemporal data fusion）算法进行比较,实验结果表明,在主要发生物候变化的第1个数据集,本文方法的相关系数CC（correlation coefficient）分别提升了0.25%和0.28%,峰值信噪比PSNR（peak signal-to-noise ratio）分别提升了0.153 1 dB和1.379 dB,均方根误差RMSE（root mean squared error）分别降低了0.05%和0.69%,结构相似性SSIM（structural similarity）分别提升了0.79%和2.3%。在发生剧烈地物变化的第2个数据集,本文方法的相关系数分别提升了6.64%和3.26%,峰值信噪比分别提升了2.086 0 dB和2.510 7 dB,均方根误差分别降低了1.45%和2.08%,结构相似性分别提升了11.76%和11.2%。结论 本文方法根据时间变化的特点,对时间模型进行优化,同时采用更加灵活的相似像素搜寻策略,收到了很好的效果,提升了融合结果的准确性。     

基于目标优化的高光谱图像亚像元定位

目的 高光谱图像混合像元的普遍存在使得传统的分类技术难以准确确定地物空间分布,亚像元定位技术是解决该问题的有效手段。针对连通区域存在孤立点或孤立两点等特例时,通过链码长度求周长最小无法保证最优结果及优化过程计算量大的问题,提出了一种改进的高光谱图像亚像元定位方法。方法 以光谱解混结合二进制粒子群优化构建算法框架,根据光谱解混结果近似估计每个像元对应的亚像元组成,通过分析连通区域存在特例时基于链码长度求周长最小无法保证结果最优的原因,提出修改孤立区域的周长并考虑连通区域个数构造代价函数,最后利用二进制粒子群优化实现亚像元定位。为了减少算法的时间复杂度,根据地物空间分布特点,采用局部分析代替全局分析,提出了新的迭代优化策略。结果 相比直接基于链码长度求周长最小的优化结果,基于改进的目标函数优化后,大部分区域边界更明显,并且没有孤立1点和孤立两点的区域,识别率可以提高2%以上,Kappa系数增加0.05以上,新的优化策略可以使算法运算时间减少近一半。结论 实验结果表明,本文方法能有效提高亚像元定位精度,同时降低时间复杂度。因为高光谱图像中均匀混合区域不同地物的分布空间相关性不强,因此本文方法适用于非均匀混合的高光谱图像的亚像元定位。