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。     

A spatio-temporal fusion method for remote sensing data Using a linear injection model and local neighbourhood information

This paper presents a spatio-temporal fusion method for remote sensing images by using a linear injection model and local neighbourhood information. In this method, the linear injection model is first introduced to generate an initial fused image, the spatial details are extracted from the fine-resolution image at the base date, and are weighted by a proper injection gains. Then, the spatial details and the relative spectral information from the coarse-resolution images are blended to generate the fusion result. To further enhance its robustness to the noise, the local neighbourhood information, derived from the fine-resolution image and the fused result simultaneously, is introduced to refine the initial fused image to obtain a more accurate prediction result. The algorithm can effectively capture phenology change or land-cover-type change with minimum input data. Simulated data and two types of real satellite images with seasonal changes and land-cover-type changes are employed to test the performance of the proposed method. Compared with a spatial and temporal adaptive reflectance fusion model (STARFM) and a flexible spatio-temporal fusion algorithm (FSDAF), results show that the proposed approach improves the accuracy of fused images in phenology change area and effectively captures land-cover-type reflectance changes.     

Super-resolution mapping using Hopfield Neural Network with panchromatic imagery

Land-cover proportions of mixed pixels can be predicted using soft classification. From the land-cover proportions, a hard land-cover map can be predicted at sub-pixel spatial resolution using super-resolution mapping techniques. It has been demonstrated that the Hopfield Neural Network (HNN) provides a suitable method for super-resolution mapping. To increase the detail and accuracy of the sub-pixel land-cover map, supplementary information at an intermediate spatial resolution can be used. In this research, panchromatic (PAN) imagery was used as an additional source of information for super-resolution mapping. Information from the PAN image was captured by a new PAN reflectance constraint in the energy function of the HNN. The value of the new PAN reflectance constraint was defined based on forward and inverse models with local end-member spectra and local convolution weighting factors. Two sets of simulated and degraded data were used to test the new technique. The results indicate that PAN imagery can be used as a source of supplementary information to increase the detail and accuracy of sub-pixel land-cover maps produced by super-resolution mapping from land-cover proportion images.     

A generalization of spatial and temporal fusion methods for remotely sensed surface parameters

Remotely sensed surface parameters, such as vegetation index, leaf area index, surface temperature, and evapotranspiration, show diverse spatial scales and temporal dynamics. Generally the spatial and temporal resolutions of remote-sensing data should match the characteristics of surface parameters under observation. These requirements sometimes cannot be provided by a single sensor due to the trade-off between spatial and temporal resolutions. Many spatial and temporal fusion (STF) methods have been proposed to derive the required data. However, the methodology suffers from disorderly development. To better inform future research, this study generalizes the existing methods from around 100 studies as spatial or temporal categories based on their physical assumptions related to spatial scales and temporal dynamics. To be specific, the assumptions are related to the scale invariance of the temporal information and temporal constancy of the spatial information. The spatial information can be contexture or spatial details. Experiments are conducted using Landsat data acquired on 13 dates in two study areas and simulated Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results are presented to demonstrate the typical methods from each category. This study concludes the following. (1) Contexture methods depend heavily on how components maps (contexture) are defined. They are not recommended except when components maps can be estimated properly from observed images. (2) The spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) methods belong to the temporal and spatial categories, respectively. Thus, STARFM and ESTARFM should be better applied to temporal variance – dominated and spatial variance – -dominated areas, respectively. (3) Non-linear methods, such as the sparse representation-based spatio-temporal reflectance fusion model, can successfully address land-cover changes in addition to phonological changes, thereby providing a promising option for STF problems in the future.     

Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes

Owing to technical limitations the acquisition of fine spatial resolution images (e.g. Landsat data) with frequent (e.g. daily) coverage remains a challenge. One approach is to generate frequent Landsat surface reflectances through blending with coarse spatial resolution images (e.g. Moderate Resolution Imaging Spectroradiometer, MODIS). Existing implementations for data blending, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced STARFM (ESTARFM), have their shortcomings, particularly in predicting the surface reflectance characterized by land-cover-type changes. This article proposes a novel blending model, namely the Unmixing-based Spatio-Temporal Reflectance Fusion Model (U-STFM), to estimate the reflectance change trend without reference to the change type, i.e. phenological change (e.g. seasonal change in vegetation) or land-cover change (e.g. conversion of a vegetated area to a built-up area). It is based on homogeneous change regions (HCRs) that are delineated by segmenting the Landsat reflectance difference images. The proposed model was tested on both simulated and actual data sets featuring phenological and land-cover changes. It proved more capable of capturing both types of change compared to STARFM and ESTARFM. The improvement was particularly observed on those areas characterized by land-cover-type changes. This improved fusion algorithm will thereby open new avenues for the application of spatio-temporal reflectance fusion.     

An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions

Due to technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result not one sensor provides both high spatial resolution and high temporal resolution. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. One approach is to “blend” the radiometry from daily, global data (e.g. MODIS, MERIS, SPOT-Vegetation) with data from high-resolution sensors with less frequent coverage (e.g. Landsat, CBERS, ResourceSat). Unfortunately, existing algorithms for blending multi-source data have some shortcomings, particularly in accurately predicting the surface reflectance of heterogeneous landscapes. This study has developed an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm, and has tested it with both simulated and actual satellite data. Results show that ESTARFM improves the accuracy of predicted fine-resolution reflectance, especially for heterogeneous landscapes, and preserves spatial details. Taking the NIR band as an example, for homogeneous regions the prediction of the ESTARFM is slightly better than the STARFM (average absolute difference [AAD] 0.0106 vs. 0.0129 reflectance units). But for a complex, heterogeneous landscape, the prediction accuracy of ESTARFM is improved even more compared with STARFM (AAD 0.0135 vs. 0.0194). This improved fusion algorithm will support new investigations into how global landscapes are changing across both seasonal and interannual timescales.     

遥感数据时空融合技术在农作物监测中的适应性研究

受卫星回访周期及云的影响,大范围研究区同一时期的Landsat卫星数据很难获取,因而国内外学者提出了遥感影像时空融合技术。以石河子为实验区,利用STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)模型融合生成了高时空分辨率TM影像,对不同作物类型真实反射率与融合影像反射率作相关性分析,分析了遥感数据时空融合技术在新疆农作物监测中的适用性。结果表明:利用STARFM模型模拟得到的融合影像与真实影像间的相关性较高,但当地物类型发生变化时,融合影像与真实影像间将存在明显的差异。地物类型变化作物融合影像反射率与真实影像反射率间的相关性较小。     

基于多模式均值时空模型的目标融合检测方法

针对复杂环境下的目标检测问题,提出了一种基于背景模型的融合检测方法。首先在多模式均值模型的基础上,构造多模式均值时空模型,结合像素在时空域上的分布信息,改善了模型对非平稳场景较为敏感的缺点,给出了模型更新方法和前景检测方法;然后利用该模型对可见光和红外图像序列分别进行建模和前景检测,给出了一种基于置信度的目标融合检测方法,利用双传感器信息提高检测精度和可靠性。实验结果验证了本文方法的有效性。     

An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery

Because of low temporal resolution and cloud influence, many remote-sensing applications lack high spatial resolution remote-sensing data. To address this problem, this study introduced an improved spatial and temporal data fusion approach (ISTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the weaknesses of the spatial and temporal data fusion approach (STDFA) method, including the sensor difference and spatial variability. A weighted linear mixed model was used to adjust the spatial variability of surface reflectance. A linear-regression method was used to remove the influence of differences in sensor systems. This method was tested and validated in three study areas located in Xinjiang and Anhui province, China. The other two methods, the STDFA and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), were also applied and compared in those three study areas. The results showed that the ISTDFA algorithm can generate daily synthetic Landsat imagery accurately, with correlation coefficient r equal to 0.9857 and root mean square error (RMSE) equal to 0.0195, which is superior to the STDFA method. The ISTDFA method had higher accuracy than ESTARFM in areas greater than 200 × 200 MODIS pixels while the ESTARFM method had higher accuracy than the ISTDFA method in small areas. The correlation coefficient r had a negative power relation with ratio of land-cover change pixels. A land-cover change of 20.25% pixels can lead to a reduced correlation coefficient r of 0.295 in the blue band. The accuracy of the ISTDFA method indicated a logarithmic relationship with the size of the applied area, so it is recommended for use in large-scale areas.     

Blending MODIS and Landsat images for urban flood mapping

Satellite images provide important data sources for monitoring flood disasters. However, the trade-off between spatial and temporal resolutions of current satellite sensors limits their uses in urban flooding studies. This study applied and compared two data fusion models, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), in generating synthetic flooding images with improved temporal and spatial resolution for flood mapping. The synthetic images are produced in two scenarios: (1) for real-time prediction based on Landsat and MODIS images acquired before the investigated flooding; and (2) for post-disaster prediction based on images acquired after the flooding. The 2005 Hurricane Katrina in New Orleans was selected as a case study. The result shows that the Landsat-like images generated can be successfully applied in flood mapping. Particularly, ESTARFM surpasses STARFM in predicting surface reflectance in both real-time and post-flooding predictions. However, the flood mapping results from the Landsat-like images produced by both STARFM and ESTARFM are similar with overall accuracy around 0.9. Only for the flooding maps of real-time predictions does ESTARFM get a slightly higher overall accuracy than STARFM, indicating that the lower quality of the Landsat-like image generated by STARFM may not affect flood mapping accuracy, due to the marked contrast between land and water. This study suggests great potential of both STARFM and ESTARFM in urban flooding research. Blending multi-sources images could also support other disaster studies that require remotely sensed data with both high spatial and temporal resolution.     

基于时空数据融合模型的TM影像云去除方法研究

针对已提出的各类云去除方法在实际应用中存在的局限性,将时空数据融合模型引入到云去除方法中。首先基于MODIS数据提供的时间维变化信息和辅助时相TM数据提供的空间信息,应用增强时空适应反射率融合模型(ESTARFM)得到了目标时相似TM合成数据;然后用TM合成数据替换掉目标时相TM影像中被云及其阴影覆盖区域的数据。在修复后的影像中替换区域与非云区域色调基本一致。通过非云区TM合成数据间接对替换云及其阴影区数据的精度进行定量评价。结果表明:相对于真实TM影像,非云区域合成数据各波段均值差异都在1%以内;各波段的相对误差分别为16.29%、12.92%、13.47%、12.87%、9.71%和11.84%,且各波段的相关系数均大于0.7;非云及其阴影区融合影像数据间接表明填补云及阴影区数据各波段的总体精度优于83%。因此,所提出的方法能够修复TM影像中被云及其阴影覆盖区域的数据,提高MODIS与TM数据的利用率。     

基于多模态局部转向核的脑部多发性硬化检测算法研究

容积效应和伪影现象是MR影像处理中的重要影响因素,单模态处理方法易受两者影响。提出一种改进的基于多模态局部转向核的方法来检测大脑中的多发性硬化。该方法利用多模态脑MR影像和大脑近似轴对称的先验知识来进行大脑情况的变化检测。局部转向核能够度量像素与其周围环境的相似程度,因此该方法将局部转向核作为特征,用余弦相似性来衡量差异性。实验结果表明,多模态的引入减少了容积效应和伪影现象,改善了检测效果。     

Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series

Spatiotemporal fusion (STF) technologies are commonly used to acquire high spatiotemporal resolution remote sensing observations. However, most STF technologies fail to consider the nonlinear variation in vegetation in the time domain. Based on the Best Linear Unbiased Estimator (BLUE), this paper proposed a novel STF algorithm (referred to BLUE) which accounts for the phenological characteristics of vegetation. First, annual time series of normalized difference vegetation index (NDVI) data with high spatial resolution but low temporal resolution is fitted using a double logistic function and used as the background field. Then, NDVI data with low spatial resolution but high temporal resolution is used as the observation field. The information in the background and observation fields is fused using the BLUE to obtain high spatiotemporal resolution NDVI data. The proposed algorithm was used to produce dense time series of 30 m resolution NDVI data for a 10 km × 10 km experimental area in 2014. The experimental results demonstrate that the accuracy of fusion results from the proposed BLUE method are higher than those from the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Linear Mixing Growth Model (LMGM), especially when the temporal component of surface heterogeneity is dominant. The proposed algorithm has broad prospects in vegetation monitoring at high spatiotemporal resolution.     

基于灰色关联分析和IHS变换的图像融合算法*

针对IHS融合算法的光谱畸变问题,提出一种基于灰色关联分析和IHS变换的图像融合算法.该算法利用灰色关联分析法检测出SAR图像像素中的边缘点和非边缘点;通过IHS变换,在线性加权融合时赋予边缘点较大的权值.实验表明,该算法在保持光谱信息方面具有显著的优越性,并且能够有效提高融合图像的空间分辨能力,具有简洁性和抗噪性,明显优于传统的IHS融合算法和改进的基于直方图匹配的IHS融合算法.     

基于自适应混合高斯模型的时空背景建模

提出了一种基于自适应混合髙斯模型的时空背景建模方法, 有效地融合了像素在时空域上的分布信息, 改善了传统的混合髙斯背景建模方法对非平稳场景较为敏感的缺点. 首先利用混合髙斯模型学习每个像素在时间域上的分布, 构造了基于像素的时间域背景模型, 在此基础上, 通过非参数密度估计方法统计每个像素邻域内表示背景的髙斯成分在空间上的分布, 构造了基于像素的空间域背景模型; 在决策层融合了基于时空背景模型的背景减除结果. 为了提高本文时空背景建模的效率, 提出了一种新的混合高斯模型髙斯成分个数的自适应选择策略, 并利用积分图实现了空间域背景模型的快速计算. 通过在不同的场景下与多个背景建模方法比较, 实验结果验证了本文算法的有效性.     

改进的核回归图像恢复

Steering核回归是一种自适应的、有效的图像恢复方法,在图像去噪、放大和去模糊中都得到了广泛应用。但此模型以高斯函数为核函数,故得到的恢复图像边缘,尤其是细小边缘常常会因过分平滑而模糊。提出基于鲁棒统计的各向异性核回归图像恢复模型,该模型在Steering核回归模型基础上,结合各向异性距离,以鲁棒统计权函数代替高斯核函数。大量图像恢复实验结果显示,与Steering核回归方法相比较,所提出方法得到的恢复图像质量显著提高,尤其是在细小边缘保持方面更具有明显优势。     

A clustering algorithm with optimized multiscale spatial texture information: application to SAR image segmentation

An image segmentation method based on optimized spatial texture information is proposed in this article. Spatial information, including the relative position of neighbouring pixels and texture features of the multiscale neighbourhood, is incorporated into the similarity measure of the fuzzy c-means (FCM) clustering algorithm, in which the Gaussian kernel is adopted to diminish the local incorrect segmentation. The FCM clustering is spatially adjusted and optimized by the particle swarm optimization (PSO) algorithm. The purpose of optimization is to obtain the appropriate control parameters influencing spatial information, which can improve segmentation results. Experimental results demonstrate that the proposed method achieves better segmentation performance and is capable of effectively segmenting synthetic images and synthetic aperture radar (SAR) images.     

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.     

多云雾地区高时空分辨率植被覆盖度构建方法研究

针对多云雾地区高时空分辨率数据缺乏现状,提出了一套区域尺度高时空分辨率植被覆盖度数据构建方法.首先,通过时空适应反射率融合模型(STARFM)有效地将TM 的较高空间分辨率与MODIS的高时间分辨率融合在一起,构建了研究区植被生长峰值阶段的NDVI数据;然后,以植被生长峰值阶段的NDVI为输入,基于地表覆被类型,综合应用等密度和非密度亚像元模型对研究区的植被覆盖度进行估算.结果表明:①即使数据源存在大量的云雾,且存在一定的时相差异,研究区植被覆盖度的估算结果过渡自然,不存在明显的不接边效应;②以植被生长峰值阶段的NDVI数据为输入进行植被覆盖度估算,有效拉开了同一地表覆被类型不同覆盖度像元的NDVI梯度,提高了亚像元估算模型对输入数据的抗扰动性;③基于地表覆被类型,应用亚像元混合模型,能够提高植被覆盖度的估算精度.经野外实测数据验证,总体约８５％的估算精度表明,针对高时空分辨率遥感数据缺乏的多云雾区域,本研究提出的方法能够实现区域尺度植被覆盖度数据的构建.