Integration of Spatial–Spectral Information for Resolution Enhancement in Hyperspectral Images

In this paper, a new algorithm is proposed for resolution enhancement in hyperspectral images (HSIs). The key techniques are included: spectral unmixing and superresolution mapping, by which spatial and spectral information of HSIs is substantially fused. The proposed algorithm first represents each pixel in scene as a linear combination of landcover spectra and noise. Then, a fully constrained least squares algorithm is used to obtain the proportion of each landcover in each pixel, i.e., abundance, subjecting to two constraints: nonnegativity and sum-to-one. After that, superresolution mapping is performed on high-resolution grids according to spectral unmixing abundances of each landcover and following spatial correlation of clutters. Thus, by reasonably integrating spatial and spectral information of landcovers in HSIs, the proposed algorithm realizes resolution enhancement of the HSIs based on a back-propagation neural network. The proposed algorithm is independent from the a priori information associated with original HSIs, i.e., a main merit of the algorithm. In order to evaluate the performance of the new algorithm, numerical experiments are conducted on both simulated images and real HSIs collected by the Airborne Visible/Infrared Imaging Spectrometer. The proposed algorithm is compared with the traditional method in the experiments. The experimental results prove that the proposed algorithm effectively enhances the resolution of HSIs and indicate its applicability.     

基于多字典稀疏表示的遥感图像亚像元映射

本文提出了一种基于多字典稀疏表示的亚像元映射算法,利用已知的同类型高空间分辨率地物分布图像,构建能够更好反映不同类别地物空间分布模式的多个字典,将待分类亚像元用每一类字典稀疏表示,并依据重构误差最小化原则以及光谱失真程度约束条件来划分亚像元的地物类别.模拟与真实数据上的实验结果表明,本文算法能有效应对地物空间分布模式的多样性,具有更高的亚像元映射精度和更好的算法鲁棒性.     

遥感图像的MAP超分辨重建

遥感根本目的就是获得清晰的高空间分辨率的图像,从而可以进一步地分析处理。为了在遥感测量中获得更高空间分辨率、更高信噪比、更清晰的图像,本文对图像处理领域超分辨算法进行了研究。建立了一套拟合模拟现实的成像系统模型,在这种模型的基础之上,利用最大后验概率系统理论,讨论了现实情况中的运动模糊,噪声等情况,改进了MAP超分辨算法。实验结果表明:使用本文改进的基于MAP理论的Markov随机场约束的多帧超分辨重建算法,可以较好提高超分辨效果,与三次立方插值方法相比,PSNR至少提高约5.1dB左右,与未改进的MAP方法相比,PSNR提高约0.2dB左右。本文提出了动态的先验约束方法,给约束函数添加与迭代次数相关的约束项,该改进创新可以加快收敛并且更加逼近真实图像,实验表明该方法收敛速度更快,约束效果良好,更适合实际应用。     

Super-resolution target identification from remotely sensed imagesusing a Hopfield neural network

Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorded     

An Innovative Neural-Net Method to Detect Temporal Changes in High-Resolution Optical Satellite Imagery

The advent of new high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover changes from space. Satellite observations are carried out regularly and continuously, and provide a great deal of insight into the temporal changes of land cover use. High spatial resolution imagery better resolves the details of these changes and makes it possible to overcome the "mixed-pixel" problem that is inherent with more moderate resolution satellite sensors. At the same time, high-resolution imagery presents a new challenge over other satellite systems, in that a relatively large amount of data must be analyzed and corrected for registration and classification errors to identify the land cover changes. To obtain the accuracies that are required by many applications to large areas, very extensive manual work is commonly required to remove the classification errors that are introduced by most methods. To improve on this situation, we have developed a new method for land surface change detection that greatly reduces the human effort that is needed to remove the errors that occur with many classification methods that are applied to high-resolution imagery. This change detection algorithm is based on neural networks, and it is able to exploit in parallel both the multiband and the multitemporal data to discriminate between real changes and false alarms. In general, the classification errors are reduced by a factor of 2-3 using our new method over a simple postclassification comparison based on a neural-network classification of the same images.     

多偏移遥感图像的BP神经网络亚像元定位

提出了一种借助多偏移遥感图像来改进基于BP神经网络(BPNN)的亚像元定位新方法.不同于原BPNN方法使用单幅低空间分辨率观测图像,新方法利用多幅带有亚像元偏移的低空间分辨图像来确定亚像元属于各类的概率,然后根据概率值和地物覆盖比例确定亚像元类别,以降低BPNN定位模型中的不确定性和误差.实验表明,提出方法在视觉和定量评价上,均能获得更高精度的亚像元定位结果,验证了提出方法的有效性.     

Estimation of the Number of Decomposition Levels for a Wavelet-Based Multiresolution Multisensor Image Fusion

The wavelet-based scheme for the fusion of multispectral (MS) and panchromatic (PAN) imagery has become quite popular due to its ability to preserve the spectral fidelity of the MS imagery while improving its spatial quality. This is important if the resultant imagery is used for automatic classification. Wavelet-based fusion results depend on the number of decomposition levels applied in the wavelet transform. Too few decomposition levels result in poor spatial quality fused images. On the other hand, too many levels reduce the spectral similarity between the original MS and the pan-sharpened images. If the shift-invariant wavelet transform is applied, each excessive decomposition level results in a large computational penalty. Thus, the choice of the number of decomposition levels is significant. In this paper, PAN and MS image pairs with different resolution ratios were fused using the shift-invariant wavelet transform, and the optimal decomposition levels were determined for each resolution ratio. In general, it can be said that the fusion of images with larger resolution ratios requires a higher number of decomposition levels. This paper provides the practitioner an understanding of the tradeoffs associated with the computational demand and the spatial and spectral quality of the wavelet-based fusion algorithm as a function of the number of decomposition levels     

Target-cluster fusion approach for classifying high resolution IKONOS imagery

To extract GIS features from high spatial resolution imagery is an important task in remote sensing applications. However, traditional pixel-based classification methods, which were developed in the era of 10-100 m ground pixel size imagery, cannot exploit the advantages of new images provided by IKONOS and QuickBird. This is due to the increase of the within-class variability inherent from more detailed and higher spatial resolution data. To successfully extract various land covers from high resolution imagery, a target-clustering fusion (TCF) system is presented in the paper. Compared to the conventional classification methods that typically produce more salt-and-pepper-like results, the proposed TCF system can preserve detailed spatial information on each classified target related to its neighbours. To evaluate the efficacy of TCF, experiments are conducted using real IKONOS images.     

Scaling-up and model inversion methods with narrowband opticalindices for chlorophyll content estimation in closed forest canopieswith hyperspectral data

Radiative transfer theory and modeling assumptions were applied at laboratory and field scales in order to study the link between leaf reflectance and transmittance and canopy hyper-spectral data for chlorophyll content estimation. This study was focused on 12 sites of Acer saccharum M. (sugar maple) in the Algoma Region, Canada, where field measurements, laboratory-simulation experiments, and hyper-spectral compact airborne spectrographic imager (CASI) imagery of 72 channels in the visible and near-infrared region and up to 1-m spatial resolution data were acquired in the 1997, 1998, and 1999 campaigns. A different set of 14 sites of the same species were used in 2000 for validation of methodologies. Infinite reflectance and canopy reflectance models were used to link leaf to canopy levels through radiative transfer simulation. The closed and dense (LAI>4) forest canopies of Acer saccharum M. used for this study, and the high spatial resolution reflectance data targeting crowns, allowed the use of optically thick simulation formulae and turbid-medium SAILH and MCRM canopy reflectance models for chlorophyll content estimation by scaling-up and by numerical model inversion approaches through coupling to the PROSPECT leaf radiative transfer model. Study of the merit function in the numerical inversion showed that red edge optical indices used in the minimizing function such as R₇₅₀/R₇₁₀ perform better than when all single spectral reflectance channels from hyper-spectral airborne CASI data are used, and in addition, the effect of shadows and LAI variation are minimized     

融合MODIS与Landsat数据生成高时间分辨率Landsat数据

遥感数据时空融合技术是一种低空间分辨率影像与中空间分辨率影像在时间域和空间域的融合技术,利用遥感数据时空融合技术获得的融合影像既具备低空间分辨率影像的高时间分辨率特征,又具备中空间分辨率影像的高空间分辨率特征.提出了一种新的遥感数据时空融合方法(STDFA).该方法从时序MODIS数据中提取地物的时间变化信息,结合早期Landsat-TM影像的纹理信息,融合出具有MODIS时间分辨率和TM空间分辨率的影像.以江苏省南京市江宁区为研究区,以Landsat红波段和近红外波段为融合波段,对该方法进行了测试.结果显示,该方法能够产生高精度的中空间分辨率影像,融合影像与真实影像间的相关系数达到0.939.融合影像计算的NDVI与真实中空间分辨率影像计算的NDVI间的相关性达到0.938.     

Resolution Enhancement of Multilook Imagery for the Multispectral Thermal Imager

This paper studies the feasibility of enhancing the spatial resolution of multilook Multispectral Thermal Imager (MTI) imagery using an iterative resolution enhancement algorithm known as Projection Onto Convex Sets (POCS). A multiangle satellite image modeling tool is implemented, and simulated multilook MTI imagery is formed to test the resolution enhancement algorithm. Experiments are done to determine the optimal configuration and number of multiangle low-resolution images needed for a quantitative improvement in the spatial resolution of the high-resolution estimate. The issues of atmospheric path radiance and directional reflectance variations are explored to determine their effect on the resolution enhancement performance.     

Phase-Adaptive Superresolution of Mammographic Images Using Complex Wavelets

This correspondence describes a new superresolution approach for enhancing the resolution of mammographic images using complex wavelet frequency information. This method allows regions of interest of a mammographic image to be viewed in enhanced resolution while reducing the patient exposure to radiation. The proposed method exploits the structural characteristics of breast tissues being imaged and produces higher resolution mammographic images with sufficient visual fidelity that fine image details can be discriminated more easily. In our approach, the superresolution problem is formulated as a constrained optimization problem using a third-order Markov prior model and adapts the priors based on the phase variations of the low-resolution mammographic images. Experimental results indicate the proposed method is more effective at preserving the visual information when compared with existing resolution enhancement methods.     

A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution)

Superresolution produces high-quality, high-resolution images from a set of degraded, low-resolution images where relative frame-to-frame motions provide different looks at the scene. Superresolution translates data temporal bandwith into enhanced spatial resolution. If considered together on a reference grid, given low-resolution data are nonuniformly sampled. However, data from each frame are sampled regularly on a rectangular grid. This special type of nonuniform sampling is called interlaced sampling. We propose a new wavelet-based interpolation-restoration algorithm for superresolution. Our efficient wavelet interpolation technique takes advantage of the regularity and structure inherent in interlaced data, thereby significantly reducing the computational burden. We present one- and two-dimensional superresolution experiments to demonstrate the effectiveness of our algorithm.This work was supported in part by the National Science Foundartion Grant CCR-9984246.     

基于约束空间光谱联合的亚像素定位方法北大核心CSCD

针对高光谱遥感图像,提出了一种约束空间光谱的亚像素定位方法。传统的亚像素定位方法以解混的结果作为输入,可能无法充分利用高光谱图像丰富的光谱信息。本文所提出的基于约束空间光谱联合的亚像素定位方法(constraint spatial-spectral subpixel mapping,CSSSM),利用下采样将像素丰度与亚像素丰度显式联系起来,代入线性解混模型得到亚像素丰度求解的新模型。在求解过程中,通过添加稀疏性约束与平滑性约束,以限制亚像素丰度的解空间,亚像素丰度求解更精确。其中,针对亚像素丰度稀疏性先验采用重加权1范数作为新的约束,并自适应地更新权重;针对亚像素丰度空间先验信息则采用全变分(total variational,TV)正则化作为约束,然后使用乘法迭代算法求解亚像素丰度,最后利用赢者通吃的策略进行类别确定。在两个合成数据集上进行了实验,结果表明,本方法能够进一步提高亚像素定位的精度。     

On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance

The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic "daily" surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes.     

Atmospheric correction of Landsat ETM+ land surface imagery. II. Validation and applications

For pt.I see ibid., vol.39, no.11, p.2490-8 (2001). This is the second paper of the series on atmospheric correction of Enhanced Thematic Mapper-Plus (ETM+) land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to (1) evaluate the accuracy of this new atmospheric correction algorithm using ground radiometric measurements, (2) apply this algorithm to correct Moderate-Resolution Imaging Spectroradiometer (MODIS) and SeaWiFS imagery, and (3) demonstrate how much atmospheric correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that atmospheric correction does matter.     

Sea Ice Tracking by Nested Correlations

Spatial differences in sea ice displacement affect ice stress, ice production, and the mass balance of the ice cover. Our concepts about the spatial structure of this field have been undernourished because of a paucity of data with high spatial detail and because of the tedium of extracting such measurements from images manually. A method is described that measures displacements from synthetic aperture radar digital imagery with fine spatial resolution, and does so fully automatically. Many small areas of ice common to two images are identified by correlating the two images. The strategy is to acquire a crude displacement field first from highly averaged images, and to refine this field with images of successively higher resolution. The median discrepancy between automatically and manually measured displacements is three pixels (0.075 km). The algorithm operates successfully on compact ice with large floes and modest rotation rates; we believe it will prove applicable to most of the arctic ice cover throughout the year.     

Retrieval of aerosol optical properties from multi-angle satelliteimagery

The authors have been investigating a novel method for retrieving atmospheric aerosol optical properties over land. The described method makes use of the presence of spatial contrasts, within the imagery, and compares the spatial Fourier transforms of the multiangle images at zero and nonzero spatial frequencies. An initial guess for the aerosol properties is made and the Fourier components of the surface reflectance distribution are retrieved at zero and nonzero frequencies. A comparison of the angular shape of the retrieved functions is then used to establish whether the initial guess for the aerosol parameters was correct. Other than the need to define a relationship between the angular variation of the Fourier transform of the intrinsic surface angular reflectance at the zero and nonzero frequencies, the method assumes no prior knowledge about the aerosols or surface characteristics. The authors discuss sensitivities to various aerosol properties     

Spectral analysis and adaptive array superresolution techniques

Recent nonlinear "superresolution" techniques reported in the field of spectral analysis are of great interest in other fields as well, including radio-frequency (RF) adaptive array antenna systems. This paper is primarily a "cross-fertilization" treatise which takes the two most popular nonlinear techniques, the Burg maximum entropy method and the maximum likelihood method, and relates them to their similar nonlinear adaptive array antenna counterparts, which consist of the generic sidelobe canceller and directional gain constraint techniques. The comparison analysis permits an examination of their principles of operation from the antenna spatial pattern viewpoint, and helps to qualify, their actual superresolution performance. A summary of the resolution performance of several adaptive algorithms against multiple-incoherent sources is provided, including a universal graph of signal-to-noise ratio (SNR) versus source separation in beamwidths for the case of two equal-strength sources. Also, a significant dividend in the easy resolution of unequal-strength sources is reported. The superresolution of coherent spatial sources or radar targets is more difficult for these techniques, but successful results have been obtained whenever sufficient relative motion or "Doppler cycles" are available. Two alternate adaptive spatial spectrum estimators are suggested, consisting of a circular array predicting to its center point, and a new "thermal noise" algorithm.