Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data

MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.

Super-resolution compressive spectral imaging via two-tone adaptive coding

Coded apertures with random patterns are extensively used in compressive spectral imagers to sample the incident scene in the image plane.Random samplings,however,are inadequate to capture the structural characteristics of the underlying signal due to the sparsity and structure nature of sensing matrices in spectral imagers.This paper proposes a new approach for super-resolution compressive spectral imaging via adaptive coding.In this method,coded apertures are optimally designed based on a two-tone adaptive compressive sensing(CS)framework to improve the reconstruction resolution and accuracy of the hyperspectral imager.A liquid crystal tunable filter(LCTF)is used to scan the incident scene in the spectral domain to successively select different spectral channels.The output of the LCTF is modulated by the adaptive coded aperture patterns and then projected onto a lowresolution detector array.The coded aperture patterns are implemented by a digital micromirror device(DMD)with higher resolution than that of the detector.Due to the strong correlation across the spectra,the recovered images from previous spectral channels can be used as a priori information to design the adaptive coded apertures for sensing subsequent spectral channels.In particular,the coded apertures are constructed from the a priori spectral images via a two-tone hard thresholding operation that respectively extracts the structural characteristics of bright and dark regions in the underlying scenes.Super-resolution image reconstruction within a spectral channel can be recovered from a few snapshots of low-resolution measurements.Since no additional side information of the spectral scene is needed,the proposed method does not increase the system complexity.Based on the mutual-coherence criterion,the proposed adaptive CS framework is proved theoretically to promote the sensing efficiency of the spectral images.Simulations and experiments are provided to demonstrate and assess the proposed adaptive coding method.Finally,the underlying concepts are extended to a multi-channel method to compress the hyperspectral data cube in the spatial and spectral domains simultaneously.

Combination of super-resolution reconstruction and SGA-Net for marsh vegetation mapping using multi-resolution multispectral and hyperspectral images

Vegetation is crucial for wetland ecosystems.Human activities and climate changes are increasingly threatening wetland ecosystems.Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands.This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images,combining super-resolution techniques and a novel self-constructing graph attention neural network(SGA-Net)algorithm.The SGA-Net algorithm includes a decoding layer(SCE-Net)to preciselyfine marsh vegetation classification in Honghe National Nature Reserve,Northeast China.The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network(SRCNN)obtained higher accuracy with a peak signal-to-noise ratio(PSNR)of 28.87 and structural similarity(SSIM)of 0.76 in spatial quality and root mean squared error(RMSE)of 0.11 and R^(2) of 0.63 in spectral quality.The improvement of classification accuracy(MIoU)by enhanced super-resolution generative adversarial network(ESRGAN)(6.19%)was greater than that of SRCNN(4.33%)and super-resolution generative adversarial network(SRGAN)(3.64%).In most classification schemes,the SGA-Net outperformed DeepLabV3+and SegFormer algorithms for marsh vegetation and achieved the highest F1-score(78.47%).This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping.

Multi-domain High-Resolution Platform for Integrated Spectroscopy and Microscopy Characterizations

In recent decades,materials science has experienced rapid development and posed increasingly high requirements for the characterizations of structures,properties,and performances.Herein,we report on our recent establishment of a multi-domain(energy,space,time)highresolution platform for integrated spectroscopy and microscopy characterizations,offering an unprecedented way to analyze materials in terms of spectral(energy)and spatial mapping as well as temporal evolution.We present several proof-of-principle results collected on this platform,including in-situ Raman imaging(high-resolution Raman,polarization Raman,low-wavenumber Raman),time-resolved photoluminescence imaging,and photoelectrical performance imaging.It can be envisioned that our newly established platform would be very powerful and effective in the multi-domain high-resolution characterizations of various materials of photoelectrochemical importance in the near future.

Super-resolution processing of passive millimeter-wave images based on conjugate-gradient algorithm

This paper designs a 3 mm radiometer and validate with experiments based on the principle of passive millimeter wave （PMMW） imaging. The poor spatial resolution, which is limited by antenna size, should be improved by post data processing. A conjugate-gradient （CG） algorithm is adopted to circumvent this drawback. Simulation and real data collected in laboratory environment are given, and the results show that the CG algorithm improves the spatial resolution and convergent rate. Further, it can reduce the ringing effects which are caused by regularizing the image restoration. Thus, the CG algorithm is easily implemented for PMMW imaging.

Super-resolution processing of passive millimeter-wave images based on adaptive projected Landweber algorithm

Passive millimeter wave （PMMW） images inherently have the problem of poor resolution owing to limited aperture dimension. Thus, efficient post-processing is necessary to achieve resolution improvement. An adaptive projected Landweber （APL） super-resolution algorithm using a spectral correction procedure, which attempts to combine the strong points of all of the projected Landweber （PL） iteration and the adaptive relaxation parameter adjustment and the spectral correction method, is proposed. In the algorithm, the PL iterations are implemented as the main image restoration scheme and a spectral correction method is included in which the calculated spectrum within the passband is replaced by the known low frequency component. Then, the algorithm updates the relaxation parameter adaptively at each iteration. A qualitative evaluation of this algorithm is performed with simulated data as well as actual radiometer image captured by 91.5 GHz mechanically scanned radiometer. From experiments, it is found that the super-resolution algorithm obtains better results and enhances the resolution and has lower mean square error （MSE）. These constraints and adaptive character and spectral correction procedures speed up the convergence of the Landweber algorithm and reduce the ringing effects that are caused by regularizing the image restoration problem.

Multiresolution Fusion of Remote Sensing Images Based on Resolution Degradation Model

A new method based on resolution degradation model is proposed to improve both spatial and spectral quality of the synthetic images. Some ETM+ panchromatic and multispectral images are used to assess the new method. Its spatial and spectral effects are evaluated by qualitative and quantitative measures and the results are compared with those of IHS, PCA, Brovey, OWT(Orthogonal Wavelet Transform) and RWT(Redundant Wavelet Transform). The results show that the new method can keep almost the same spatial resolution as the panchromatic images, and the spectral effect of the new method is as good as those of wavelet-based methods.

感应读出延时线阳极光子计数探测器研究

针对空间天文、生物荧光、光谱测量等领域对高灵敏、大面阵探测器的应用需求,研制出基于感应读出方式的延迟线阳极光子计数成像探测器。该探测器由微通道板、延时线阳极以及读出电路组成,其中延时线阳极的性能直接影响探测器的成像质量。作为一种电荷感应读出延时线阳极,该阳极利用信号到达的时间差解码入射光子的位置,可获得高的探测灵敏度和大的成像面积,同时简化了工艺难度并提升了可靠性。设计了感应读出延时线阳极,理论分析了探测器不同设计参数及材料对感应电荷量的影响,提出了解决异层电极间感应电荷量难以平衡的方法。据此研制出了收集面积为40 mm×40 mm的位敏阳极。测试结果表明该阳极信号传输衰减小于10%,极间串扰小于3%。利用该阳极进行了光子计数成像实验,获得了优于150μm的空间分辨率,为研制可应用于天文紫外光谱测量的大面阵、高灵敏探测器提供了理论依据及实验指导。

考虑点扩散函数效应的光谱遥感图像超分辨率制图

由于硬件设备的限制和土地覆盖类别的多样性,采集得到的光谱遥感图像的空间分辨率有时较为粗糙,导致大量混合像元产生,严重影响了土地覆盖类型空间分布的制图精度。超分辨率制图技术可以有效地处理光谱遥感图像中的混合像元,获得准确的地物类别分布信息。在遥感大数据背景下,来自同一卫星采集同一区域的多位移图像可以作为辅助数据改进超分辨率制图结果。然而,目前多位移图像插值超分辨率制图方法很少有效地考虑点扩散函数效应影响,导致制图结果精度降低。为了解决这一问题,本文提出了一种考虑点扩散函数效应的多位移光谱遥感图像超分辨率制图方法,改善土地覆盖类别制图结果。在所提出的方法中,首先,对多位移图像进行光谱解混,以生成粗糙丰度图像。然后,在考虑点扩散函数效应的情况下,对粗糙丰度图像先采用面到点克里格法,然后进行理想方波滤波,得到改善的粗糙丰度图像。接下来,通过插值对改善的粗糙丰度图像进行上采样以获得上采样丰度图像,并对上采样的所有丰度图像进行整合以获得精细丰度图像。最后,根据精细丰度图像提供的类别比例信息,使用类别分配方法将类别标签分配给亚像元,以获得理想的制图结果。实验结果表明,通过减少点扩散函数效应影响,所提出的方法展示出最佳性能表现,例如在美国华盛顿特区数据集实验结果的总体精度分别达到77.63%和Kappa系数达到0.7263。

联合图像—频率监督的图像超分辨率重建算法

针对现有单幅图像超分重建方法难以捕获图像中完备有效信息的问题,提出一种联合图像—频率监督的图像超分辨率重建算法,旨在利用多个域之间的互补信息,进而获得更完备的图像特征表示。首先通过分析图像像素转换到频域空间后的特性,根据其复数表征方式提出了一种新的频域距离监督损失,将频谱信息有效地应用到卷积神经网络的优化过程;然后通过分析频域中不同频带的表征特点,在频域距离损失基础上构建了频谱加权损失,并将其分别应用到低频和高频两个频带;最后结合图像域的监督,构成多个域的联合优化,取得良好的性能。在Set14、B100和Kodak三种公开数据集上进行了验证,结果表明:该算法的PSNR和SSIM分别达到了33.47 dB和0.9859,与几种图像超分方法相比取得了最好的性能。

面向多光谱卫星成像的广义光谱超分辨率

针对多种多光谱卫星成像模式,对原始光谱超分辨率概念进行扩充,提出一种联合数据驱动与模型驱动的深度学习算法。模拟构建的多个数据集,讨论了不同多光谱成像模式下的光谱超分辨率之间的差异,验证了所构建算法的稳健性,提高了现有多光谱卫星影像利用率,对光谱超分辨率的更广泛情形进行了概括。

基于随机森林方法的卫星高光谱影像道路提取与分析

高分辨率卫星高光谱影像在道路精细分类与提取方面具有广阔的前景,但目前星载高光谱道路提取所需谱段、光谱分辨率等核心指标尚不明确。文章在构造高光谱影像道路提取的随机森林方法的基础上,利用模拟数据和实验,比较了可见光近红外通道和短波红外通道,以及不同光谱分辨率条件下道路提取的精度。结果表明:利用可见光近红外或短波红外数据均能很好地实现机场跑道、水泥路、沥青道路、土路的准确分类,分类精度随光谱分辨率减小而提高。其中,高光谱影像在提取机场道路和水泥路方面精度高、优势明显。文章相关结论可为高分辨率高光谱卫星总体设计和道路提取处理算法提供参考。

碳卫星超光谱探测仪地面光谱定标

碳卫星超光谱探测仪聚焦于陆地生态系统植被碳汇和森林蓄积量探测,利用670~780 nm谱段的光谱绘制植被荧光的时空分布规律,满足全球碳汇定量监测、森林植被生产力评估的需求。如何有效地标定超光谱探测仪的光谱参数,建立探测仪和被测光谱信息的对应关系是定量化反演的基础。通过光栅方程推导了超光谱探测仪的光谱数据误差模型,并结合光学系统的弥散斑分布函数,卷积得到了超光谱探测仪的仪器线形函数(ILS)分布规律。仿真结果表明,仪器线形函数是缓慢变化的,在一个小光谱范围内ILS可以近似认为是一致的;波长误差是一个系统误差,主要由光栅制造误差等引起,采用已知波长特征谱线标定的方法可以消除。通过真空罐模拟在轨环境,建立了包含可调谐激光器、波长计、旋转散射片、积分球和平行光管等装置的光谱定标系统,提供线宽小于0.001 nm均匀分布的单色标准光源,利用自动化数据处理系统测试探测仪响应曲线和单色标准光源的对应关系,标定超光谱探测仪的光谱参数。超光谱探测仪光谱采样率2.5像元左右,单波长光谱的有效数据点少,无法给出ILS函数的精确数据,以0.015 nm波长间隔单波长扫描的新方法将光谱采样密度提高2个数量级,高斯拟合获取光谱分辨率,数据处理结果表明超光谱探测仪光谱分辨率为0.24~0.26 nm。通过选取特征波长和三次多项式拟合的方法得到波长定标方程,给出了全部像元的光谱定标数据,选取特征波长验证拟合波长残差,结果表明定标精度优于0.005 nm。为了进一步验证光谱定标结果,开展了超光谱探测仪的地面推扫成像实验,利用中国科学院空天院怀来试验站的测试平台,获取了松树林和石子路面的光谱数据,超光谱探测仪测量的大气吸收线和HITRAN模拟的大气吸收线比对结果表明,氧吸收线中心波长偏差小于0.003 nm,证明超光谱探测仪光谱定标精度满足指标要求。

高空间分辨率高可见度的太赫兹光谱成像研究

太赫兹光谱成像,不但包括在二维图像空间的强度信息,同时可以得到太赫兹波段的光谱信息,构成了一个三维的数据矩阵。由于受到太赫兹成像系统内部硬件的限制和影响,太赫兹频域较高频段处信号存在能量弱、信噪比低的特点,导致所成的太赫兹图像普遍存在分辨率低、对比度低等问题。因此,利用三维数据矩阵,应用适合的算法,实现了提高太赫兹光谱成像空间分辨率、边缘细节可见度的目的。搭建了三维可移动式太赫兹时域光谱成像系统,实现了对标准高分辨率板的二维扫描。对该系统所采集到的信号分别进行时域、频域等多种方式成像对比,结合瑞利判据和分辨率标尺对成像系统的空间分辨率、景深进行标定,研究了提高太赫兹光谱成像的空间分辨率算法。然后,针对太赫兹频域高频区域信噪比低、对比度低、噪声原因复杂的特点,结合深度残差学习的图像去噪理论,提出了太赫兹图像深度去噪网络,在训练集中引入成像系统中真实的“太赫兹残差噪声”。最后,利用所训练出的模型对太赫兹频域高频区域图像进行盲去噪,并用重建图像分别与原始成像结果和传统太赫兹去噪算法结果进行比较,分别从主观和客观两个方面评价了不同算法对太赫兹频域高频图像的去噪效果。实验结果表明,通过该算法实现了极限空间分辨率约为157μm,去噪后图像极限空间分辨率处的瑞利判据鞍-峰比约为0.623,图像整体对比度为46.635;空间分辨率相比传统成像方法提高了约一倍,对比度提高约26%。研究结果为高空间分辨率高可见度的太赫兹光谱成像方式提供了一种新的规范,并针对太赫兹频域较高频区域的图像噪声问题提供了一种新的解决方案。

基于多尺度空谱特征提取的高光谱遥感图像重建

遥感高光谱图像(HSI)凭借其丰富的光谱信息被广泛地应用于军事、农业、地质等领域。然而,HSI成像因其成本高、传输难等问题限制了其应用规模。通过光谱超分辨(SSR)方法,可以实现输入高分辨率红绿蓝(RGB)图像生成相应的高光谱图像,有效地解决了HSI成本高与传输难的问题。但是,已有的SSR方法对于图像的空间特征提取与光谱之间的相关性关注仍有所不足。针对上述问题,本文提出了一种多尺度空谱特征提取方法,并在SSR领域引入组归一化(GN)方法,关注相邻光谱之间的相关性。同时,还借助条件生成对抗网络进一步优化网络的参数,提升生成图像的真实性,并在DFC2018 Houston数据集上进行实验。结果表明:本文提出的方法可以实现精准的高光谱图像重建,重建的高光谱图像相比当前先进的主流方法性能均明显提高,其中相比CanNet方法,均方根误差下降了48.3%,平均峰值信噪比上升了20.7%。

X光弯晶成像金属薄膜面密度测量技术

针对靶用高Z金属薄膜的无损检测需求,提出了一种通过超环面弯晶聚焦型X光单能成像器件,实现金属薄膜均匀性及面密度等参数精确标定的测量技术。该技术即通过高通量、高单能性成像,定量获取薄膜X光透过率及其空间分布,有效提升了面密度测量的精度,同时实现了对其均匀性的高空间分辨评估。从总体方案设计、元器件制备和测试实验等方面开展了深入研究,并评估了各种可能因素对测量不确定度的影响。所发展的超环面弯晶成像系统针对20 keV级的高能X射线在mm尺度内实现了优于5μm的微区分辨,能谱分辨达到几eV。通过泡沫金样品面密度测量实验证明了技术可行性,相对不确定度优于2%。研究结果为激光惯性约束聚变高Z靶材料的精密无损检测提供了一种新的测量技术,并有望应用于其他需要大视场、高空谱分辨成像的需求领域。

基于不规则尺度区域光谱信息的高光谱图像亚像元定位

亚像元定位技术可以分析混合像元,并实现从丰度图像到亚像元级精细土地覆盖定位图像的转换。然而,传统的亚像元定位方法所使用的光谱信息通常在指定的矩形局部窗口中构造,并且很少使用所有波段的光谱信息,影响了亚像元定位的性能。为了解决这一问题,本文提出了一种基于不规则尺度区域光谱信息的高光谱图像亚像元定位方法(SIISA)。在三幅遥感图像上的实验结果表明,所提出的SIISA优于现有的亚像元定位方法。

Luojia-HSSR:A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.

感兴趣未知红外波段遥感图像超分辨率反演重构

提出了感兴趣未知波段遥感图像超分辨率反演重构算法。基于已有波段的多光谱图像,根据构建的地物光谱反射率数据库,利用高斯混合聚类算法和相关距离法,对辐射定标和大气校正后的场景光谱反射率图像的物质进行分类与类别判定。基于流形空间约束的图像解混算法得到场景图像的地物分布,据此,反演重构感兴趣未知波段地物光谱反射率图像。基于已有波段的远红外图像,利用温度反演算法与插值算法,反演重构感兴趣未知波段高分辨率地物温度场分布。根据地物光谱反射率信息和地物温度场信息,利用遥感链路成像模型,超分辨率反演重构出感兴趣未知红外波段高分辨率遥感图像。实验结果表明,本文算法能够超分辨率反演重构出感兴趣未知红外波段不同场景的高分辨率遥感图像,重构图像与场景实况图像的相似度很高,提升图像的目标检测识别能力。