基于小波神经网络的红外光谱发射率计算方法

红外光谱发射率是战机蒙皮重要的红外隐身参数之一,为消除大气等外界因素对红外辐射特性测试结果的干扰,建立一种基于小波神经网络的目标红外辐射亮度模型,利用这种模型对测试样本进行网络训练,建立3~5 μm 和8~12 μm 波段红外辐射亮度模型,进而计算不同波长下目标的光谱发射率。通过与标准黑体对比,验证所建小波神经网络模型的光谱发射率与黑体实际的光谱发射率相比,最大相对误差约为2%,并将该方法应用于飞机蒙皮的光谱发射率计算。     

一种改进的可见-红外多光谱辐射测温反演算法

在基于参考温度的二次测量法数学模型的基础上,提出了无需假设光谱发射率模型的可见-红外多光谱辐射测温迭代递推算法,并对4种发射率假设模型进行了仿真计算。结果表明:新算法的温度绝对误差小于20 K,发射率趋势也与假设模型的发射率趋势吻合较好。进一步完善了可见-红外多光谱辐射测温理论。     

双向反射分布函数结合Bi-LSTM网络求解壁面发射率

壁面光谱发射率求解是飞行器红外隐身的关键技术之一。首先设计了壁面反射光路和光源,通过光谱辐射计获取壁面反射的辐射亮度序列,为尽可能地消除外界干扰对于光谱发射率求解精度的影响,基于双向长短期记忆网络,设计了Bi-LSTM亮度回归网络模型,并对测试样本进行训练学习。基于BRDF的壁面发射率求解模型及基于Bi-LSTM网络的亮度回归模型求解壁面的发射率。计算结果显示,提出的基于双向反射分布函数的壁面发射率求解方法的相对误差为12.21%,满足工程测试需求。     

脉冲加热材料热物性多光谱动态测量装置的研制

提出一种将多光谱测温法和积分球反射法相结合的脉冲加热技术测量材料热物性的新方法。基于积分球反射法,利用多光谱测温法获得材料在多个光谱下的辐射信息，可同时获得试样在多个光谱下的法向光谱发射率。介绍了利用该方法的脉冲加热瞬态热物性测量装置的各组成部分及其工作原理，该装置能 同时测量带状试样的比热、电阻率、全波长半球发射率及多个光谱下的法向光谱发射率。     

机载热红外多光谱数据定标与温度反演

设计了热红外航空遥感实验,对获取的热红外图像进行了定标,用发射率归一化方法反演了地物的温度和发射率光谱,探讨了利用地物热红外光谱识别地物的可行性.结果表明,基于热红外多光谱数据的发射率归一化方法可以有效地反演地物温度和发射率光谱,所反演的发射率光谱可以比较有效地应用于地物的识别,尤其是对于土壤不同性状的探测识别可以取得良好的结果.     

单目标极小值优化法的多波长真温反演研究

在多波长温度测量系统中,光谱发射率假设为波长或温度模型来实现真温的求解。由于模型假设存在一定的不确定性,光谱发射率模型与实际光谱发射率的变化规律可能不符,会造成较大的真温反演误差。另外,光谱发射率与波长或温度之间的函数关系通用性较差,尤其是待测辐射体发生改变时,这种关系自然也就不复存在了。为了获得正确的真温,首次将优化思想引入到方法中,建立了单目标极小值优化法（Single Objective Minimization Optimization Method,SMO）完成真温反演。新方法无需建立光谱发射率和波长或温度之间的模型,降低了真温求解方法的复杂性和技术难度。与原有的光谱发射率与温度之间的模型（也称二次测量法,Second Measurement Method,SMM法）相比,在相同的初始条件下,新方法与二次测量法相比,反演速度提高了95%以上。     

航空热红外多光谱数据的地物发射率谱信息提取模型及其应用研究

基于１３２种地物的实验室光谱曲线,建立适合于模块化机载成像光谱仪（ＭＡＩＳ）热红外多波段的发射率经验关系,以此修正发射率反演方法（ＴＥＳ）中的ＭＭＤ模型。在此基础上选用１９９７年８月在法国ＡＶＩＧＮＯＮ农业区获取的一幅ＭＡＩＳ热红外多光谱图像,在利用ＬＯＷＴＲＡＮ７实现大气初纠正的前提下,应用修正后的ＴＥＳ算法提取发射率谱信息。结果表明：反演所得发射率与实验室光谱在形状上具有很好的一致性,利用发射率谱结合地表亮温信息进行植被分类是可能的。     

地物目标热红外光谱发射率的野外测量方法研究

在航天红外遥感应用中,地物目标光谱发射率是卫星遥感测量地面温度的一个重要参数。野外测量的大气环境、目标背景和地物的热力学特性等因素的影响,使得野外测量地物目标表面光谱发射率变得较为复杂。重点讨论了利用傅里叶变换红外光谱仪野外测量地物目标光谱发射率的方法和程序,介绍了几种正确分离目标温度与发射率的方法。野外测量实验结果表明,按照文中所述的测量方法,测量得到的地物热红外光谱发射率具有良好的一致性,发射率测量误差小于0．02。     

地物目标热红外光谱发射率的野外测量方法研究

在航天红外遥感应用中,地物目标光谱发射率是卫星遥感测量地面温度的一个重要参数。野外测量的大气环境、目标背景和地物的热力学特性等因素的影响,使得野外测量地物目标表面光谱发射率变得较为复杂。重点讨论了利用傅里叶变换红外光谱仪野外测量地物目标光谱发射率的方法和程序,介绍了几种正确分离目标温度与发射率的方法。野外测量实验结果表明,按照文中所述的测量方法,测量得到的地物热红外光谱发射率具有良好的一致性,发射率测量误差小于0．02。     

多光谱辐射测温的理论研究

在多光谱辐射温度计的数据处理中的常假设发射率的对数与波长成线性关系，本文提出卫种自动判别发射率和波长数学模型的新方法，着重分析了应用此方法时各种测量误差对真温及发射率的影响，计算机仿真结果与理想假设值符合得很好，是一种较好的获知真温及发射率的方法。     

基于采样二通道不可分小波的多光谱图像融合

针对基于非下采样不可分小波图像融合方法空间分辨率不高、基于张量积小波融合方法会出现方块效应的不足,提出了一种基于伸缩矩阵为[1,1;1,-1]的二通道采样不可分小波的多光谱图像和全色图像融合方法.利用矩阵扩充方法,构造了一组新的不可分低通滤波器和高通滤波器组,利用所设计滤波器组分别对多光谱图像的亮度分量和全色图像作下采样的多尺度不可分小波分解,分别对分解后的低频子图像和高频子图像按不同的融合规则进行融合.实验结果表明,其保持光谱信息的能力和保持空间分辨率信息的能力比基于IHS变换融合方法、基于DWT的融合方法、基于IHS-DWT的融合方法、基于IHS-Contourlet变换的融合方法、基于IHS-Curvelet变换的融合方法、SRF方法都强,与基于非下采样的二通道不可分正交小波和不可分双正交小波融合方法相比,该方法能保持较好的整体光谱信息和较高的空间分辨率信息.     

An optimization algorithm for separating land surface temperatureand emissivity from multispectral thermal infrared imagery

Land surface temperature (LST) and emissivity are important components of land surface modeling and applications. The only practical means of obtaining LST at spatial and temporal resolutions appropriate for most modeling applications is through remote sensing. While the popular split-window method has been widely used to estimate LST, it requires known emissivity values. Multispectral thermal infrared imagery provides us with an excellent opportunity to estimate both LST and emissivity simultaneously, but the difficulty is that a single multispectral thermal measurement with N bands presents N equations in N+1 unknowns (N spectral emissivities and LST). In this study, we developed a general algorithm that can separate land surface emissivity and LST from any multispectral thermal imagery, such as moderate-resolution imaging spectroradiometer (MODIS) and advanced spaceborne thermal emission and reflection radiometer (ASTER) data. The central idea was to establish empirical constraints, and regularization methods were used to estimate both emissivity and LST through an optimization algorithm. It allows us to incorporate any prior knowledge in a formal way, The numerical experiments showed that this algorithm is very effective (more than 43.4% inversion results differed from the actual LST within 0.5°, 70.2% within 1° and 84% within 1.5°), although improvements are still needed     

Compression of multispectral images by three-dimensional SPIHTalgorithm

The authors carry out low bit-rate compression of multispectral images by means of the Said and Pearlman's SPIHT algorithm, suitably modified to take into account the interband dependencies. Two techniques are proposed: in the first, a three-dimensional (3D) transform is taken (wavelet in the spatial domain, Karhunen-Loeve in the spectral domain) and a simple 3D SPIHT is used; in the second, after taking a spatial wavelet transform, spectral vectors of pixels are vector quantized and a gain-driven SPIHT is used. Numerous experiments on two sample multispectral images show very good performance for both algorithms     

Separating temperature and emissivity in thermal infraredmultispectral scanner data: implications for recovering land surfacetemperatures

The accuracy of three techniques for recovering surface kinetic temperature from multispectral thermal infrared data acquired over land is evaluated. The three techniques are the reference channel method, the emissivity normalization method, and the alpha emissivity method. The methods used to recover the temperature of artificial radiance derived from a wide variety of materials. The results indicate that the emissivity normalization and alpha emissivity techniques are the most accurate, and recover the temperature of the majority of the artificial radiance spectra to within 1.5 K; the reference channel method produces less accurate results. The primary advantage of the alpha emissivity method over the emissivity normalization method is that it works well in terrains of widely varying emissivities, e.g.,those dominated by vegetation and igneous rocks. By contrast, the emissivity normalization method works well only if the emissivity used for normalization is close to the maximum emissivity of the spectra in the scene     

基于非下采样三通道不可分对称小波的多光谱图像融合

针对IHS变换融合方法不能保持好的光谱信息,张量积小波变换融合方法生成的融合结果图像空间分辨率偏低、且易产生方块效应等不足.提出了一种基于三通道不可分对称小波的多光谱图像融合方法.利用矩阵扩充的方法,给出了三通道不可分对称小波滤波器组的构造方法,用所构造的不可分小波滤波器组分别对多光谱图像的亮度分量和全色图像作非下采样...     

热红外发射率光谱的野外测量方法与土壤热红外发射率特性研究

基于光谱平滑的温度／发射率迭代算法，提出热红外发射率光谱的野外测量与反演方法；分析了不同组分、粒径及含水量土壤的热红外发射率变化规律。结果表明，在8～9．5μm波长范围内土壤的发射率随SiO2含量的增加而降低，随含H2O量的增加而增大；在11～13μm波长范围内土壤的发射率基本保持不变，基于此分析结果，提出利用热红外光谱数据反演土壤含沙量和含水量的方法。     

基于离散向量小波变换的多光谱图像融合

为了能够更好地把多光谱图像和高分辨率图像的图像信息综合起来,以提高对图像信息的分析和提取能力,在研究了离散向量小波图像分析法之后,本文提出了一种基于离散向量小波变换的像素级图像融合新方法。向量小波作为标量小波的一种扩展具有很多标量小波所没有的优点。向量小波分析能够为图像提供一种比标量小波多分辨分析更加精细的分析方法。利用向量小波将多光谱图像和高分辨率图像进行融合后得到的融合图像,能够很好地将原图像的细节融合在一起。给出了该方法的融合结果,通过与其它融合方法进行主观与客观的评价比较,证明该融合方法可以获得更好的融合效果。     

Multispectral code excited linear prediction coding and itsapplication in magnetic resonance images

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.     

Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition

Since Chavez proposed the highpass filtering procedure to fuse multispectral and panchromatic images, several fusion methods have been developed based on the same principle: to extract from the panchromatic image spatial detail information to later inject it into the multispectral one. In this paper, we present new fusion alternatives based on the same concept, using the multiresolution wavelet decomposition to execute the detail extraction phase and the intensity-hue-saturation (IHS) and principal component analysis (PCA) procedures to inject the spatial detail of the panchromatic image into the multispectral one. The multiresolution wavelet decomposition has been performed using both decimated and undecimated algorithms and the resulting merged images compared both spectral and spatially. These fusion methods, as well as standard IHS-, PCA-, and wavelet-based methods have been used to merge Systeme Pour l'Observation de la Terre (SPOT) 4 XI and SPOT 4 M images with a ratio 4:1. We have estimated the validity of each fusion method by analyzing, visually and quantitatively, the quality of the resulting fused images. The methodological approaches proposed in this paper result in merged images with improved quality with respect to those obtained by standard IHS, PCA, and standard wavelet-based fusion methods. For both proposed fusion methods, better results are obtained when an undecimated algorithm is used to perform the multiresolution wavelet decomposition.