像元分解技术订正比辐射率的热红外遥感地热温度反演及IDL实现

陆地卫星TM数据(TM6)热波段表示地表热辐射和地表温度变化.根据热辐射传输模型,详细介绍了地表温度的反演过程.在实际应用中,地表不同物体的比辐射率获取比较复杂,通常破忽略,本文利用像元线性分解技术,计算各个像元的物质组分,通过单个物质的比辐射率,得到反演温度所需的各像元比辐射串参数.提高了地表温度演算精度,克服了以往地物比射率变化对温度的影响.通过与实测温度对比,误筹小于1K.研究还表明地下热水富集带的地表温度比地表水高5-6K左右,热红外波段可以有效探测地表温度异常的变化.本研究的算法均通过IDL实现.     

基于组合不同裂窗算法反演黄河流域地表温度研究

以黄河流域为研究区域,利用MODIS遥感数据,根据下垫面的水分状况和土地覆盖类型对整个流域分别进行分区.在整个流域不分区、水分状况分区和土地覆盖分区双因子分区2种情形下,对比了7种常用的地表温度遥感反演裂窗算法的结果,在分析每种反演算法适用性的基础上,针对不同分区单元分别选择效果最好的算法进行组合来进行流域地表温度的反演.反演结果和MODIS的NASA的温度产品相比,效果更为理想.     

基于卷积神经网络和AMSR2微波遥感的土壤水分反演研究

土壤水分是水文、气象、农业等研究中的重要参数,尤其在农作物估产模型和农业干旱监测研究中有特别重要的意义。前人在土壤水分反演算法研究上已经做了大量研究工作,但由于影响地表土壤水分的因素较多,各种算法依然存在一些不足。本研究在分析以往反演算法的基础上,总结不同算法的优势和局限性,提出利用具有深度学习特点的卷积神经网络方法(CNN)进行土壤水分反演,从而克服传统土壤水分反演方法的缺陷并提高土壤水分反演的精度。以被动微波AMSR2数据为例,在对土壤水分产品算法进行剖析的基础上,构建了基于卷积神经网络反演土壤水分的模型框架,从而提高反演算法的通用性和反演精度。深度学习卷积神经网络的精度主要取决于训练和测试样本数据库,然而被动微波像元分辨率比较低,以及很难获得与卫星同步的地面实测数据。本文选择不同地区地表均一的土壤水分观测站点,以AMSR2土壤水分产品作为参照来获取地面同步数据,从而克服地面同步观测数据的难题。将AMSR2亮温数据和对应地面同步观测土壤水分数据为样本随机分成训练和测试数据库,通过反复训练,当卷积神经网络选择两个卷积层两池化层的组合,迭代次数3000次时,网络反演的总体精度最高。经过测试样本验证表明,CNN反演土壤水分值与地面同步观测土壤水分的均方根误差RMSE为1.1178%,两者保持了较高相关性(r=0.8685)。以地面站点实测数据对CNN反演结果进行验证,相对误差为39.25%。相比于JAXA产品与实测值的验证结果,CNN反演结果的平均相对误差小10.24%,说明CNN明显提高被动微波遥感土壤水分反演的精度。     

辽宁地区陆面实测温度与MODIS反演产品对比分析

陆面温度（LST）是区域和全球尺度地表物理过程中的一个关键因子,亦是研究地表和大气之间物质交换和能量交换的重要参数。因精确反演LST具有一定的难度,因此反演LST成为当今研究的热点之一。气象卫星为快速获取大范围辐射面瞬时温度提供了重要手段,本文基于MODIS数据2、19波段反演水汽含量,31、32波段和推广的分裂窗算法反演辽宁地区陆面温度。通过对辽宁地区地而观测站实测温度的对比,表明反演精度与实测数据相差不大,具有实际参考价值。     

利用AMSR-E资料反演实时海面气象参数的个例

利用2005年1月的AMSR-E卫星资料作为一个例子,探讨了AMSR-E的12个通道的亮温与海表温度、海面气温、湿度及风速4个气象参数的关系,把亮温通道分3大类组合分别进行参数模拟,确定模拟这4个气象参数最合理的通道组合,并利用多参数回归方法建立海表温度、海面气温、湿度及风速与亮温之间的经验关系.反演结果与TAO浮标实测资料进行了比较,实时海表温度、海面气温、湿度及风速的均方根差分别为0.53℃、0.74℃、3.2%和1.1m/s.是一个利用卫星资料同时反演四个参数(海表温度、海面气温、湿度及风速)的成功例子,为计算海气热通量提供了数据,并为实时观测和研究气候变化提供了一种简单而有效的方法.     

利用MODIS影像反演沙漠化地区地表温度的方法研究

针对当前利用MODIS数据反演地表温度过于复杂和缺乏简便易行的实地验证方法的不足,尝试了改进反演方法的研究,提出了反演结果的地面准同步测量验证方法和在沙漠化地区分类计算地表比辐射率的方法,简化了原劈窗算法所需参数的计算过程。利用改进后的方法对新疆沙漠化地区的地表温度进行了反演,并同步进行了该区域在各种天气条件和地表条件下的地表温度反演结果的验证。经分析表明,在天空晴朗无云,无风,植被稀疏,地表类型均一,面积较大,沙质土壤且观测时刻与卫星过境时刻接近的条件下,该方法精度较高。研究表明改进的利用MODIS影像数据反演沙漠化地区地表温度方法和地面验证方法具有一定的可行性,适合于对沙化地区地表温度大范围且快速的遥感监测。     

基于AIEM和实地观测数据对GNSS-R反演土壤水分的研究

介绍了用GPS反射信号反演土壤水分的原理及反演研究的进展,并用改进的积分方程模型(AIEM)和实地观测数据对利用GPS反射信号反演土壤水分的方法进行了分析,分析结果表明,由于单个频率的雷达信号受地表粗糙度、角度和地表类型(裸地和植被类型)的影响比较大,很难提出一个实用的通用物理算法.利用美国2002年土壤水分实验(SMEX02)实测数据对上述的反演算法进行了分析,分析结果表明,采用经验统计算法对单个站点观测比较实用,平均相关系数达到0 85以上.整个分析表明,利用GPS前向散射信号与噪声之比反演土壤水分在单个站点能够取得比较高的精度.     

SZ-4飞船辐射模态沙漠地区数据模拟验证

对SZ-4飞船辐射模态获取的亮温数据,进行了模拟研究。辐射模态的工作频率范围为6．6～37GHz,对应的地面像元尺寸为6～32km。以新疆塔中地区为研究对象,通过分析被动微波遥感中裸露地表的辐射特性,基于裸露地表的辐射模型（AIEM模型）和地表的实测属性数据来模拟该地区的辐射亮温值,并将其与SZ-4辐射模态和AMSR-E实际的亮温观测值进行了比较,通过对正演结果的比较分析检验了SZ-4亮温数据的质量及其在沙漠地区反演地表参数的潜力。     

A mono-window algorithm for retrieving sea surface temperatures by using mid-infrared data

基于中红外波段受大气水汽影响小的特点,从大气辐射传输的角度,提出了利用中红外数据反演海表温度的单通道算法.该算法适用于单通道的中红外数据,时空变化适应性强,对大气温湿廓线等辅助信息的精度要求较低.为了验证该算法的可行性和准确性,将此算法反演的海表温度产品与美国国家数据浮标中心提供的浮标数据及MODISSST标准产品进行了比对验证.结果表明,此单通道算法反演的海表温度达到了较高的精度,能够满足海洋锋面、上升流、海洋涡旋等短周期海洋现象的观测需求.     

基于MODIS数据的去云处理实现方法

地表温度是监测地球资源环境动态变化的重要指标,精确定量反演陆面温度并分析温度变化趋势对旱灾预报、农作物产量估算、生态环境变化及区域规划等人们的生产生活方面具有重要研究意义。利用卫星遥感资料进行面状区域地表温度的同步快速获取已成为目前遥感定量研究中的重要任务之一。本文选取长三角05年全年46幅8天合成的地表温度产品数据MYD11A2,基于MODIS数据温度产品会涉及到云污染而导致数据缺失的问题,引入HANTS方法进行去云处理,为地表温度与下垫面关系时空分析奠定基础。     

Simultaneous retrieval of atmospheric profiles, land-surface temperature, and surface emissivity from Moderate-Resolution Imaging Spectroradiometer thermal infrared data: extension of a two-step physical algorithm

An extension to the two-step physical retrieval algorithm was developed. Combined clear-sky multitemporal and multispectral observations were used to retrieve the atmospheric temperature-humidity profile, land-surface temperature, and surface emissivities in the midwave (3-5 microns) and long-wave (8-14.5 microns) regions. The extended algorithm was tested with both simulated and real data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Airborne Simulator. A sensitivity study and error analysis demonstrate that retrieval performance is improved by the extended algorithm. The extended algorithm is relatively insensitive to the uncertainties simulated for the real observations. The extended algorithm was also applied to real MODIS daytime and nighttime observations and showed that it is capable of retrieving medium-scale atmospheric temperature water vapor and retrieving surface temperature emissivity with retrieval accuracy similar to that achieved by the Geostationary Operational Environmental Satellite (GOES) but at a spatial resolution higher than that of GOES.     

稀疏植被地区气溶胶光学厚度反演

研究了稀疏植被地区地表反射率的精确确定方法,构建了精确确定稀疏植被地区地表反射率的地面光谱模型,实现了该类地区气溶胶光学厚度的卫星数据反演.分析表明,所构模型的地表反射率确定误差在0.015以内.通过对气溶胶光学厚度反演的不确定性分析,确定稀疏植被地区的气溶胶光学厚度的卫星反演误差在0.15以内,并使用MODIS数据反演了北京市区及周围地区的气溶胶光学厚度.用太阳分光光度计测量的气溶胶光学厚度对MODIS数据的气溶胶光学厚度反演结果进行了验证.     

基于ENVISAT ASAR数据的鄱阳湖湿地生物量反演研究

湿生植被是鄱阳湖湿地生态系统的重要组成部分,生物量的大小是衡量湿地生态系统初级生产力的主要指标之一.本文利用ENVISAT ASAR交替极化(HH,VV)数据对鄱阳湖湿地地区的湿生植被进行生物量反演研究,并在密歇根微波冠层散射(MIMICS)模型模拟分析的基础上利用人工神经网络(ANN)方法来反演生物量.据此计算出鄱阳湖4月份湿生植被的总生物量干重约为1.065×109kg,并给出了生物量分布图.反演结果表明,ENVISAT ASAR数据可以很好地用于湿地植被生物量反演;神经网络生物量反演方法可以有效地表达生物量与后向散射系数之间复杂的非线性关系,从而大大提高反演精度;反演结果的误差主要来自于实地采样、图像配准、反演计算过程中带来的误差.     

A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data. They have to be dependent on cloud service providers for assurance of the platform’s security. Data security and privacy issues reduce the progression of cloud computing and add complexity. Nowadays; most of the data that is stored on cloud servers is in the form of images and photographs, which is a very confidential form of data that requires secured transmission. In this research work, a public key cryptosystem is being implemented to store, retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman (RSA) algorithm for the encryption and decryption of data. The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment. To enhance the user data security level, a neural network is used for user authentication and recognition. Moreover; the proposed technique develops the performance of detection as a loss function of the bounding box. The Faster Region-Based Convolutional Neural Network (Faster R-CNN) gets trained on images to identify authorized users with an accuracy of 99.9% on training.     

Cirrus cloud top temperatures retrieved from radiances in the National Polar-Orbiting Operational Environmental Satellite System--Visible Infrared Imager Radiometer Suite 8.55 and 12.0 microm bandpasses

We describe what is believed to be a new approach developed for the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) to retrieve pixel-level, cirrus cloud top temperatures (CTTs) from radiances observed in the 8.55 and 12.0 microm bandpasses. The methodology solves numerically a set of nonlinear algebraic equations derived from the theory of radiative transfer based upon the correlation between emissivities in these two bandpasses. This new approach has been demonstrated using NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) as a proxy to Visible Infrared Imager Radiometer Suite (VIIRS) data. Many scenes have been analyzed covering a wide range of geophysical conditions, including single-layered and multilayered cirrus cloud situations along with diverse backgrounds and seasons. For single-layer clouds, the new approach compares very favorably with the MODIS 5 km resolution cloud products; the mean CTT for both methods are very close, while the standard deviation for the new approach is smaller. However, in multilayered cloud situations, the mean CTTs for the new approach appear to be colder than the CTTs from MODIS cloud products, which are acknowledged to be too warm. Finally, partly because the new approach is applied at the pixel level, CTTs do not increase toward cloud edges as is seen in the MODIS products. Based upon these initial results, the new approach to retrieve improved VIIRS cloud top properties has been incorporated into the ground-based data processing segment of the NPOESS system where prelaunch testing of all VIIRS algorithms continues.     

Retrieval of geophysical parameters from moderate resolution imaging spectroradiometer thermal infrared data: evaluation of a two-step physical algorithm

A two-step physical algorithm that simultaneously retrieves geophysical parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements was developed. The retrieved geophysical parameters include atmospheric temperature-humidity profile, surface skin temperature, and two surface emissivities within the shortwave (3-5-mum) and the longwave (8-14.5-mum) regions. The physical retrieval is accomplished in two steps: (i) The Tikhonov regularization method is employed to generate a regularization solution along with an optimum regularization parameter; (ii) the nonlinear Newtonian iteration algorithm is carried out with the regularization solution as a first-guess profile to obtain a final maximum probability solution for geophysical parameters. The algorithm was tested with both simulated and real MODIS Airborne Simulator (MAS) data. Sensitivity studies on simulated MAS data demonstrate that simultaneous retrievals of land and atmospheric parameters improve the accuracy of the retrieved geophysical parameters. Finally, analysis and accuracy of retrievals from real MAS data are discussed.     

Application of NN technique for predicting the in-depth residual stresses during hard machining of AISI 52100 steel

In machining of parts, surface quality is one of the most impellent customer requirements. The most relevant issues are surface roughness and residual stresses. In particular, the latter are affected by tool geometry, material characteristics and process conditions. Residual stresses can have a significant effect on the service quality and the component life. Residual stresses can be determined by both empirical and numerical investigations for selected configurations, however, these are expensive procedures. This paper presents a hybrid model based on the artificial neural networks (ANNs) and finite element method (FEM) that can be used to predict the residual stress profile. A three layer neural network has been trained and tested on the data obtained by numerical investigations of hard machining of 52100 bearing steel. The numerical results are consistent with experimental data.     

Data mining algorithms for land cover change detection: a review

Land cover change detection has been a topic of active research in the remote sensing community. Due to enormous amount of data available from satellites, it has attracted the attention of data mining researchers to search a new direction for solution. The Terra Moderate Resolution Imaging Spectrometer (MODIS) vegetation index (EVI/NDVI) data products are used for land cover change detection. These data products are associated with various challenges such as seasonality of data, spatio-temporal correlation, missing values, poor quality measurement, high resolution and high dimensional data. The land cover change detection has often been performed by comparing two or more satellite snapshot images acquired on different dates. The image comparison techniques have a number of limitations. The data mining technique addresses many challenges such as missing value and poor quality measurements present in the data set, by performing the pre-processing of data. Furthermore, the data mining approaches are capable of handling large data sets and also use some of the inherent characteristics of spatio-temporal data; hence, they can be applied to increasingly immense data set. This paper stretches in detail various data mining algorithms for land cover change detection and each algorithm’s advantages and limitations. Also, an empirical study of some existing land cover change detection algorithms and results have been presented in this paper.     

Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool

In this study, the influence of hardness (H) and spindle speed (N) on surface roughness (Ra) in hard turning operation of AISI 4140 using CBN cutting tool has been studied. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental values and to show the effect of hardness and spindle speed on the surface roughness. Artificial neural network (ANN) and regression methods have been used for modelling of surface roughness in hard turning operation of AISI 4140 using CBN cutting tool. The input parameters are selected to be as hardness and spindle speed and the output is the surface roughness. Regression and artificial neural network optimum models have been presented for predicting surface roughness. The predicted surface roughness by the employed models has been compared with the experimental data which shows the preference of ANN in prediction of surface roughness during hard turning operation. Finally, a reverse ANN model is constructed to estimate the hardness and spindle speed from surface roughness values. The results indicate that the reverse ANN model can predict hardness for the train data and spindle speed for the test data with a good accuracy but the predicted spindle speed for the train data and the predicted hardness for the test data don’t have acceptable accuracy.