Spectral matching based remote sensing identification of two main crop rotation patterns in a large irrigation district

The rapid identification of planting patterns for major crops in a large irrigated district has vital importance for irrigation management,water fee collection,and crop yield estimation.In this study,the OTSU algorithm and Mean-Shift algorithm were employed to automatically determine threshold values for mapping two main rotated crop patterns at the pixel scale.A time series analysis was conducted to extract the spatial distribution of rice-wheat and wheat-maize rotations in the Chuanhang irrigation district from 2016 to 2020.The results demonstrate that both threshold segmentation algorithms are reliable in extracting the spatial distribution of the crops,with an overall accuracy exceeding 80%.Additionally,both Kappa coefficients surpass 0.7,indicating better performance by OTSU method.Over the period from 2016 to 2020,the area occupied by rice-wheat rotation cropping ranged from 12500 to 14400 hm 2;whereas wheat-maize rotation cropping exhibited smaller and more variable areas ranging from 19730 to 34070 hm 2.These findings highlight how remote sensing-based approaches can provide reliable support for rapidly and accurately identifying the spatial distribution of main crop rotation patterns within a large irrigation district.

POINT PATTERN MATCHING ALGORITHM BASED ON POINT PAIR TOPOLOGICAL CHARACTERISTICS AND SPECTRAL MATCHING

Most of the Point Pattern Matching (PPM) algorithm performs poorly when the noise of the point's position and outliers exist. This paper presents a novel and robust PPM algorithm which combined Point Pair Topological Characteristics (PPTC) and Spectral Matching (SM) together to solve the afore mentioned issues. In which PPTC, a new shape descriptor, is firstly proposed. A new comparability measurement based on PPTC is defined as the matching probability. Finally, the correct matching results are achieved by the spectral matching method. The synthetic data experiments show its robustness by comparing with the other state-of-art algorithms and the real world data experiments show its effectiveness.

Fast identification of mural pigments at Mogao Grottoes using a LIBS-based spectral matching algorithm

To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment.A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples,and this database was successfully used to identify unknown pigments on tablet,simulated,and real mural debris samples.The results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.

Spectral Match Optimization Based on Grey Relational Analysis

A spectral match optimization based on grey incidence matrix was put forward to evaluate camouflage painting design.A synthetic degree of incidence (SDI) was defined to comprehensively reflect the spectrum similarity based on theory of grey incidence analysis.A grey optimization model for camouflage painting sheme was constructed on the basis of SDI and grey incidence matrix.Its weight values were determined according to area percentages of all components in the camouflage scene,and a quantitative ordering for various schemes could be obtained according to the evaluation coefficients.Experiment results show that the method mentioned in this paper can provide a quantitative basis for the camouflage decision-making,and it can also be used in other camouflage scheme selection.

Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(e.g.seasonality).Furthermore,croplands are known as water guzzlers by consuming anywhere between 70%and 90%of all human water use globally.Given these facts and the increase in global population to nearly 10 billion by the year 2050,the need for routine,rapid,and automated cropland mapping year-after-year and/or season-after-season is of great importance.The overarching goal of this study was to generate standard and routine cropland products,year-after-year,over very large areas through the use of two novel methods:(a)quantitative spectral matching techniques(QSMTs)applied at continental level and(b)rule-based Automated Cropland Classification Algorithm(ACCA)with the ability to hind-cast,now-cast,and future-cast.Australia was chosen for the study given its extensive croplands,rich history of agriculture,and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing.This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer(MODIS)250-m normalized difference vegetation index 16-day composite time-series data for 16 years:2000 through 2015.The products consisted of:(1)cropland extent/areas versus cropland fallow areas,(2)irrigated versus rainfed croplands,and(3)cropping intensities:single,double,and continuous cropping.An accurate reference cropland product(RCP)for the year 2014(RCP2014)produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015.A comparison between the ACCA-derived cropland products(ACPs)for the year 2014(ACP2014)versus RCP2014 provided an overall agreement of 89.4%(kappa=0.814)with six classes:(a)producer’s accuracies varying between 72%and 90%and(b)user’s accuracies varying between 79%and 90%.ACPs for the individual years 2000–2013 and 2015(ACP2000–ACP2013,ACP2015)showed very strong similarities with several other studies.The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015,thus highlighting the value of the study in food security analysis.

Shape correspondence for cel animation based on a shape association graph and spectral matching

We present an effective spectral matching method based on a shape association graph for finding region correspondences between two cel animation keyframes.We formulate the correspondence problem as an adapted quadratic assignment problem,which comprehensively considers both the intrinsic geometric and topology of regions to find the globally optimal correspondence.To simultaneously represent the geometric and topological similarities between regions,we propose a shape association graph(SAG),whose node attributes indicate the geometric distance between regions,and whose edge attributes indicate the topological distance between combined region pairs.We convert topological distance to geometric distance between geometric objects with topological features of the pairs,and introduce Kendall shape space to calculate the intrinsic geometric distance.By utilizing the spectral properties of the affinity matrix induced by the SAG,our approach can efficiently extract globally optimal region correspondences,even if shapes have inconsistent topology and severe deformation.It is also robust to shapes undergoing similarity transformations,and compatible with parallel computing techniques.

Robust key point descriptor for multi-spectral image matching

Histogram of collinear gradient-enhanced coding （HCGEC）, a robust key point descriptor for multi-spectral image matching, is proposed. The HCGEC mainly encodes rough structures within an image and suppresses detailed textural information, which is desirable in multi-spectral image matching. Experiments on two multi-spectral data sets demonstrate that the proposed descriptor can yield significantly better results than some state-of- the-art descriptors.

Generation of artificial earthquakes for matching target response unsmooth spectrum via wavelet package transform

The seismic records of target response spectrum used in the time-history analysis should be allowed to meet the norms. However, the previous fitting methods of target spectrum are mostly for the situations that the target spectrum is a smooth curve. In many cases, it needs to match unsmooth target spectrum for single determined response spectrum. An adjustment of time history via wavelet packet transform was presented, which is able to fit unsmooth target spectrum. It was found that there is a certain bias between the band center frequency of the component of seismic record after wavelet packet decomposition and the peak frequency of response spectra of wavelet packet components. For this reason, five strategies were presented to select iteration points, and the effects of the five strategies were compared with two calculation examples. It was turned out that the peak frequency of the response spectrum of wavelet packet component can lead to good fitting effect when it is selected as the iteration point. In the iteration process, it shows great promise in fitting non-smooth target spectrum and has a trend of converge.

自激式电感耦合等离子体光源设计及光谱分析

电感耦合等离子体(ICP)光谱仪是一种应用范围很广的元素分析仪,主要用于元素定性与定量的分析。ICP光源是光谱仪的核心部件,它在光谱仪中扮演着至关重要的角色。目前主流的ICP光源有两种:自激式和他激式,各自具有优缺点。他激式光源电路复杂、体积庞大、阻抗匹配速度较慢、最大输出功率有限。设计了一种基于镀金工艺的高功率MOSFET和变频实现阻抗匹配的自激式ICP光源,该光源采用射频放大和阻抗匹配一体化的设计,把从负载线圈采样的频率信号f_(1)以及功率放大电路放大后的频率相位f_(0)进行相位比较,再根据两者相位差的变化,进而控制频率的变化,实现阻抗匹配;设计上采用了镀金工艺的金属化硅N通道型高功率的MOSFET实现功率放大,具有更大的功率密度、更小的体积,最大输出功率超过2400 W。对研制的ICP光源进行了电学静态测试,给出了鉴相器、压控振荡器以及功率的输出特性,验证了各电路在相应工作状态下的性能表现;同时结合光谱仪进行了包含Ba、Na和Li元素标准溶液的测试,获得了目标元素的光谱信号;另外,对获得的光谱信号进行了处理,使用了基于EEMD特征增强的方法对目标元素光谱信号进行有效增强,信号的线性决定系数(R^(2))从0.97降至0.99,相对标准偏差(RSD)从6.47%降至1.07%,增强了信号的准确性和可用性。本文所研制的自激式ICP光源,通过射频放大和阻抗匹配一体化设计缩小了体积,变频技术使阻抗匹配速度从毫秒级提升到纳秒级,镀金工艺的MOSFET技术使最大输出功率从原来的1800 W增加到2400 W,为ICP光源的进一步优化和应用奠定了基础,具有重要的科学研究和工程实践意义。

使用光谱分析实现弯管中两相流含气率的预测

空气被夹带进入发动机润滑油系统形成油气两相流后,会严重影响润滑系统的正常运行。因此实现发动机润滑油系统中油气两相流含气率的准确和快速测量具有十分重要的意义。基于光谱匹配方法,对发动机润滑油系统在弯管处的油气两相流含气率进行了测量研究。首先使用近红外、可见光和紫外光谱仪获取油气两相流在2个流速、5个温度工况下0.10%~1.00%含气率范围(间隔0.06%)的吸光度数据并进行分析,确认油气两相流在三个波段下均有吸收,且吸收强度与含气率有关;其次提出将数据预处理结合光谱相似性测度方法并应用于弯管含气率光谱分析中,显著降低了基于原始光谱的含气率预测的最大相对误差。在近红外波段通过使用数据增强方法如中心化(center)和标准化(autoscaling)并结合光谱角余弦方法,得出新润滑油的含气率最大相对误差相对于原始光谱得到的数值从48%降低到36%。利用该方法分别在三个波段进行含气率预测,试验工况包括2个流速、5个温度,分析了两相流的温度和流速对含气率预测的影响。温度30.0℃,流速5.1 m·min^(-1)下,含气率信息在紫外波段(193.5~413.8 nm)与光谱向量的方向或形状差异的联系更为紧密,含气率预测的最大相对误差仅为6%。在近红外和可见光波段,流速或温度一定时,最大相对误差随着温度或流速的增加均有减小趋势。紫外波段下温度对含气率预测效果没有特定的影响规律,随着两相流的流速增加,含气率预测的最大相对误差有增大趋势。结果表明对于透光性较好的新润滑油,使用紫外光谱仪进行含气率数据的采集,含气率的预测选用标准化预处理结合光谱角余弦方法,得到的含气率预测最大相对误差最小。

A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images.Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental conditions.Here,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions.Accordingly,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images.The divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDR algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize endmembers.The endmember with the minimum error is chosen as the final endmember in each specific category.The proposed method is simple and automatically considers endmember variability in hyperspectral images.The efficiency of the proposed method is evaluated using two real hyperspectral datasets.The average spectral angle and abundance angle are used to analyze the performance measures.

Applications of Hyperspectral Remote Sensing in Ground Object Identification and Classification

Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized. Finally the prospects of hyperspectral remote sensing in ground object identification and classification were prospected.

Spectral Analysis of Hyperion Data for Mapping the Spatial Variation of in a Part of Latehar &Gumla District, Jharkhand

Reflectance and emittance spectroscopy in the near-infra red and short-wave infra red offers a rapid, Inexpensive, non-destructive tool for determining the mineralogy of rock and soil samples. Hyperspectral remote sensing has the potential to provide the detailed physico-chemistry (mineralogy, chemistry, morphology) of the earth’s surface. This information is useful for mapping potential host rocks, alteration assemblages and mineral characteristics, in contrast to the older generation of low spectral resolution systems. In the present study EO-1, hyperion data has been used for the delineation of AL+OH minerals. The requirements for extracting bauxites from Hyperion images is to first compensate for atmospheric effects using cross track illumination correction & the log residual calibration model. MNF transformation was applied to reduce the data noise and for extracting the extreme pixels. Some pure pixel end member for the target mineral and the backgrounds were used in this study to account for the spectral angle mapping & matched filtering and the results were validated with the respect of field study.

考虑关联波段特性的光谱相似图像分类方法

光谱相似图像分类性能过差会增加光谱信息冗余度,降低地物勘探与军事防御等多种领域的光谱探测效率。为了多元素匀质区分光谱信息与光谱曲线,提出考虑关联波段特性的光谱相似图像分类方法。该方法首先利用光谱匹配消除光谱相似图像白色光源过曝现象。然后提取优化图像的关联波段,并将其作为聚类特征输入支持向量机中。最后根据支持向量机的输出结果,实现光谱相似图像分类。实验结果表明,所提方法分类结果清晰度较高,分类误差或像素块填色错误小,混淆矩阵中同行同列矩形块的分类精度较高。

实际灌溉面积遥感监测技术及其在江苏省灌区示范应用分析

总结实际灌溉面积监测技术研究进展,分析了江苏省灌区监测实际灌溉面积面临的问题及难点;介绍了一种灌区多尺度实际灌溉面积遥感监测关键技术方法,并以宿迁灌区、船行灌区为例阐述了这一方法的合理性、适用性,提出应用该方法提高江苏省各地灌区实际灌溉面积监测精度与效率的建议,可为江苏灌区管理提供关键技术支撑。

Seismic wave modeling in viscoelastic VTI media using spectral element method

Spectral element method（SEM） for elastic media is well known for its great flexibility and high accuracy in solving problems with complex geometries.It is an advanced choice for wave simulations.Due to anelasticity of earth media,SEM for elastic media is no longer appropriate.On fundamental of the second-order elastic SEM,this work takes the viscoelastic wave equations and the vertical transversely isotropic（VTI） media into consideration,and establishes the second-order SEM for wave modeling in viscoelastic VTI media.The second-order perfectly matched layer for viscoelastic VTI media is also introduced.The problem of handling the overlapped absorbed corners is solved.A comparison with the analytical solution in a twodimensional viscoelastic homogeneous medium shows that the method is accurate in the wave-field modeling.Furtherly,numerical validation also presents its great flexibility in solving wave propagation problems in complex heterogeneous media.This second-order SEM with perfectly matched layer for viscoelastic VTI media can be easily applied in wave modeling in a limited region.

基于全波段反射光谱的花岗岩及其主要矿物自动识别研究--以康定某隧道为例

岩矿的全波段光谱是高光谱遥感岩性解译的物理基础,也对岩矿的自动识别及分类具有重要意义。本文应用高性能便携地物光谱仪,在卤素灯全波段光源照射下,测量实验室、野外露头及隧道内岩矿样本的反射光谱,构建了94种常见岩石和矿物的用户地物波谱库,重点对典型花岗岩及构成花岗岩的6种主要矿物光谱特征进行研究。系统总结了花岗岩及其主要矿物波谱特征、主要吸收峰谷及产生的机理。运用光谱角匹配法(SAM)、光谱特征拟合法(SFF)和二进制编码分类法(BE)3种方法相结合并进行1∶1∶0.7权重分配的评分方式,将野外测得的8件中—酸性岩浆岩及矿物比对ENVI软件自带的JPL岩矿波谱库与用户地物波谱库,进行自动识别,结果显示,运用波谱匹配方法可实现岩矿自动识别和分类,用户地物波谱库在岩矿自动识别中具有明显优势。本研究以康定某隧道现场花岗岩及其主要矿物的全波段光谱研究为例,探讨了岩矿地物波谱在岩矿识别及自动分类中的价值,为岩矿快速、无损、便捷识别提供了新思路,建立的用户波谱库可应用于区域内岩矿的分类,对高光谱遥感岩性填图及矿产勘查具有较重要价值。

飞机机翼表面损伤的近红外高光谱识别

飞机表面损伤的检测和设备研究,对于飞行安全与运营效益具有重大的现实意义。光谱匹配技术是飞机表面损伤高光谱检测需要解决的关键技术之一。不同光谱匹配算法的识别精度常因研究对象而异。为了利用光谱匹配算法实现飞机样品损伤识别,首先搭建了室内近红外高光谱飞机表面损伤检测系统,采集了参考样品和蒙皮样品两类样品的高光谱数据,并利用损伤像元光谱和无损像元光谱制作了两类像元的标准光谱。接着基于计算待测像元光谱与标准光谱相似度的匹配方法,先后采用光谱角(SA)、马氏距离(MD)、光谱信息散度(SID)、光谱相关系数(SCC)四类单一光谱匹配算法和六类组合光谱匹配算法进行两类样品的损伤识别。利用总体分类精度Pa和Kappa系数对上述光谱匹配算法的损伤识别结果进行精度评价。通过对SA、MD、SID、SCC单一算法的阈值参数优化,给出能够较好满足检测需求的合理阈值组。进一步,基于四类单一算法进行组合匹配算法设计,分别采用SA-MD、SA-SID、SA-SCC、MD-SID、MD-SCC和SID-SCC六类组合算法对两类飞机样品进行损伤识别。结果表明,相比单一匹配算法,组合算法的整体识别准确率相对较高。最后,分别给出适用于飞机样品损伤识别的最优单一匹配算法和组合算法,其中SCC算法和MD-SCC算法,对两类样品的损伤识别率分别可达95%以上和97.5%,可为外场飞机表面损伤的高光谱检测提供技术支撑。

基于场景光谱库的高光谱影像模拟

高光谱影像能够获取地物精细的光谱信息,是地物精细识别、参数高精度反演的数据源。然而,高光谱传感器由于其自身的特征,往往覆盖周期较长,从而限制了其应用推广。为了提高高光谱影像的时效性,研究人员开展了大量高光谱影像模拟的研究,然而已有的大多数方法是基于标准光谱库,与实际的场景光谱存在较大差异。本文构建了一种基于场景光谱库的高光谱影像模拟方法,并提出一种基于归一化植被指数(NDVI)的光谱匹配算法,提升了模拟的速度和精度。该方法以山东省德州市西南部为试验区进行实验验证,以高分一号(GF-1)WFV多光谱数据为模板影像对高分五号(GF-5)AHSI载荷数据进行了模拟,并且同传统的模拟方法进行了对比分析。分析结果表明:建立的新方法模拟结果表现良好,283个有效波段的平均R^(2)为0.69,相较于基于类别匹配的传统模拟方法提升了0.16,但各波段的模拟效果存在差异,其中在第71波段处模拟效果最佳,R^(2)达到了0.81,比基于类别匹配的模拟方法增加了0.18;在运行效率方面,该方法模拟时间大大缩短,模拟速率提高了75%。