一种基于地物波谱特征的最佳波段组合选取方法

对多光谱数据选取最佳的波段组合,是图像解译和专题信息提取的重要前提。文中提出一种基于地物波谱特征的最佳波段组合选取方法,即综合考虑方差、相关系数、OIF指数和地物间的可分离性4个因素,利用ERDAS和EXCEL等工具进行各指标的解算,并通过实验,选择红菱矿区1995年的TM多光谱影像为数据源,选取了基于水体波谱特征的最佳波段组合TM345。经定性分析和定量计算,验证了该方法的可行性。     

RS-II四通道野外光谱仪的特性及应用

遥感接收的信息反应了地物的电磁波特性。它的工作波段已远远超出了人眼有响应的可见光波段,而扩展至紫外、红外和微波等波段。因此要识别遥感图象必须依据各类地物在这些波段中的波谱特性。遥感中常用的扫描型传感器所记录的数值与地物的波谱值有定量的对应关系,因而准确地测定地物的波谱值,是定量遥感工作的基础。 由北京大学遥感技术应用研究所和物理学研制的RS-Ⅱ型四通道野外光谱仪,是为测定地物在可见光和近红外波段的反射率而设计的一种便携式野外光谱仪。它有四个光谱通道,这些通道的光谱范围与陆地卫星中的多光谱扫描仪(MSS)的四个波段的光谱范     

面向土地利用分类的HJ-1 CCD影像最佳分形波段选择

环境一号卫星（HJ-1）CCD影像光谱波段较少,地物之间的准确分类识别有一定困难。采用分形纹理辅助地物分类识别是一种有效方法,而波段选择是提高分类识别精度的关键。本文以江西赣州定南县土地利用分类为例,采用双毯覆盖模型对HJ卫星CCD影像6类典型地物的波谱分形特征进行了分析,利用不同地物在不同波段上的分形区分度差异构建了最佳分形波段选择模型,并利用该模型挑选出最佳分形波段来辅助土地利用分类,最后对分类结果进行检验。结果表明：最佳分形波段选择模型能够综合权衡不同地物在不同波段上的分形区分度差异,利用挑选出来的最佳分形波段来辅助分类,其分类总体精度相对于原始影像分类提高了11.77%,相对于第1主成分分形辅助下的分类提高了1.56%。     

彩色红外航空像片反演地物波谱的研究

本文通过对彩色红外航空摄影的物理机制的研究,揭示了其影像光谱密度的指数变换与地物光谱反射比之间的线性关系,给出了实用的由彩色红外航空像片反演地物波谱的统计方法并作了相应实验,结果表明,用国产彩色红外航空像片HCJ-2可以反演6个以上波段的地物波谱。由于本统计方法易于使整幅航空像片的反演在计算机上实现,故它基本上具有实用、深入和高效的特点。     

地物波谱数据辅助的SPOT影像模拟真彩色方法研究

提出一种新的高分辨率遥感影像真彩色模拟方法。该方法从遥感成像光谱角度,依据地物波谱特性,采用光谱角匹配模型(SAM),首先计算出地物在绿(G)、红(R)、近红外(NIR)以及短波红外(SWIR)波段的整体和局部波谱向量角;然后根据角度距离构建最小距离隶属函数,查找对应地物波谱库中最为相似的地物波谱;最后对此条地物波谱曲线积分计算出该像素点在蓝波段的反射率。实验表明,该方法模拟出的SPOT真彩色影像在视觉效果和空间信息上优于传统的方法。     

测绘地物波谱本底数据库

地物波谱特性是利用遥感进行建模反演和地物目标探测识别的理论基础,波谱数据库对于提高遥感应用水平有重要作用。为规范和丰富中国波谱数据库并促进其在测绘等领域的应用,依托国家科技性基础专项,建立了“测绘地物波谱本底数据库”,在数据规范编制、波谱数据采集实验与整编入库、地物波谱数据库建设和应用示范初步探索方面取得重要进展。在波谱数据采集和处理的标准规范方面,建立完善了测绘地物波谱与配套非波谱数据收集与汇总标准、测绘地物波谱和配套非波谱参数测试技术规范、测绘地物波谱库地物分类编码规范。依照项目制定的数据规范标准,对现有波谱数据进行了整编和入库,共计整编数据1.4万余条;组织实施了全国范围的典型地物波谱采集实验以及相应配套参数测量实验,主要试验类型包括典型地物波谱测量试验、定点时间序列测量试验、多尺度测量试验、全波段测量试验、参数控制测量试验和无人机观测试验等,共计获取地物波谱1.7万余条。特别是在华北和东北进行了全波段观测试验（可见光—近红外波（0.35—2.5 μm）、红外波段（3—15 μm）、微波波段（Ka、K、X、C、L波段））,获取冰雪、土壤、植被冠层、人工目标的全波段数据。为方便波谱数据的应用和共享,项目建设了包含3万余条数据的测绘地物波谱本底数据库（GOSPEL）及数据共享平台。依据地物波谱数据的类型和应用需求,形成了全波段典型地物波谱数据集、多角度光谱反射率数据集、多尺度典型地物反射率数据集、长时间系列波谱数据集等共25个特色数据集。基于测绘地物波谱本底数据库进行了地物分类、遥感机理模拟、遥感定量反演、遥感产品验证等方面的应用示范。     

成像光谱遥感技术及其图像光谱信息提取的分析研究

成像光谱技术是80年代发展起来的最新遥感方法。本文对其原理、理论基础及图像光谱信息提取的方法进行了探讨,并在红外细分光谱(FIMS)金矿蚀变带信息提取分析研究的基础上,通过对可见光细分19波段AMSS、澳大利亚的24波段GEOSCAN、MKII AMSS及美国GER64通道成像光谱数据的初步处理,发展和形成了一些针对超多波段成像光谱数据的图像处理和分析及光谱信息提取的方法。 成像光谱信息提取的方法,主要包括图像光谱反射率转换技术、图像光谱曲线显示、光谱特征参数测度(光谱吸收特征的波长位置、宽度、深度)、图像地物光谱曲线与地物光谱数据库的信息匹配以及地物光谱识别专家系统。本文以红外细分光谱图像在金矿蚀变带信息提取分析中的应用为例,讨论了成像光谱图像的一种处理分析技术及其发展前景。     

基于谱间特征分析的城市建筑用地信息提取

以2003年某地区QuickBird多光谱影像为数据源,首先分析建筑用地及其他地物在QuickBird卫星影像4个波段上的光谱特征,并由此探讨其在光谱特征上的可分性。研究发现对于建筑用地等地物类型,波段2和波段4的光谱特征对比度最大,因此通过简单的波段比值运算可最大限度地增强该类地物的亮度。在此基础上,通过分析水体、裸露地的波谱特性,建立基于光谱特征的决策树模型,对研究区域建筑用地进行提取并对结果进行精度评价,结果表明,该方法总体提取效果较好。因此利用该模型可以将背景地物类型复杂的某地区建筑用地信息提取出来,并且模型受时相影响较小,只是在阈值大小上会存在一些差异。     

资源一号02D卫星高光谱数据黄河三角洲湿地景观分类

资源一号02D卫星(ZY-1 02D)于2019年成功发射，2020年10月正式投入使用，是中国自主建造并成功运行的首颗民用高光谱业务卫星，具有广泛的应用前景。本研究以黄河三角洲湿地为研究区，以ZY-1 02D高光谱(AHSI)影像为数据源，结合无人机和地面调查数据，开展湿地景观分类研究。首先通过ZY-1 02D AHSI获取地物反射率波谱曲线，分析不同地物波谱曲线的差异，作为地物识别和分类的依据；充分考虑研究区植被覆盖度的差异，结合无人机影像制定研究区7类基本地物和9类精细地物两种湿地景观分类体系；利用随机森林算法进行分类，并引入Tree SHAP方法进行波段重要性排序和选择；探究影响ZY-1 02D AHSI分类的重要波段，选取与Landsat 8 OLI多光谱波段相重叠的波段进行分类，并与Landsat 8 OLI分类结果进行比较。结果表明：(1) ZY-1 02D AHSI数据能够较好地反映不同地物类型光谱曲线的差异；(2)对于两种分类体系，仅用前40个重要波段的总体分类精度达到最高，7类基本地物分类和9类精细地物分类的分类精度分别为92.18%和90.76%，这40个波段大多...     

基于最佳波段组合的高光谱遥感影像分类

针对高光谱数据维数高、数据量大、信息冗余多、波段相关性强等特点,在综合各种数据降维方法的基础上,提出一种基于最佳波段组合的高光谱遥感影像分类方法。以美国印第安纳州地区的AVIRIS数据为例,分析各波段信息量和相邻波段的相关性,利用子空间划分、分段波段指数选择法,进行特征波段的选择;并针对难区分地物类别,应用J-M距离模型对其可分性进行判别,获得最佳波段组合。最后采用支持向量机分类器进行分类。实验结果表明,采用最佳波段组合方法,可以有效地提高高光谱的分类精度。     

Study of fractional vegetation cover using high spectral resolution data

A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.     

Crop cover and crop phenological information from red and infrared spectral responses

Possibility of utilizing the red and infrared spectral information for assessing status of vegetation cover and consequential crop phenological information are discussed. The experiment was conducted in a potential agricultural area around Mandya town of Karnataka State and airborne spectral information was obtained through modular multispectral scanner from a height of 1000 meters above the ground level. The spectral information of red (0.66–0.70 urn) and infrared (0.77–0.86 urn) bands was extracted with the aid of an interactive computer system : the multispectral data analysis system. Based on the spectral information, the data was analysed and interpreted with the support of ground information. Crop fields without vegetation were observed to have infrared/red ratio in the range of 0.70 to 0.97 and also it was possible to distinguish wet and dry paddy field. Crop fields covered with vegetation exhibited higher infrared/red ratio depending on the nature of crop growth. For instance, rice crop exhibited spectral ratio of 0.78 at the time of planting, 3.52 at the time of maximum vegetation growth and 2.04 during the maturation phase. In case of sugarcane crop, the increase and decrease in spectral ratio were gradual because of its longer duration. From infrared and red band information it was possible to distinguish crop species based on rate of change of vegetation cover which corresponded with the change in spectral ratios. The temporal information expressed in two dimensional space for red and infrared band also enabled clearly to distinguish between rice and sugarcane.     

Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands

Leaf pigment content provides valuable insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast, nondestructive method for pigment estimation. A number of methods were used previously for estimation of leaf pigment content, however, spectral bands employed varied widely among the models and data used. Our objective was to find informative spectral bands in three types of models, vegetation indices (VI), neural network (NN) and partial least squares (PLS) regression, for estimating leaf chlorophyll (Chl) and carotenoids (Car) contents of three unrelated tree species and to assess the accuracy of the models using a minimal number of bands. The bands selected by PLS, NN and VIs were in close agreement and did not depend on the data used. The results of the uninformative variable elimination PLS approach, where the reliability parameter was used as an indicator of the information contained in the spectral bands, confirmed the bands selected by the VIs, NN, and PLS models. All three types of models were able to accurately estimate Chl content with coefficient of variation below 12% for all three species with VI showing the best performance. NN and PLS using reflectance in four spectral bands were able to estimate accurately Car content with coefficient of variation below 14%. The quantitative framework presented here offers a new way of estimating foliar pigment content not requiring model re-parameterization for different species. The approach was tested using the spectral bands of the future Sentinel-2 satellite and the results of these simulations showed that accurate pigment estimation from satellite would be possible.     

Delineating salt-affected soils in the ganges plain by digital analysis of landsat data

A study has been conducted to determine the feasibility of delineating salt-affected soils using computer-aided analysis of Landsat-1 data in an area of the Ganges Plain. The multispectral scanner data have been obtained on 2nd December 1972 Landsat pass. Both supervised and unsupervised classification techniques are used. Four spectral classes of salt-affected soils have been separated. The results indicate that Landsat data can be successfully used for differentiating salt-affected soils.     

An Efficient Reordering Prediction-Based Lossless Compression Algorithm for Hyperspectral Images

In this letter, we propose an efficient lossless compression algorithm for hyperspectral images; it is based on an adaptive spectral band reordering algorithm and an adaptive backward previous closest neighbor (PCN) prediction with error feedback. The adaptive spectral band reordering algorithm has some strong points. It can adaptively determine the range of spectral bands needed to be reordered, and it can efficiently find the optimum branches. Hyperspectral images have a large number of spectral bands, which express the same land cover structure and have high correlation. The adaptive backward PCN prediction with error feedback can sufficiently make use of this correlation. Experiments show that implementing both the reordering of the spectral bands before prediction and the prediction with error feedback improve compression performance     

Fractal-based dimensionality reduction of hyperspectral images

The spectral reflectance of any pixel in a remote sensing image depends on the characteristics of the particular land cover (LC) present in the Instantaneous Field of View (IFOV) of the sensor. The fractal dimension of the spectral reflectance curve (SRC) of any pixel can thus be visualized as a representation of the characteristics of the LC. Based on this, a fractalbased method for reduction of the dimensionality of Hyperspectral (HS) images has been investigated. The fractal dimension (FD) of SRC has been calculated by adopting a method based on Hausdorff metric that reduces the dimensionality from N HS bands to a single feature incorporating the characteristics associated with each of the bands. Further, it has been established that FD values can be used as a feature to identify anomaly in water cover.     

Spectral Deconvolution and Non-Overlap Bands Sampling for IMS-1 Hyperspectral Imager Data

In this paper, we attempt to realize ‘near true’ spectral signatures of the ground features achievable from IMS-1 hyperspectral Imager (HySI) by employing spectral deconvolution method. From spectral response function of 64 spectral bands, it is first inferred that their spectral bandwidths are higher than the Nyquist sampling, suggesting that the spectral details of ground features are likely to be affected. The proposed approach tries artificially to reduce effective bandwidth of each band, thereby resolving individual band spectrum separable for further spectroscopic investigation. The spectral bands are further sampled to non-overlapping 17 spectral bands at a sampling interval of 25 nm to enable extraction of unique spectral signatures of the ground features.     

联合统计信息与散射模型的GF-5 AHSI可见光影像薄云校正

针对高分五号可见短波红外高光谱相机AHSI (visible-shortwave infrared Advanced HyperSpectral Imager)可见光波段存在的薄云干扰,本文提出了一种联合统计信息与散射模型的校正方法。利用AHSI影像邻近波段间地表与云雾辐射的统计差异,实现对不同场景下相对薄云辐射RCR (Relative Cloud Radiance)的准确估计。基于此,根据不同波段的散射特性,分别利用分级暗目标统计和散射模型约束策略,获取可见光波段的绝对云辐射强度,最终实现影像校正。通过设置模拟与真实实验对方法的有效性和鲁棒性进行目视和定量检验。模拟实验中,可见光波段内的薄云干扰均可被有效地去除,校正结果与真实地表十分一致;此外,RMSE (RootMean-Square Error),MAE (Mean Absolute Error)和SA (Spectral Angle) 3个评价指标的值分别为1.9891,1.6822和0.4901,远小于对比方法。真实实验中,不同场景内的薄云可被有效抑制,在较为准确恢复降质地表信息的同时保持晴空区光谱特征;Q指数,SSIM (Structural Similarity Index)和UQI (Universal Quality Index)的计算结果优于对比方法。综上,本文提出方法可用于不同场景下高分五号AHSI影像可见光波段的薄云校正,可得到目视效果良好且光谱保真度高的校正结果。     

基于小波变换的多光谱图像压缩方法

在分析多光谱图像小波变换后系数特点的基础上,提出了一种共享有效图的小波变换压缩方法（ＳＳＭＷＴ）。该方法将小波变换压缩技术中的零树编码推广到多光谱图像压缩中,利用多光谱图像的结构相关性,对多幅小波图像只需构造一幅有效图,同时去除空间冗余和谱间结构冗余,并与Ｋ－Ｌ变换相结合,进一步去除谱间统计冗余,实验表明了该方法的有效性。     

Spectral Reflectance Characteristics of the Soils on Basaltic Terrain of Central Indian Plateau

In-situ spectral reflectance characteristics of soils were studied under field conditions with Multiband Ground Truth Radiometer covering 0.45?C0.52, 0.52?C0.59, 0.62?C0.68, and 0.77?C0.86 ??m spectral bands. Twenty-two surface soil samples were studied in laboratory for their spectral reflectance characteristics using ISCO Model S.R. Spectroradiometer in visible wavelength (450?C725 nm), with 25 nm bandwidth, and in infrared wavelength (750?C1550 nm), with 50 nm bandwidth. The Bidirectional reflectance factor representative of spectral reflectance varied from 3.78 to 11.3???m in band 1, 6.09 to 15.41???m in band 2, 8.05 to 19.41???m in band 3, and 12.18 to 31.2???m in band 4. In-situ spectral reflectance in general increases with the wavelength from visible to infrared bands for all the soils. Black soils have relatively lower reflectance as compared to red soils, which is attributed to the variation in the physicochemical properties of soils. Spectral reflectance, under laboratory conditions, for all the soils increases with wavelength from visible to infrared region except at 950 nm and 1200 nm, where reflectance decreased in all soils, due to weaker water absorption bands and also at 1350 nm, due to strong water absorption at this band. The spectral reflectance of red soils were higher, in-situ as well as under laboratory conditions, as compared to black soils, which is attributed to variation in soil colour, organic matter and clay content of soils. It is observed that the spectral reflectance decrease due to moisture content in soils in all the spectral bands because of darker appearance of soils at moist conditions. Laboratory reflectance measurements serve to define the extent to which intrinsic spectral information is available from soils as a consequence of their composition.