基于谱间特征与多种指数分析的居民地信息提取方法

本文在分析现有居民地提取方法的基础上,提出将归一化建筑指数(NDBI)、改进归一化差异水体指数(MNDWI)、土壤调节植被指数(SAVI)、比值居民地指数(RRI)相结合进行居民地信息提取的方法。以浙江省宁波市为例,通过光谱采样及各类地物在4种指数上的取值分析,建立模型进行居民地信息提取及精度验证,结果表明:利用该模型可以实现居民地信息的自动提取,能提高居民地与裸地的可分性,减少背景地物的影响,总体精度为91.08%。     

ASTER数据的煤矿区水体信息提取

为实现多光谱数据对煤矿区水体信息提取，以萍乡市芦新岭及周边煤矿为试验区，在国内外学者对水体指数法提取水体信息和应用的启发下，对试验区不同地物的波谱特征进行分析。本文首先利用数学统计方法建立一种ASTER数据综合归一化差异水体指数模型SWI ASTER_xy，然后利用这一模型对试验区进行水体信息提取。试验表明，该模型能够有效地提取矿区各类水体，还可以运用在ASTER数据（或其他多光谱数据）对不同区域不同地物的提取，在遥感地物定量提取上具有很好的效果和潜力，具有一定的推广价值。     

基于GF-1遥感数据的城市水体信息提取方法研究

水体信息的高精度提取是水资源监测、调查与管理等研究领域的关键问题。本文选取国产GF-1卫星影像作为数据源,根据影像中典型地物光谱特征采用归一化水体指数法(NDWI)、多波段谱间关系法对研究区进行了水体提取,并通过结果分析提出了基于主成分分析与多尺度分割技术的综合水体信息提取方法。对以上4种方法进行对比分析,验证结果表明:归一化水体指数法易受阴影信息影响;改进的多波段谱间关系法能较为完整地提取水体信息,但受小范围阴影信息影响;主成分分析综合法总体精度较高;多尺度分割提取法能有效地分离水体信息与非水体信息,水体信息提取的效果最佳。     

不同指数模型提取ALOS AVNIR-2影像中水体的敏感性和精度分析

以广东惠州附近水域为研究区,利用ALOS AVNIR-2多光谱数据分析水体和其他主要地物在影像上的光谱特征。构建几种常用的波谱指数,分析阈值对各种指数模型提取水体的敏感性及所能达到的最大精度。发现利用这些指数均可以较好地提取图像中的水体信息,提取效果从好到差依次是:归一化差值水体指数法(NDWI)、近红外波段分割法(NIR)、归一化差值植被指数法(NDVI)和比值植被指数法(RVI)。其中NDWI指数模型,在选择合适阈值的情况下,水体提取的总体精度最高可达98%左右,并且提取过程对阈值影响不敏感。     

遥感组合指数与不同分类技术结合提取农业用地方法

多光谱遥感影像因具有丰富的波谱信息,提高了地表覆盖的辨识能力,利用遥感数据高精度自动提取专题信息是目前研究的热点和难点。本文以北京市ASTER影像为例,通过对城市生态环境中土地类型及其光谱特征规律分析,组合归一化差异植被指数、修正归一化差异水体指数和归一化差异建筑指数三种指数,制作组合指数新影像。对组合指数影像采用基于支持向量机的面向对象分类方法进行农业用地信息提取,同时将该方法分别与基于原始影像、组合指数影像的最大似然及支持向量机的分类方法进行对比分析。实验结果表明:组合归一化差异指数影像压缩了数据维数,降低了覆盖地物相关性,易于农业用地信息提取。对组合指数影像采用基于支持向量机的面向对象分类方法精度达95.701%。     

高潜水位煤矿区沉陷水体信息提取

针对高潜水位煤矿区煤炭开采后极易形成大面积的沉陷积水区,且沉陷水体信息易与矿区其他地物混淆的问题,该文提出了针对高潜水位矿区水体信息提取的增强型改进归一化水体指数(E-MNDWI).对研究区典型地物光谱进行分析,将E-MNDWI应用于Landsat系列数据,并对比验证该指数与改进归一化水体指数(MNDWI)的精度水平.研究结果表明:矿区投入运行后,利用E-MNDWI提取沉陷水体的精度普遍较MNDWI高,总体精度在90％以上,Kappa系数大于80％.选取高分1号数据作为验证数据源,发现E-MNDWI的计算面积精度高达93.07％.与其他水体指数对比,E-MNDWI的综合精度最高.E-MNDWI对于山区和平原水体提取具有较好的效果,能够消除山体阴影和其他物体倒影的影响.     

SPOT卫星影像居民地信息自动提取的决策树方法研究

以南京市江宁县为研究区域，首先分析了该区域居民地的影像特征，然后研究了居民地及其背景地物在SPOT—4(Ⅺ)卫星影像4个波段上的光谱特征，并由此探讨了它们在光谱特征上的可分性。研究发现，除道路在光谱特征上与居民地差异不大而难以完全分开外，其它背影地物均可以依据各波段亮度值的大小关系或适当的阈值与居民地分开。但道路和居民地在形状上存在明显差异，因此可以利用形状指数的差异加以去除。最后分析建立了基于光谱特征和形状特征的简单决策树模型，对研究区域居民地信息进行了提取并对结果进行了精度评价。结果表明，该方法的总体提取效果较好，特别是对于面积大于10000m^2的城镇和集村。其提取精度与通常的监督分类方法相比有了很大的提高，只是在水际交界处和道路两侧有误判现象。因此，利用该模型可以将背景地物类型复杂的江南地区的城镇和集村居民地自动提取出来，并且模型受时相影响较小，只是在域值大小上会存在一些差异。     

基于中分辨率成像光谱仪数据的建筑指数研究

中分辨率成像光谱仪(MODIS)数据是研究大尺度土地利用/土地覆盖的有效数据手段。针对现有建设用地提取方法的不足,文章通过分析MODIS数据的地物光谱特征,给出适合MODIS数据的归一化建筑指数,并以京津冀城市群为研究区提取建设用地,最后从位置和面积两方面对提取结果进行精度验证。结果表明:该文构建的归一化建筑指数不但加强了MODIS数据中的建设用地,还成功抑制了裸地、荒地和已收割耕地等对建设用地提取的干扰。     

一种用于城市信息提取的改进居民地指数

区域或者全球尺度上的城市分布信息提取是目前研究的热点与难点。采用DMSP-OLS夜晚灯光数据直接提取城市信息会受到灯光溢出问题的影响,且溢出问题因灯光光斑大小而异,不易定量分析。采用可见光-近红外遥感影像提取城市信息时,多选取植被丰富的地区,避免了裸土对城市信息提取造成的影响,但限制了研究区域的选择。为了解决以上问题,应用DMSP-OLS夜晚灯光数据和可见光-近红外遥感影像,对居民地指数(human settlement index,HSI)进行改进,构建了改进居民地指数(modified human settlement index,MHSI)。采用MHSI对中国和美国的城市进行了提取实验,并利用中国历年城市统计数据和美国NLCD土地覆盖数据集对提取结果进行验证。实验结果表明,MHSI在解决灯光溢出问题的同时,避免了其他地物类型(裸土、水体和植被)对城市信息提取的影响,一次性实现了区域或者全球城市信息的提取,提取精度优于HSI和MODIS土地覆盖数据集。     

应用高分二号提取二级类建设用地

为更好地利用国产遥感数据研究土地利用资源分布,基于面向对象和指数分类的方法,开展高分二号遥感影像建设用地二级类提取.实现了研究区居民点、仓储用地、工业用地、公共管理与公共服务用地及交通用地的提取,提取总精度达96.9％,Kappa系数达0.94.结果表明本研究方法可以较准确提取高分二号遥感数据的二级类建设用地,对国产数据地物信息提取的研究有一定借鉴意义.     

苏、锡、常建成区遥感方法提取及城市扩展分析

将苏州、无锡、常州三市作为研究区域,详细介绍了借助遥感技术快速获取城市建成区的新方法和技术路线.将TM3、TM2两个波段移植于归一化建筑指数(NDBI)法,并同NDBI相结合提取城市建设用地,再通过制定的适于遥感划分建成区的界定方法.筛选出符合要求的建设用地得到三座城市的建成区范围.利用简单的统计方法和叠加分析方法,对...     

Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning.     

基于综合指数分析的城市建筑用地信息提取研究

以西安市长安区TM影像为例,研究关于城市建筑用地信息快速、准确提取的方法。通过对归一化差异型指数构成原理的分析,选取土壤调节植被指数（ SAVI ）、归一化水体指数（ NDWI ）和归一化差异型建筑指数（ NDBI ）来提取植被、水体和城市建筑用地专题影像,并将其构建为一幅新影像,分析新影像谱间特征,运用逻辑运算将城市建筑用地信息提取出来。本文方法总体提取效果十分有效,尤其是对于面积较大的城市建筑用地,总精度高达85．3％。综合指数法弥补了单靠某一指数提取城市建筑用地信息的不足,提取结果客观可信,是一种不经人为干预、快速有效的提取城市建筑用地的方法。     

Semi-automatic mapping of anthropogenic impervious surfaces in an urban/suburban area using Landsat 8 satellite data

Impervious surfaces have a significant impact on urban runoff, groundwater, base flow, water quality, and climate. Increase in Anthropogenic Impervious Surfaces (AIS) for a region is a true representation of urban expansion. Monitoring of AIS in an urban region is helpful for better urban planning and resource management. Cost effective and efficient maps of AIS can be obtained for larger areas using remote sensing techniques. In the present study, extraction of AIS has been carried out using Double window Flexible Pace Search (DFPS) from a new index named as Normalized Difference Impervious Surface Index (NDAISI). NDAISI is developed by enhancing Biophysical Composition Index (BCI) in two stages using a new Modified Normalized Difference Soil Index (MNDSI). MNDSI has been developed from Band 7 and Band 8 (PAN) of Landsat 8 data. In comparison to existing impervious surface extraction methods, the new NDAISI approach is able to improve Spectral Discrimination Index (SDI) for bare soil and AIS significantly. Overall accuracy of mapping of AIS, using NDAISI approach has been found to be increased by nearly 23% when compared with existing impervious surface extraction methods.     

基于指数分析法的西安市土地利用变化及驱动力研究

基于2000和2007年2期TM遥感影像,利用指数分析法,分别提取出归一化差异建筑指数(NDBI)、修正归一化差异水体指数(MNDWI)和归一化差异植被指数(NDVI)3种指数模型,分别代表西安市的3种最主要的土地利用类型--建筑用地、水体和植被.采用神经网络分类器进行监督分类,借助ERDAS Imagine 9.0、ENVI、ArcGIS 9.2和Matlab等软件平台,计算出西安市土地利用类型的动态转移矩阵,构建了土地利用变化动态度指数模型,定量分析西安市土地利用的时空变化.依据研究区土地利用变化的结果分析,变化的驱动力因子主要是人口增长、经济增长和政策变动.     

基于PCA/NDVI的森林覆盖遥感信息提取方法研究

摘要： 以大兴安岭为试验区，提出将主成分分析（PCA）得到的第1分量、归一化植被指数（NDVI）和Landsat TM 1~TM 7某一波段进行合成，增强森林覆盖区和背景区信息的反差，并利用最大似然法对影像进行监督分类，分类精度超过92%。通过对不同云雾量和森林覆盖的2个时相影像试验表明，本方法提高了遥感影像森林覆盖信息提取的自动化程度和精度。     

基于植被指数模型的淡水湖泊湿地景观信息提取

高邮湖湿地是江苏省重要湿地之一,对生态、环境控制、调节气候和保护生物多样性具有重要意义。采用2007年的LandsatTM影像作为遥感信息源,选择影像的光谱特征和比值植被指数(RVI)、差值植被指数(DVI)、归一化植被指数(NDVI)、归一化差异绿度指数(NDGI)、土壤调节植被指数(SAVI)和最佳土壤调节植被指数(OSAVI)6种植被指数做了光谱特征分析,从而确定出最佳指数模型,并基于决策树方法,实现研究区景观信息的遥感分类。研究结果表明,决策树分类法易于综合多种特征进行遥感影像分类,植被指数参与到决策树分类中能够提高分类的总体精度,其总体精度达到79.58%,Kappa系数为0.721 0,分类结果理想且人工参与灵活。     

改进HOT法的Landsat 8 OLI遥感影像雾霾及薄云去除

针对Haze optimized transformation(HOT)方法存在的地物敏感性、过度矫正、红绿蓝波段RGB合成影像色彩失真等问题,提出了相应的改进方法。首先,采用归一化差分植被指数(NDVI)结合地物红蓝光谱差(RBSD)制作通用掩膜,并利用掩膜提取原始影像植被覆盖区对应的原始HOT图部分作为HOT值评估雾霾强度的有效像素集;然后从有效像素集出发推断非植被区的HOT值,得到有效HOT图;最后以有效HOT图为参考,实施暗目标减法。在暗目标减法过程中,首先利用直方图取百分位数的方法确定起始波段的改正值,然后根据散射模型计算其他波段的改正值。在红蓝光谱空间中,去雾后的影像表现出与原始无云区相似的特征,同时保持了不同地物间的差异。实验表明:改进的HOT方法能有效去除雾霾及薄云;有效解决了HOT对水体、裸地、人造地物等地表覆被类型的敏感性问题,避免了RGB合成影像的色彩失真;并且统一了不同波段的纠正尺度,解决了某一(或几个)波段的过度矫正问题,防止了块斑和光晕的产生。     

Fuzzy Based Approach for Moist Deciduous Forest Identification Using MODIS Temporal Data

Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %.     

Imaging and visualizing the spectral signatures from Landsat TM and ‘τ’ value-based surface soil microzonation mapping at and around Agartala (India)

This work aims to assess the soil microzonation of Agartala city and its surrounding areas based on spectral geophysical signatures. Different spectral resolutions of Landsat TM have been used for assessing the Normalized Difference Vegetative Index, spatial thermal emission representation and plant water moisture representation. Normalized Difference Vegetative Index (NDVI) was measured from band 4 (near-infrared (NIR)) and band 3 (photosynthetically active radiation (PAR)). The Digital Number (DN) values of thermal infrared band (TIR) were used for measuring spatial variation of thermal representation in the city area. A very simple model was developed for measuring thermal emission representative index from NDVI and automated classified TIR band. Overlaid NDVI and classified TIR shows the spatial distribution of thermal emission representative values. Classified mid-wave infrared band (MWIR) was used for measuring the surface geotherm units (τ) which are related with different types of soil. On the basis of spatial distribution of τ value which is clearly visible in a thermal emission representative map overlaid by classified MWIR, the soil microzonation map of the study area was prepared. This soil microzonation map shows that Agartala and its surrounding areas are characterized by four types of soil which are related to different geomorphic and geological units. The soil of this area is classified as dry sandy soil and sandy clay soil of the highland areas and wet sandy alluvium and clayey alluvium of the flood plain area.