Comparison of atmospheric correction algorithms for TM image in inland waters

In order to extract quantitative water‐leaving information from the Thematic Mapper (TM) image accurately in inland waters, atmospheric correction is a necessary step. Based on former researchers' results, the paper presents two atmospheric correction algorithms based on meteorological data (MD) and on Moderate Resolution Imaging Spectroradiometer (MODIS) Vicarious Calibration (MVC) for TM image in inland waters according to the theory of radiative transfer. Studying Taihu lake, China, in this paper we derived water remote sensing reflectance from a TM image of 26 July 2004 by these two atmospheric correction algorithms and we compare the results with that of dark object subtraction (DOS) and 6S code. The results show that the effect of atmospheric correction based on meteorological data and MODIS Vicarious Calibration is much better than that of DOS and 6S code. Although the MD is more accurate, MVC may be an ideal choice for TM images in inland water because TERRA MODIS images can be acquired easily than collecting meteorological data at the time of satellites passing over.     

Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images

The distribution of phytoplankton chlorophyll concentration in Lake Garda (Italy) was estimated using Landsat Thematic Mapper (TM) data acquired at two different times, February 1992 and March 1993. To investigate the waterleaving radiance adequately, the contribution of the atmospheric path radiance reaching the sensor should be removed. In this work a completely image-based atmospheric correction method was applied by means of an inversion technique based on a simplified radiative transfer code (RTC). A semi-empirical approach of relating atmospherically corrected TM spectral reflectances to in situ measurements through regression analysis was used. Limnological parameters were measured near to the TM images dates; some of the in situ measurements were used to define algorithms relating chlorophyll concentration measurements to water surface reflectance and the others too were used to validate the results of the predictive model. The models developed, which performed better (r² = 0.818) when concentrations were higher than > 3.0 mg m³, were used to map chlorophyll concentration throughout the lake. Spatial distribution maps of chlorophyll concentration and concentration changes were produced with contour intervals of 1 mg m³.     

Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment

Precise atmospheric correction is important for applications where small differences in surface reflectance (SR) are significant, such as biomass estimation, crop phenology, and retrieval of water quality parameters. It also enables direct comparison between different image dates and different sensors. As a precursor to monitoring different parameters of water quality around the coastline of Hong Kong using medium-resolution sensors Landsat TM/ETM, and HJ-1A/B, this study evaluated the performance of five atmospheric correction methods. The estimated SR of the first four reflective bands of Landsat 7 ETM+ and of the identical bands of the HJ-1A/B satellites was compared with in situ multispectral radiometer (MSR) SR measurements over sand, artificial turf, grass, and water surfaces for the five atmospheric correction methods – second simulation of the satellite signal in the solar spectrum (6S), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH), atmospheric correction (ATCOR), dark object subtraction (DOS), and the empirical line method (ELM). Among the five methods, 6S was observed to be consistently more precise for SR estimation, with significantly less difference from the in-situ-measured SR, especially over lower reflective water surfaces. Of the two image-based methods, DOS performed well over the darker surfaces of water and artificial turf, although still inferior to 6S, while ELM worked well for grass sites as compared to the DOS and equalled the good performance of 6S over the high reflective sand surfaces.

The study also evaluated the new standard Landsat SR product Landsat ecosystem disturbance adaptive processing system (LEDAPS) using the in situ measured SR data for the three land surface types – sand, artificial turf, and grass. For the highly and moderately reflecting bright sand and artificial turf, LEDAPS performed poorly, while for the darker grass site it performed better, although still inferior to 6S and ELM methods. This is probably due to the variable aerosol types and atmospheric conditions of Hong Kong, as LEDAPS was mainly compiled with reference to larger continental landmass areas.     

Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain,China

A method is presented for bi‐directional reflectance distribution function (BRDF) parametrization for topographic correction and surface reflectance estimation from Landsat Thematic Mapper (TM) over rugged terrain. Following this reflectance, albedo is calculated accurately. BRDF is parametrized using a land‐cover map and Landsat TM to build a BRDF factor to remove the variation of relative solar incident angle and relative sensor viewing angle per pixel. Based on the BRDF factor and radiative transfer model, solar direct radiance correction, sky diffuse radiance and adjacent terrain reflected radiance correction were introduced into the atmospheric‐topographic correction method. Solar direct radiance, sky diffuse radiance and adjacent terrain reflected radiance, as well as atmospheric transmittance and path radiance, are analysed in detail and calculated per pixel using a look‐up table (LUT) with a digital elevation model (DEM). The method is applied to Landsat TM imagery that covers a rugged area in Jiangxi province, China. Results show that atmospheric and topographic correction based on BRDF gives better surface reflectance compared with sole atmospheric correction and two other useful atmospheric‐topographic correction methods. Finally, surface albedo is calculated based on this topography‐corrected reflectance and shows a reasonable accuracy in albedo estimation.     

Comparison of atmospheric correction methods using ASTER data for the area of Crete,Greece

The purpose of atmospheric correction is to produce more accurate surface reflectance and to potentially improve the extraction of surface parameters from satellite images. To achieve this goal the influences of the atmosphere, solar illumination, sensor viewing geometry and terrain information have to be taken into account. Although a lot of information from satellite imagery can be extracted without atmospheric correction, the physically based approach offers advantages, especially when dealing with multitemporal data and/or when a comparison of data provided by different sensors is required. The use of atmospheric correction models is limited by the need to supply data related to the condition of the atmosphere at the time of imaging. Such data are not always available and the cost of their collection is considerable, hence atmospheric correction is performed with the use of standard atmospheric profiles. The use of these profiles results in a loss of accuracy. Therefore, site-dependent databases of atmospheric parameters are needed to calibrate and to adjust atmospheric correction methods for local level applications. In this article, the methodology and results of the project Adjustment of Atmospheric Correction Methods for Local Studies: Application in ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) (ATMOSAT) for the area of Crete are presented. ATMOSAT aimed at comparing several atmospheric correction methods for the area of Crete, as well as investigating the effects of atmospheric correction on land cover classification and change detection. Databases of spatio-temporal distributions of all required input parameters (atmospheric humidity, aerosols, spectral signatures, land cover and elevation) were developed and four atmospheric correction methods were applied and compared. The baseline for this comparison is the spatial distribution of surface reflectance, emitted radiance and brightness temperature as derived by ASTER Higher Level Products (HLPs). The comparison showed that a simple image based method, which was adjusted for the study area, provided satisfactory results for visible, near infrared and short-wave infrared spectral areas; therefore it can be used for local level applications. Finally, the effects of atmospheric correction on land cover classification and change detection were assessed using a time series of ASTER multispectral images acquired in 2000, 2002, 2004 and 2006. Results are in agreement with past studies, indicating that for this type of application, where a common radiometric scale is assumed among the multitemporal images, atmospheric correction should be taken into consideration in pre-processing.     

遥感数字图像的大气辐射校正应用研究

卫星遥感数字图像成像过程中，由于电磁波受大气作用造成数据质量下降，影响遥感信息的提取及精度。介绍了大气辐射校正的一般原理和PCI软件ATCOR2模块的算法，研究了基于该模块的遥感数字图像大气辐射校正实现方法。根据研究区实际情况，选用1976年美国标准大气乡村气溶胶大气参数，对一景Landsat TM影像进行校正处理。对比处理前后图像和直方图，可知该方法增强了图像清晰度，提高了视觉效果，有利于遥感信息提取和专题解译。     

Using multiple radiometric correction images to estimate leaf area index

Ecological applications of remote-sensing techniques are generally limited to images after atmospheric correction, though other radiometric correction data are potentially valuable. In this article, six spectral vegetation indices (VIs) were derived from a SPOT 5 image at four radiometric correction levels: digital number (DN), at-sensor radiance (SR), top of atmosphere reflectance (TOA) and post-atmospheric correction reflectance (PAC). These VIs include the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), slope ratio of radiation curve (K), general radiance level (L), visible-infrared radiation balance (B) and band radiance variation (V). They were then related to the leaf area index (LAI), acquired from in situ measurement in Hetian town, Fujian Province, China. The VI–LAI correlation coefficients varied greatly across vegetation types, VIs as well as image radiometric correction levels, and were not surely increased by image radiometric corrections. Among all 330 VI–LAI models established, the R ² of multi-variable models were generally higher than those of the single-variable ones. The independent variables of the best VI–LAI models contained all VIs from all radiometric correction levels, showing the potentials of multi-radiometric correction images in LAI estimating. The results indicated that the use of VIs from multiple radiometric correction images can better exploit the capabilities of remote-sensing information, thus improving the accuracy of LAI estimating.     

机载AISA Eagle Ⅱ高光谱数据处理——以额济纳旗试验区为例

机载AISA EagleⅡ传感器为"黑河综合遥感联合试验(HiWATER)"额济纳旗试验区提供航空高光谱影像。介绍了高光谱原始数据的辐射定标、几何校正、大气校正等预处理过程。根据研究区地形差异以及数据使用目的的多样性,几何校正中可选择是否加高精度DEM产品,大气校正的选择策略可分为平坦地形无DEM的大气校正和起伏地形添加DEM大气校正。本试验数据采用加载高精度DEM的几何校正和平坦地形大气校正方法,经过预处理后的高光谱数据产品,其地理坐标与高分辨率的CCD影像对比,地理位置信息较为准确;与实测地物光谱对比,影像光谱能较好地体现地物光谱的特性,数据可用作定量遥感进一步的研究。     

Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon

Detecting and characterizing continuous changes in early forest succession using multi-temporal satellite imagery requires atmospheric correction procedures that are both operationally reliable, and that result in comparable units (e.g., surface reflectance). This paper presents a comparison of five atmospheric correction methods (2 relative, 3 absolute) used to correct a nearly continuous 20-year Landsat TM/ETM+ image data set (19-images) covering western Oregon (path/row 46/29). In theory, full absolute correction of individual images in a time-series should effectively minimize atmospheric effects resulting in a series of images that appears more similar in spectral response than the same set of uncorrected images. Contradicting this theory, evidence is presented that demonstrates how absolute correction methods such as Second Simulation of the Satellite Signal in the Solar Spectrum (6 s), Modified Dense Dark Vegetation (MDDV), and Dark Object Subtraction (DOS) actually make images in a time-series somewhat less spectrally similar to one another. Since the development of meaningful spectral reflectance trajectories is more dependant on consistent measurement of surface reflectance rather than on accurate estimation of true surface reflectance, correction using image pairs is also tested. The relative methods tested are variants of an approach referred to as “absolute-normalization”, which matches images in a time-series to an atmospherically corrected reference image using pseudo-invariant features and reduced major axis (RMA) regression. An advantage of “absolute-normalization” is that all images in the time-series are converted to units of surface reflectance while simultaneously being corrected for atmospheric effects. Of the two relative correction methods used for “absolute-normalization”, the first employed an automated ordination algorithm called multivariate alteration detection (MAD) to statistically locate pseudo-invariant pixels between each subject and reference image, while the second used analyst selected pseudo-invariant features (PIF) common to the entire image set. Overall, relative correction employed in the “absolute-normalization” context produced the most consistent temporal reflectance response, with the automated MAD algorithm performing equally as well as the handpicked PIFs. Although both relative methods performed nearly equally in terms of observed errors, several reasons emerged for preferring the MAD algorithm. The paper concludes by demonstrating how “absolute-normalization” improves (i.e., reduces scatter in) spectral reflectance trajectory models used for characterizing patterns of early forest succession.     

Reduction of atmospheric and topographic effect on Landsat TM data for forest classification

The incident radiance in forested areas with rugged terrain varies greatly with the changes in solar elevation and azimuth, slope and aspect of the terrain, and the relative position of trees. The geotropic nature must be considered in the course of topographic correction. The Sun‐Canopy‐Sensor (SCS) model is introduced to substitute the cosine correction in a physical model. We used an atmospheric simulation code, MODTRAN, and a digital elevation model (DEM) to calculate the path radiance, downwards diffuse radiance and two‐way transmittance of direct and diffuse light at different altitudes. Based on the atmospheric parameters derived above and the Lambertian assumption, surface reflectance in a forested area was retrieved from Landsat Thematic Mapper (TM) imagery using a revised physical model. Meanwhile, a smoothed DEM was used to assess the effect of noise on the DEM and misregistration between the DEM and the satellite imagery. Correlation analysis, spectral comparison between sunlit and shaded slopes and a support vector machine (SVM) classification were performed to assess the effect of the revised radiometric correction algorithm. Results indicate that the revised physical model with smoothed DEM is more adequate for forested terrain and more consistent spectra for similar vegetation under different illuminations can be obtained. Finally, higher classification accuracy of forested land can be achieved with the revised correction algorithm compared with the SCS correction and the original physical correction model.     

The integrated radiometric correction of optical remote sensing imageries

Abstract Environmental analysis, management and modelling require detailed and precise land‐use/land‐cover discrimination as initial conditions of land surface characteristics. With the ultimate goal of accurate land surface classification analysis, we devised a fully image‐based and physically based correction method (the Integrated Radiometric Correction (IRC) method) considering both the atmospheric and the topographic effects simultaneously, using the information deduced from the satellite images and 5 m resolution DEM data. The overall process is carried out in four steps: (i) calculation of the radiance/irradiance relational expression for horizontal surfaces, (ii) devising the radiance/irradiance relational expression for inclined surfaces, (iii) derivation of solar and land geometric parameters from DEM data, as well as the calculation of the topographic correction factor (A‐factor) and the atmospheric transmittance functions, and (iv) retrieval of the corrected surface reflectance and radiance. Using Landsat/ETM+ satellite data, the performance of the formulated IRC method is evaluated visually and statistically. Visual evaluation of radiometrically corrected images shows significant improvements for each band as well as for various bands composites, while the independence between the corrected surface reflectance and radiance, and the topography (incidence angle (i) or solar illumination (cos i)) is shown by very weak correlation coefficients as compared with non‐corrected data.     

基于ATCOR2模型的CBERS-02数据大气校正

根据CBERS-02与Landsat5传感器对应波段带宽设置的极度相似性和交叉定标原理,提出利用landsat5来辅助CBERS-02的大气校正。其方法为:通过逐像元回归,求解出CBERS\|02多光谱波段和TM5第1、2、3、4波段的回归系数,然后推导出CBERS\|02的辐射定标参数,最后在ERDAS中的ATCOR2模型中通过修改Landsat4_5的大气校正模块的有关参数,实现了在ATCOR2模块中对中巴资源二号卫星的大气校正。实验结果表明该方法具有现实可行性,校正后增强了影像对比度,提高了影像的清晰度,恢复了影像质量。     

High-resolution ground verification,cluster analysis and optical model of reef substrate coverage on Landsat TM imagery (Red Sea,Egypt)

A combination of high-resolution ground verification, cluster analysis using Landsat Thematic Mapper (TM) data, and optical modelling, was applied to Red Sea reef substrate. Ground verification, in an area of 3 by 20 pixels (90 by 600 m) with one metre scale resolution, identified the presence of 30 different bottom types that were later reduced to twelve dominant bottom types. A combination of bispectral plots and principal component analysis using spectral bands 1, 2 and 3 confirmed the presence of nine bottom types. The identified clusters were separated and used as a training set to classify substrate. Optical modelling, using literature radiance values and coverage of the original twelve dominant bottom types and a simple model for atmospheric and water column absorption, revealed a difference of up to 60 W m -2 between predicted substrate radiance and the satellite sensor values in the reef top area. Considering the simple atmospheric correction model, the lack of in situ radiance measurements and the uncertainties with respect to possible changes in bottom type distribution since the acquisition of the 14 year old image, the results show the potential use of satellite imagery for reef research in both biological and geological analysis through very precise and semi-quantitative ground verification, including in situ reflectance measurements.     

Cover The wake of Hurricane Felix

Analytical canopy reflectance (CR) models have reached the level of adequacy that makes it possible to estimate vegetation parameters by inversion of such models. The growing efficiency of algorithms and the increasing power of computers urge the development of procedures for the estimation of vegetation phytometrical parameters on large areas using satellite data and inversion of theoretical CR models. In this article, clusterization of a Landsat Thematic Mapper (TM) quarter scene is performed in the space of spectral signatures, and the CR model is inverted for these clusters. Optical parameters of the atmosphere which are needed for the atmospheric correction are estimated on the same image. The estimated Leaf Area Index (LAI) pattern is in good accordance to the land use map. Estimated LAI and chlorophyll content of forests are systematically biased.     

A spatially adaptive fast atmospheric correction algorithm

An atmospheric correction algorithm for high spatial resolution satellite sensors like Landsat Thematic Mapper (TM) has been developed. The algorithm works with a catalogue of atmospheric correction functions stored in look-up tables. The catalogue consists of a broad range of atmospheric conditions (different altitude profiles of pressure, air temperature, and humidity; several aerosol types; ground elevations from 0-1?km above sea level; solar zenith angles ranging from 0°-70°). The catalogue covers visibilities (surface meteorological range) from 5-40?km, values can be extrapolated down to 4?km and up to 80km. The 1994 edition of the catalogue was compiled using the MODTRAN-2 and the SENSAT-5 codes.

The algorithm consists of an interactive and an automatic part. The interactive phase serves for the definition of a reference target (dense dark vegetation or water) as well as haze and cloud. The reflectance of the reference target in a single spectral band (dark vegetation: TM band 3; water: TM band 4) has to be specified. Additionally, the image can be partitioned into sub-images, called sectors. This phase also selects one of the atmospheres available in the catalogue, i.e. the altitude profile of pressure, temperature and humidity as well as the aerosol type (e.g. rural) are fixed.

The automatic phase first calculates the visibility of the reference areas for the selected atmosphere. The visibility is obtained by matching the measured signal (i.e the digital number (DN) converted to a radiance using the sensor calibration) to the model-derived signal in the spectral channel of known target reflectance. The sector-average visibility of the reference pixels is assigned to the non-reference pixels. The second step is the haze removal performed by histogram matching the statistics of the haze regions to the statistics of the clear part of the scene for each sector and each channel. The last step is the calculation of the ground reflectance image including the adjacency correction, and the computation of the ground brightness temperature image (TM band 6)     

Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction

ABSTRACT

Visible near-infrared and shortwave infrared data acquired by spaceborne sensors contain atmospheric noise, along with target reflectance that may affect its end applications, e.g. geological, vegetation, soil surface studies, etc. Several atmospheric correction algorithms have been already developed to remove unwanted atmospheric components of a spectral signature of Earth targets obtained from airborne/spaceborne hyperspectral image. In spite of this, choosing of an appropriate atmospheric correction algorithm is an ongoing research. In this study, two hybrid atmospheric correction (HAC) algorithms incorporating a modified empirical line (EL_m) method were proposed. The first HAC model (named HAC_1) combines (i) a radiative transfer (RT) model based on the concepts of RT equations, which uses real-time in situ atmospheric and climatic data, and (ii) an EL_m technique. The second one (named HAC_2) combines (i) the well-known ATmospheric CORrection (ATCOR) model and (ii) an EL_m technique. Both HAC algorithms and their component single atmospheric correction algorithms (ATCOR, RT, and EL_m) were applied to radiance data acquired by Hyperion satellite sensor over study sites in Australia. The performances of both HAC algorithms were analysed in two ways. First, the Hyperion reflectances obtained by five atmospheric correction algorithms were analysed and compared using spectral metrics. Second, the performance of each atmospheric correction algorithm was analysed for prediction of soil organic carbon (SOC) using Hyperion reflectances obtained from atmospheric correction algorithms. The prediction model of SOC was built using partial least square regression model. The results show that (i) both the hybrid models produce a good spectrum with lower Spectral Angle Mapper and Spectral Information Divergence values and (ii) both hybrid algorithms provided better SOC prediction accuracy, in terms of coefficient of determination (R²), residual prediction deviation (RPD), and ratio of performance to interquartile (RPIQ), with R² ≥ 0.75, RPD ≥ 2, and RPIQ ≥ 2.58 than single algorithms. HAC algorithms, developed using EL_m technique, may be recommended for atmospheric correction of Hyperion radiance data, when archived Hyperion reflectance data have to be used for SOC prediction mapping.     

Technical Note Simulation of MEIS-II data on 5S code to quantify algal concentration in an intertidal area

An atmospheric correction algorithm due to Deschamps et al. (1981) has been applied to MEIS-II data. Some atmospheric conditions such as continental and maritime aerosol models have been used in this work. Simulation on 5S code was made with Thematic Mapper Band 4 and Spot band 3. All these pieces of the puzzle permitted the qualification of algal concentration in an intertidal area, which was the aim of this work. Continental or maritime aerosol models gave a similar result due probably to the specific area, located between land and nearshore. Apparent radiance is smaller than corrected radiance because absorption process takes place in this part of the spectrum (0.8 to 0.9μm). When apparent radiance is higher than 30 Wm^?2 sr^?1 μm^?1 algal concentration is overestimated by up to 60 per cent.     

Atmospheric correction of Hyperion imagery over estuarine waters: a case study of the Pearl River Estuary in southern China

Atmospheric correction of ocean colour remote-sensing data is based on the assumption that no water-leaving reflectance occurs in the near-infrared (NIR) area. However, this assumption is not valid for highly productive waters. To solve this problem, this paper describes a modified atmospheric correction scheme for Hyperion data. Based on the assumption that the ratio of water-leaving radiance and aerosol radiance in two NIR bands follows a fixed rule, we moved a rectangular box around the imagery to calculate those two parameters, which were then used to replace the assumption of zero water-leaving radiance in the NIR. We applied the new atmospheric correction algorithm to one Hyperion image. Following comparison of in situ measurements to results of the FLAASH atmospheric correction schema, preliminary findings show that the new algorithm is effective in reducing error in retrieved water-leaving radiance values, to some extent.     

The role of atmospheric correction algorithms in the prediction of soil organic carbon from Hyperion data

In this study, the role of atmospheric correction algorithm in the prediction of soil organic carbon (SOC) from spaceborne hyperspectral sensor (Hyperion) visible near-infrared (vis-NIR, 400–2500 nm) data was analysed in fields located in two different geographical settings, viz. Karnataka in India and Narrabri in Australia. Atmospheric correction algorithms, (1) ATmospheric CORection (ATCOR), (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), (3) 6S, and (4) QUick Atmospheric Correction (QUAC), were employed for retrieving spectral reflectance from radiance image. The results showed that ATCOR corrected spectra coupled with partial least square regression prediction model, produced the best SOC prediction performances, irrespective of the study area. Comparing the results across study areas, Karnataka region gave lower prediction accuracy than Narrabri region. This may be explained due to difference in spatial arrangement of field conditions. A spectral similarity comparison of atmospherically corrected Hyperion spectra of soil samples with field-measured vis-NIR spectra was performed. Among the atmospheric correction algorithms, ATCOR corrected spectra found to capture the pattern in soil reflectance curve near 2200 nm. ATCOR’s finer spectral sampling distance in shortwave infrared wavelength region compared to other models may be the main reason for its better performance. This work would open up a great scope for accurate SOC mapping when future hyperspectral missions are realized.     

基于Landsat 8卫星影像的北京地区土地覆盖分类

目的 土地覆盖分类能为生态系统模型、水资源模型和气候模型等提供重要信息,遥感技术运用于土地覆盖分类具有诸多优势。作为区域性土地覆盖分类应用的重要数据源,Landsat 5/7的TM和ETM+等数据已逐渐失效,Landsat 8陆地成像仪(OLI)较TM和ETM+增加了新的特性,利用Landsat 8数据进行北京地区土地覆盖分类研究,探讨处理方法的适用性。方法 首先,确定研究区域内土地覆盖分类系统,并对Landsat 8多光谱数据进行预处理,包括大气校正、地形校正、影像拼接及裁剪;然后,利用灰度共生矩阵提取全色波段纹理信息,与多光谱数据进行融合;最后,使用支持向量机(SVM)进行分类,获得土地覆盖分类结果。结果 经过精度评价和分析发现,6S模型大气校正和C模型地形校正预处理提高了不同类别之间的可分性,多光谱数据结合全色波段纹理特征能有效提高部分地物的土地覆盖分类精度,总体精度提高2.8%。结论 相对于Landsat TM/ETM+数据,Landsat 8 OLI数据新增特性有利于土地覆盖分类精度的提高。本文方法适用于Landsat 8 OLI数据土地覆盖分类研究与应用,能够满足大区域土地覆盖分类应用需求。