共查询到20条相似文献，搜索用时 31 毫秒
1.
Pixel-based image compositing enables production of large-area surface reflectance images that are largely devoid of clouds, cloud shadows, or haze. Change detection with spectral trend analysis uses a dense time series of images, such as pixel-based composites, to quantify the year, amount, and magnitude of landscape changes. Topographically-related shadows found in mountainous terrain may confound trend-based forest change detection approaches. In this study, we evaluate the impact of topographic correction on trend-based forest change detection outcomes by comparing the amount and location of changes identified on an image composite with and without a topographic correction. Moreover, we evaluated two different approaches to topographic correction that are relevant to pixel-based image composites: the first corrects each pixel according to the day of year (DOY) the pixel was acquired, whilst the second corrects all pixels to a single reference date (August 1st), which was also the target date for generating the pixel-based image composite. Our results indicate that a greater area of change is detected when no topographic correction is applied to the image composite, however, the difference in change area detected between no correction and either the DOY or the August 1st correction is minor and less than 1% (0.54–0.85%). The spatial correspondence of these different approaches is 96.2% for the DOY correction and 97.7% for the August 1st correction. The largest differences between the correction processes occur in valleys (0.71–1.14%), upper slopes (0.71–1.09%), and ridges (0.73–1.09%). While additional tests under different conditions and in other environments are encouraged, our results indicate that topographic correction may not be justified in change detection routines computing spectral trends from pixel-based composites. 相似文献
2.
Mapping of vegetation in mountain areas based on remote sensing is obstructed by atmospheric and topographic distortions. A variety of atmospheric and topographic correction methods has been proposed to minimize atmospheric and topographic effects and should in principle lead to a better land cover classification. Only a limited number of atmospheric and topographic combinations has been tested and the effect on class accuracy and on different illumination conditions is not yet researched extensively. The purpose of this study was to evaluate the effect of coupled correction methods on land cover classification accuracy. Therefore, all combinations of three atmospheric (no atmospheric correction, dark object subtraction and correction based on transmittance functions) and five topographic corrections (no topographic correction, band ratioing, cosine correction, pixel-based Minnaert and pixel-based C-correction) were applied on two acquisitions (2009 and 2010) of a Landsat image in the Romanian Carpathian mountains. The accuracies of the fifteen resulting land cover maps were evaluated statistically based on two validation sets: a random validation set and a validation subset containing pixels present in the difference area between the uncorrected classification and one of the fourteen corrected classifications. New insights into the differences in classification accuracy were obtained. First, results showed that all corrected images resulted in higher overall classification accuracies than the uncorrected images. The highest accuracy for the full validation set was achieved after combination of an atmospheric correction based on transmittance functions and a pixel-based Minnaert topographic correction. Secondly, class accuracies of especially the coniferous and mixed forest classes were enhanced after correction. There was only a minor improvement for the other land cover classes (broadleaved forest, bare soil, grass and water). This was explained by the position of different land cover types in the landscape. Finally, coupled correction methods showed most efficient on weakly illuminated slopes. After correction, accuracies in the low illumination zone (cos β ≤ 0.65) were improved more than in the moderate and high illumination zones. Considering all results, best overall classification results were achieved after combination of the transmittance function correction with pixel-based Minnaert or pixel-based C-topographic correction. Furthermore, results of this bi-temporal study indicated that the topographic component had a higher influence on classification accuracy than the atmospheric component and that it is worthwhile to invest in both atmospheric and topographic corrections in a multi-temporal study. 相似文献
3.
机载多光谱LiDAR数据的地物分类方法 总被引：2，自引：1，他引：1
机载多光谱LiDAR系统能够快速地获取大范围地表面上地物光谱和几何数据,并能够保证所获取的光谱与空间几何数据在空间和时间上相对完整和一致性。支持向量机（SVM）是一种基于小样本的学习方法,它避开了从归纳到演绎的传统分类过程。因此,本文提出了基于SVM多光谱LiDAR数据的地物目标分类方法。该方法首先将多个独立波段的LiDAR数据融合为单一的、包含多个波段信息的点云数据,然后将融合后的点云内插为距离影像和多光谱影像,最后利用SVM进行多光谱LiDAR数据的地物覆盖分类。通过对加拿大Optech公司的Titan机载多光谱LiDAR数据的试验证明：相对于传统的单波段LiDAR数据,多光谱LiDAR数据可以获得较好的地物分类精度;比较试验发现SVM分类方法适用于多光谱LiDAR数据的地物分类。 相似文献
4.
Canopy shadowing mediated by topography is an important source of radiometric distortion on remote sensing images of rugged terrain. Topographic correction based on the sun–canopy–sensor (SCS) model significantly improved over those based on the sun–terrain–sensor (STS) model for surfaces with high forest canopy cover, because the SCS model considers and preserves the geotropic nature of trees. The SCS model accounts for sub-pixel canopy shadowing effects and normalizes the sunlit canopy area within a pixel. However, it does not account for mutual shadowing between neighboring pixels. Pixel-to-pixel shadowing is especially apparent for fine resolution satellite images in which individual tree crowns are resolved. This paper proposes a new topographic correction model: the sun–crown–sensor (SCnS) model based on high-resolution satellite imagery (IKONOS) and high-precision LiDAR digital elevation model. An improvement on the C-correction logic with a radiance partitioning method to address the effects of diffuse irradiance is also introduced (SCnS + C). In addition, we incorporate a weighting variable, based on pixel shadow fraction, on the direct and diffuse radiance portions to enhance the retrieval of at-sensor radiance and reflectance of highly shadowed tree pixels and form another variety of SCnS model (SCnS + W). Model evaluation with IKONOS test data showed that the new SCnS model outperformed the STS and SCS models in quantifying the correlation between terrain-regulated illumination factor and at-sensor radiance. Our adapted C-correction logic based on the sun–crown–sensor geometry and radiance partitioning better represented the general additive effects of diffuse radiation than C parameters derived from the STS or SCS models. The weighting factor Wt also significantly enhanced correction results by reducing within-class standard deviation and balancing the mean pixel radiance between sunlit and shaded slopes. We analyzed these improvements with model comparison on the red and near infrared bands. The advantages of SCnS + C and SCnS + W on both bands are expected to facilitate forest classification and change detection applications. 相似文献
5.
包络线去除的丘陵地区遥感影像阴影信息重建 总被引：1，自引：0，他引：1
中国西南丘陵常态山和喀斯特山交错分布,遥感影像普遍存在山体阴影,分布零散且无规律,基于DEM的地形校正模型(C校正等)虽然算法成熟、易于操作,但在复杂地形区存在误差。引入基于相似像元包络线的阴影校正方法(CR校正),按照阴影提取、包络线去除、相似像元寻找和阴影亮度重建的步骤,采用西南丘陵地区Landsat 8 OLI影像进行验证实验。结果表明:CR校正后,阴影区的视觉特征与邻近非阴影区趋于一致,阴影像元亮度有明显提升;校正后影像主要波段标准差减小,与非阴影区参考光谱的相对均方根误差在2.919%以内,最低仅为0.516%;自动分类精度从43.59%提高到61.57%,CR校正有效提高了有阴影的丘陵地区遥感影像质量。 相似文献
6.
Arseny Zhogolev 《国际地球制图》2018,33(6):573-586
The digital elevation model based on SRTM is very convenient for a wide range of studies but requires correction due to the influence of forest vegetation. The present study was conducted to analyse the effect of boreal forests on altitudes, aspects and slopes calculated from the SRTM. A DEM based on topographic maps at 1:100 000 scale was used as a reference. The linear regression analysis showed low data correlation in forested areas. The presence of different types of forests and felling in the woods leads to a complex distribution of deviations from the SRTM. A simple correction method was proposed, using a forest mask, built according to Landsat, and forest heights indicated on the topographic maps. After correction, the correlation coefficient between the altitudes increased by 0.05–0.14, the share of matching aspects by 1–4% and the share of matching slopes by 2–8%. 相似文献
7.
SCS+C地形辐射校正模型的应用分析研究 总被引：1，自引：0，他引：1
在对有森林覆盖的山区影像进行地形辐射校正时，基于太阳-冠层-传感器（SCS）几何关系的校正模型优于基于太阳-地形-传感器（STS）几何关系的模型。SCS校正模型解释了树木不依赖于地形、观测角和光照入射角而具有向地性生长的本质特性，但在某些地形区域，SCS与余弦校正同样存在过度校正的问题。为了解决这个问题，研究者在SCS校正模型中引入C校正系数来解释散射辐射项，提出了SCS+C校正模型。以北京密云Landsat 5影像为数据源，通过目视判别、直方图、定量的统计参数和地物光谱曲线对比等方法，对SCS+C校正模型与传统的余弦校正、C校正和SCS校正模型进行了对比。结果表明，4种方法均能在很大程度上消除地形阴影，更好地反映阴影区域的细节信息； 从总体的光谱特性保真程度来说，余弦和SCS校正都因过度校正问题表现较差，SCS+C校正最好，C校正次之。 相似文献
8.
Liegang Xia Jiancheng Luo Weihong Wang Zhanfeng Shen 《Journal of the Indian Society of Remote Sensing》2014,42(3):505-515
This paper proposes an automatic framework for land cover classification. In majority of published work by various researchers so far, most of the methods need manually mark the label of land cover types. In the proposed framework, all the information, like land cover types and their features, is defined as prior knowledge achieved from land use maps, topographic data, texture data, vegetation’s growth cycle and field data. The land cover classification is treated as an automatically supervised learning procedure, which can be divided into automatic sample selection and fuzzy supervised classification. Once a series of features were extracted from multi-source datasets, spectral matching method is used to determine the degrees of membership of auto-selected pixels, which indicates the probability of the pixel to be distinguished as a specific land cover type. In order to make full use of this probability, a fuzzy support vector machine (SVM) classification method is used to handle samples with membership degrees. This method is applied to Landsat Thematic Mapper (TM) data of two areas located in Northern China. The automatic classification results are compared with visual interpretation. Experimental results show that the proposed method classifies the remote sensing data with a competitive and stable accuracy, and demonstrate that an objective land cover classification result is achievable by combining several advanced machine learning methods. 相似文献
9.
Abstract Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA. 相似文献
10.
ABSTRACTForest fires can change forest structure and composition, and low-density Airborne Laser Scanning (ALS) can be a valuable tool for evaluating post-fire vegetation response. The aim of this study is to analyze the structural diversity differences in Mediterranean Pinus halepensis Mill. forests affected by wildfires on different dates from 1986 to 2009. Several types of ALS metrics, such as the Light Detection and Ranging (LiDAR) Height Diversity Index (LHDI), the LiDAR Height Evenness Index (LHEI), and vertical and horizontal continuity of vegetation, as well as topographic metrics, were obtained in raster format from low point density data. In order to map burned and unburned areas, differentiate fire occurrence dates, and distinguish between old and more recent fires, a sample of pixels was previously selected to assess the existence of differences in forest structure using the Kruskal–Wallis test. Then, k-nearest neighbors algorithm (k-NN), support vector machine (SVM) and random forest (RF) classifiers were compared to select the most accurate technique. The results showed that, in more recent fires, around 70% of the laser returns came from grass and shrub layers, yielding low LHDI and LHEI values (0.37–0.65 and 0.28–0.46, respectively). In contrast, the areas burned more than 20 years ago had higher LHDI and LHEI values due to the growth of the shrub and tree strata. The classification of burned and unburned areas yielded an overall accuracy of 89.64% using the RF method. SVM was the best classifier for identifying the structural differences between fires occurring on different dates, with an overall accuracy of 68.79%. Furthermore, SVM yielded an overall accuracy of 75.49% for the classification between old and more recent fires. 相似文献
11.
Monitoring Forest Disturbance in Lesser Khingan Mountains Using MODIS and Landsat TM Time Series from 2000 to 2011 总被引：1，自引：0，他引：1
Lingxue Yu Tingxiang Liu Kun Bu Jiuchun Yang Shuwen Zhang 《Journal of the Indian Society of Remote Sensing》2017,45(5):837-845
The widespread changes in forest cover caused by climatological and anthropogenic factors can influence the forest ecosystem and climate system to a great extent. With the increasing availability of remote sensing data, monitoring of forest changes at high temporal resolution and on various scales is becoming more realistic. Though several methods based on time series data have been used to detect forest disturbance, there are few studies paying attention to boreal areas where the forest is significant in regulating the global carbon cycle and biogeophysical processes. In this paper, we present a robust method of Breaks Detection Based On Polynomial Model (BDPM) to track boreal (e.g. Lesser Khingan Mountains) deforestation and forest fires based on the MODIS and Landsat TM time series data. Compared with the previous methods, the BDPM offers the following advantages: (1) Fitting of the polynomial model using the seasonal variation of forests in the whole region instead of a single pixel to avoid error accumulation; (2) to avoid confusion between vegetation change due to climate changes and abrupt forest disturbances, we segmented the long-time NDVI series data into 12 seasonal cycles and simulated the temporal variations in each seasonal cycle. 相似文献
12.
地形校正可以减小地形起伏对地物光谱的影响,提高计算机分类在山区的精度。设计了针对全球土地覆盖分类的Landsat TM/ETM+数据地形校正方法 SCOS(Smoothed COS余弦),首先对地形的坡度角进行抹平处理,很大程度上削弱了地表非朗伯性对地形校正的影响,然后利用简单有效的余弦校正去除地形效应。该方法与其他常用地形校正算法的对比分析是通过对全球不同区域、不同地表覆盖的有代表性的6景Landsat TM/ETM+数据的试验,采用统计分析与目视判读的方式,从过度校正和类内均一性两个方面进行的。结果表明,该方法在目视效果和统计结果上优于常规方法,并且更加简单有效,无需复杂的大气参数及传感器参数,满足全球地表覆盖分类对地形校正的需求。 相似文献
13.
Automatic extraction of urban features from high resolution satellite images is one of the main applications in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping, disaster management, GIS (geographic information systems) updating, and military target detection. One common approach to detecting urban features from high resolution images is to use automatic classification methods. This paper has four main objectives with respect to detecting buildings. The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the primary consideration is the impact of kernel configuration on the performance of the SVM. The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches. The performance evaluation of classification results is done using two different accuracy assessment methods: pixel based and object based approaches, which reflect the third aim of the study. The objective here is to demonstrate the differences in the evaluation of accuracies of classification methods. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluate variation in the accuracy of classifiers for six different real situations in order to identify the impact of spatial and spectral diversity on results. The method is applied to Quickbird images for various urban complexity levels, extending from simple to complex urban patterns. The simple surface type includes a regular urban area with low density and systematic buildings with brick rooftops. The complex surface type involves almost all kinds of challenges, such as high dense build up areas, regions with bare soil, and small and large buildings with different rooftops, such as concrete, brick, and metal.Using the pixel based accuracy assessment it was shown that the percent building detection (PBD) and quality percent (QP) of the MLC and SVM depend on the complexity and texture variation of the region. Generally, PBD values range between 70% and 90% for the MLC and SVM, respectively. No substantial improvements were observed when the SVM and MLC classifications were developed by the addition of more variables, instead of the use of only four bands. In the evaluation of object based accuracy assessment, it was demonstrated that while MLC and SVM provide higher rates of correct detection, they also provide higher rates of false alarms. 相似文献
14.
GIS支持下滑坡灾害空间预测方法研究 总被引：11，自引：0，他引：11
滑坡预测在防灾减灾工作中具有重要意义,它包括空间、时间预测两个方面。基于统计模型进行区域评价与空间预测是滑坡灾害研究的重要方向,但是预测结果往往依赖样本数量和空间分布等。本文以马来西亚金马伦高原为研究区,选择高程、坡度、坡向、地表曲率、构造、土地覆盖、地貌类型、道路和排水系统作为评价因子,探讨运用地理信息系统(GIS)和遥感(RS)获取与管理滑坡灾害信息,以及热带雨林地区湿热环境下滑坡空间预测的方法。支持向量机(SVM)和逻辑(Logistic)回归模型分别应用于滑坡空间预测,结果显示平均预测精度分别为95.9%和86.2%,SVM法具有较高的描述精度,值得推荐;同时,基于SVM模型的滑坡空间预测受样本影响较小,预测结果相对比较稳定,这对于滑坡灾害区域评价与预测的快速实现具有实际意义。 相似文献
15.
Full-waveform topographic LiDAR data provide more detailed information about objects along the path of a laser pulse than discrete-return (echo) topographic LiDAR data. Full-waveform topographic LiDAR data consist of a succession of cross-section profiles of landscapes and each waveform can be decomposed into a sum of echoes. The echo number reveals critical information in classifying land cover types. Most land covers contain one echo, whereas topographic LiDAR data in trees and roof edges contained multi-echo waveform features. To identify land-cover types, waveform-based classifier was integrated single-echo and multi-echo classifiers for point cloud classification.The experimental area was the Namasha district of Southern Taiwan, and the land-cover objects were categorized as roads, trees (canopy), grass (grass and crop), bare (bare ground), and buildings (buildings and roof edges). Waveform features were analyzed with respect to the single- and multi-echo laser-path samples, and the critical waveform features were selected according to the Bhattacharyya distance. Next, waveform-based classifiers were performed using support vector machine (SVM) with the local, spatial features of waveform topographic LiDAR information, and optical image information. Results showed that by using fused waveform and optical information, the waveform-based classifiers achieved the highest overall accuracy in identifying land-cover point clouds among the models, especially when compared to an echo-based classifier. 相似文献
16.
In Canada, fire danger is rated by the Canadian forest fire danger rating system (CFFDRS). One of its components is the fire weather index (FWI) system, which has among others the drought code (DC). DC is used here as a surrogate of dead forest fuel moisture. DC values were computed from weather data acquired between 1993 and 1999 and compared to 10-day composite NOAA-AVHRR images acquired over Canadian northern boreal forests. They were yearly correlated with single compositing period and cumulative NDVI and surface temperature (ST) NOAA-AVHRR data. Correlations with cumulative spectral variables were stronger than with single compositing period variables and the best correlations occurred for the spring compositing periods (R between 0.57 and 0.80). Spring DC models using both single compositing period and cumulative spectral variables were established. Surface temperature-based indices were more often used in the models than NDVI-based indices. The models were stronger for dry or normal years than for wet years. Limitations and possible improvements of the models are discussed. 相似文献
17.
Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics 总被引：1，自引：0，他引：1
Catherine BodartHugh Eva René Beuchle Rastislav RašiDario Simonetti Hans-Jürgen StibigAndreas Brink Erik LindquistFrédéric Achard 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):555-563
In support to the Remote Sensing Survey of the global Forest Resource Assessment 2010, the TREES-3 project has processed more than 12,000 Landsat TM and ETM+ data subsets systematically distributed over the tropics. The project aims at deriving area estimates of tropical forest cover change for the periods 1990-2000-2005. The paper presents the pre-processing steps applied in an operational and robust manner to this large amount of multi-date and multi-scene imagery: conversion to top-of-atmosphere reflectance, cloud and cloud shadow detection, haze correction and image radiometric normalization. The results show that the haze correction algorithm has improved the visual appearance of the image and significantly corrected the digital numbers for Landsat visible bands, especially the red band. The impact of the normalization procedures (forest normalization and relative normalization) was assessed on 210 image pairs: in all cases the correlation between the spectral values of the same land cover in both images was improved. The developed automatic pre-processing chain provided a consistent multi-temporal data set across the tropics that will constitute the basis for an automatic object-based supervised classification. 相似文献
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To have sustainable management and proper decision-making, timely acquisition and analysis of surface features are necessary. Traditional pixel-based analysis is the popular way to extract different categories, but it is not comparable by the achievements that can be achieved through the object-based method that uses the additional characteristics of features in the process of classification. In this paper, three types of classification were used to classify SPOT 5 satellite image in mapping land cover; Support vector machine (SVM) pixel-based, SVM object-based and Decision Tree (DT) pixel-based classification. Normalised Difference Vegetation Index and the brightness value of two infrared bands (NIR and SWIR) were used in manually developed DT classification. The classification of the SVM (pixel based) was generated using the selected groups of pixels that represent the selected features. In addition, the SVM (object based) was implemented by using radial-based function kernel. The classified features were oil palm, rubber, urban area, soil, water and other vegetation. The study found that the overall classification of the DT was the lowest at 69.87% while those of SVM (pixel based) and SVM (object based) were 76.67 and 81.25%, respectively. 相似文献