面向多尺度影像的瓦片金字塔技术研究与实现

面向多尺度影像研究了基于不规则切片的多尺度影像金字塔构建算法,利用多种分辨率的遥感影像统一构建影像金字塔,同时支持任意长宽比例的影像切片,使得影像金字塔的构建具有更强的自适应性,并在.NET4以及VS2008的开发环境中对多尺度影像金字塔的构建算法进行了程序实现,算法中的各项参数都可以根据实际情况自主选择。最后对算法进行了实验,对比分析了规则与不规则2种切片方式,并对算法在效果、效率等方面进行了实验和分析。     

基于多分辨率分析的灰度调制影像融合方法研究

在遥感图像处理中,由于应用的需要,已经有很多算法可以用来融合高分辨率的全色影像和低分辨率的多光谱影像。本文阐述了小波变换的Mallat算法和a′Trous（多孔）算法,在此基础上,作者提出了一种基于多分辨率分析的灰度调制影像融合方法（MRAGM算法）,以SPOT全色影像与TM多光谱影像融合为例,详细给出了算法和解算步骤,最后分别用三种方法对SPOT高分辨率全色影像和TM低分辨率多光谱影像进行了融合,并且对融合后的影像从定性和定量方面都进行了评价,可以得出MRAGM方法比Mallat算法和a′Trous（多孔）算法的融合效果要好。     

基于改进ASIFT算法倾斜影像匹配研究

传统ASIFT算法在匹配时效率低,根据影像匹配相关知识及倾斜影像的特点,本文提出一种对原始影像进行一次逆仿射变换,将倾斜影像恢复到近似的正摄影像的改进ASIFT算法,使影像匹配时精度和效率上得到了明显的提高。改进算法在试验中与传统ASIFT算法匹配效果在精度上更好,且匹配效率有大幅度提高。     

基于改进的区域增长算法的高分辨率影像分割

提出了一种基于区域增长的影像分割改进算法;该算法可以在区域增长的基础上进行区域合并和平滑最终改善影像分割的效果。通过实验结果显示:该算法对于高分辨率遥感影像分割效果较好。     

低空无人机影像的快速拼接技术研究

对低空无人机影像的快速拼接技术进行研究,采用SIFT算法和SURF算法对尺度不变特征进行提取,根据特征描述符间的欧氏距离进行特征匹配,并使用RANSAC算法对匹配中产生的误匹配点对进行剔除,然后利用单应性矩阵实现无人机的影像拼接。实验结果表明,SIFT算法和SURF算法均能较好地对影像特征进行提取,SURF算法在效率上更优,RANSAC算法的剔除效果较好,能够得到良好的拼接影像。     

利用特征组合检测算法的无人机遥感影像匹配研究

影像匹配是无人机遥感影像拼接和三维建模的基础和关键步骤。结合不同算法的优势，本文提出一种基于特征组合与RANSAC算法的无人机遥感影像匹配方法。该匹配方法首先采用AKAZE算法检测影像的特征点，然后利用SIFT描述符描述特征向量并获取特征点的主方向，最后基于单映射变换矩阵的RANSAC算法进行精准匹配。本文对基于特征组合与RANSAC算法的匹配效果进行了试验对比分析，试验结果表明：与常用匹配方法的匹配效果相比，本文的匹配方法继承了AKAZE算法的快速匹配能力，匹配总耗时介于AKAZE算法和SIFT算法之间，约为BRISK算法匹配耗时的20%；同时，该匹配方法继承了SIFT算法的多匹配点对性能，从整体匹配效果来看，本文的匹配方法优于AKAZE、SIFT、BRISK算法。     

一种无人机影像滤波分频拼接算法

针对无人机影像拼接处理中易产生鬼影、拼接缝等问题,提出一种基于滤波分频的无人机影像拼接算法。首先利用高斯低通滤波将已配准的无人机影像对分解成高频、低频影像;然后对低频影像采用加权平滑融合算法进行拼接,基于改进的动态规划算法搜索最优拼接线完成高频影像的拼接;最后将拼接好的高频、低频影像线性合成得到最终的拼接影像。实验表明,本文提出的算法可以较好地解决无人机影像拼接过程中出现的鬼影问题,最大化地避免拼接缝的出现,而且对亮度差异明显的无人机影像对也能取得良好的拼接效果。     

基于小波影像金字塔的影像自动匹配

数字影像高精度快速匹配是遥感影像3维信息提取的关键技术之一，文中对如何实现数字影像自动化匹配进行了研究，提出了一种基于小波影像金字塔的数字影像自动匹配方法。该方法在生成小波影像金字塔的基础上，利用交叉相关算法实现不同影像金字塔同一层影像间的匹配，利用视差控制将同名点从金字塔高层影像向低层影像进行传递。利用该方法对具体的SPOT影像进行了实验，表明基于小波影像金字塔的数字影像自动匹配方法具有较好的效果。     

Wallis变换在小波影像融合中的应用

介绍了一种基于Ｗａｌｌｉｓ变换的影像增强方法在影像融合中的应用。通过分析线性拉抻、直方图均衡拉伸和Ｗａｌｌｉｓ变换３种影像增强方法对多孔算法中小波系数分量的影响,得出对原始的高分辨率影像进行线性和直方图均衡性拉伸后和多光谱影像的融合效果与直接用原始高分辨率影像进行融合的效果差别不大,增强算法本身没有提高融合的效果,而基于Ｗａｌｌｉｓ变换的影像增强方法可以较好地把携带细节信号的小波分量表现出来,有效     

成像光谱仪影像的几种处理方法

针对成像光谱仪影像的专题分析，介绍了沿用常规遥感影像分析方法时的原理和新的算法，提出了影像融合方法的应用，说明了这些方法在学术思想上的归属，并简要介绍了各种方法的实验效果。     

MODIS NDVI时间序列数据的去云算法比较

受多重因素的影响,MODIS NDVI数据产品中存在着大量的噪声,需要进行去噪重建.针对目前几种常用的NDVI时间序列数据去云方法,如HANTS法、SPLINE插值法以及Savizky-Golay法,以山东省MODIS NDVI时间序列数据(一年的)作为检验数据,从不同角度比较几种算法的去云能力和使用范围.结果表明:S...     

An Agent-Based Approach for Regional Forest Fire Detection Using MODIS Data: A Preliminary Study in Iran

Detecting fires, which are at their early stages is the first component of effective fire fighting. To date, several algorithms have been proposed to detect fire spots using remote sensing data. Nevertheless, in order to be able to accurately detect small and cool fires, which are very important at the regional scale, most of these algorithms need to be adjusted and improved. In this paper, an agent-based algorithm is presented for regional forest fire detection using bi-temporal MODIS data. The algorithm is designed to be so self-adaptive and consistent that it could be applied to the different pairs of consecutive images taken by the same satellite platform and at the same daytime. The results clearly show that compared with the MODIS contextual algorithm (version 4), the proposed method is more sensitive to small and cool forest fires in Iran.     

Estimation of regional evapotranspiration over the North China Plain using geostationary satellite data

Data from the first operational Chinese geostationary satellite Fengyun-2C (FY-2C) satellite are applied in combination with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products for the assessment of regional evapotranspiration over the North China Plain. The approach is based on the improved triangle method, where the temperature–vegetation index space includes thermal inertia. Two thermal infrared channels from FY-2C are used to estimate surface temperature (Ts) based on a split window algorithm originally proposed for the MSG-SEVIRI sensor. Subsequently the high temporal resolution of FY-2C data is exploited to give the morning rise in Ts. Combined with the 16 days composite MODIS vegetation indices product (MOD13) at a spatial resolution of 5 km, evaporative fraction (EF) is estimated by interpolation in the ΔTs–NDVI triangular-shaped scatter space. Finally, regional actual evapotranspiration (ET) is derived from the evaporative fraction and available energy estimated from MODIS surface albedo products MCD43. Spatial variations of estimated surface variables (Ts, EF and ET) corresponded well to land cover patterns and farmland management practices. Estimated ET and EF also compared well to lysimeter data collected for the period June 2005–September 2007. The improved triangle method was also applied to MODIS products for comparison. Estimates based on FY-2C products proved to provide slightly better results than those based on MODIS products. The consistency of the estimated spatial variation with other spatial data supports the use of FY-2C data for ET estimation using the improved triangle method. Of particular value is the high temporal frequency of image acquisitions from FY-2C which improves the likelihood of obtaining cloud free image acquisitions as compared to polar orbiting sensors like MODIS.     

基于MODIS遥感数据的水体提取算法研究

针对MODIS遥感数据采用多波段普间关系算法提取水体容易与阴影混淆,产生提取不精确的问题。作者将对多波段普间关系水体提取算法进行研究,并以鄱阳湖为实验对象,使用改进后的多波段普间关系算法对水体进行提取。实现提升水体提取精度的目标。     

A new method for generating a clear-sky Landsat composite for cropland from cloud-contaminated Landsat-7 and Landsat-8 images

A new method was developed in this study for producing a clear-sky Landsat composite for cropland from cloud-contaminated Landsat images acquired in a short time period. It used Thiel–Sen regression to normalize all Landsat scenes to a MODIS image to make all Landsat images radiometrically consistent and comparable. Pixel selection criteria combining the modified maximum vegetation index and the modified minimum visible reflectance selection methods were designed to enhance the pixel selection of land/water over cloud/shadow in the image compositing. The advantages of the method include (1) avoiding complicated atmospheric corrections but with reliable surface reflectance results, (2) being insensitive to errors induced by image co-registration uncertainties between Landsat and MODIS images, (3) avoiding the lack of samples for the regression analysis using the full Landsat scenes (rather than overlay regions), and (4) enhancing cloud/shadow detection. The composite image has MODIS-like surface reflectance, thus making MODIS algorithms applicable for retrieving biophysical parameters. The method was automatically implemented on a set of 13 cloud-contaminated (>39%) Landsat-7 (Scan-Line Corrector-Off) and Landsat-8 scenes acquired during peak growing season in a crop region of Manitoba, Canada. The result was a 95.8% cloud-free image. The method can also substantially increase the usage of cloud-contaminated Landsat data.     

青藏高原那曲地区MODIS 地表温度估算

地表温度是区域和全球尺度陆面过程研究中的一个关键参数,利用遥感卫星资料反演得到的地表温度数据在气象、水文和生态领域研究中有重要作用.本文基于改进后的针对MODIS 数据的分裂窗口算法,对MODIS L1B 卫星数据进行实用而简便的云检测处理,并根据青藏高原陆地、水体和冰雪等常见下垫面状况的遥感影像分类结果,反演得到了2007-01-03 、04-18 、06-12 和10-02 四日的无云下垫面地表温度.最后,将Sobrino 结果在青藏高原那曲地区与MODIS 日地表温度产品及CAMP/Tibet 观测站地表温度数据进行了对比验证分析.结果表明,该方法得到的地表温度结果与MODIS 数据产品具有较好的一致性,并且地表温度结果与地面观测数据(去除可疑点后)的平均误差仅为1.435 K .     

The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia

Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.     

Improving satellite image classification by using fractional type convolution filtering

This letter shows how conventional methods for satellite image classification can be improved by applying some filtering algorithms as a pre-classifying step. We will introduce a filtering scheme based on convolution equations of fractional type. The use of this kind of filter as a pre-classification step will be illustrated by classifying MODerate-resolution Imaging Spectroradiometer (MODIS) data to map burned areas in Mediterranean countries. The methodology we propose improved the estimations obtained by merely classifying the post-fire images (i.e. without filtering) in the study areas considered.     

A cloud detection method based on a time series of MODIS surface reflectance images

The Moderate Resolution Imaging Spectroradiometer (MODIS)-Terra surface reflectance product (MOD09A1), with bands 1 to 7, is a gridded, eight-day composite product derived from the MODIS-Terra top of atmosphere reflectance swaths. It performs cloud detection and corrects for the effects of atmospheric gases and aerosols. The cloud mask (CM) algorithms for MODIS are based on empirical thresholds on spectral reflectance and brightness temperature. Since the spatial resolution of the thermal band is 1000 m, while that of MOD09A1 is 500 m, many undetected and false clouds are observed in MOD09A1. These errors always result in temporal and spatial inconsistencies in higher-level products. In this paper, a cloud detection algorithm (TSCD) based on a MOD09A1 time series is introduced. Time series cloud detection (TSCD) algorithm is based on the relative stability of ground reflectance and the sudden variations in reflectance that result from cloud cover. The algorithm first searches the clear-sky reference data, and then discriminates clouded and unclouded pixels by detecting a sudden change of reflectance in the blue wavelength and spectral correlation coefficient at the pixel level. Compared with cloud cover assessments obtained from MODIS' original CM, TSCD provides similar or better discrimination in most situations when the land surface changes slowly.     

Machine Learning and Bias Correction of MODIS Aerosol Optical Depth

Machine-learning approaches (neural networks and support vector machines) are used to explore the reasons for a persistent bias between aerosol optical depth (AOD) retrieved from the MODerate resolution Imaging Spectroradiometer (MODIS) and the accurate ground-based Aerosol Robotic Network. While this bias falls within the expected uncertainty of the MODIS algorithms, there is room for algorithm improvement. The results of the machine-learning approaches suggest a link between the MODIS AOD biases and surface type. MODIS-derived AOD may be showing dependence on the surface type either because of the link between surface type and surface reflectance or because of the covariance between aerosol properties and surface type.