Reconstruction of cloud-contaminated satellite remote sensing images using kernel PCA-based image modelling

The presence of clouds can restrict the potential uses of remote sensing satellite imagery in extracting information and interpretation. Automatic detection and removal of clouds which hide significant information in the image is an important task in remote sensing. Hence, our aim is to detect clouds and restore the missing information in order to make the image ready for further analysis and applications. Due to the difference in nature and appearance, thick and thin clouds are dealt separately. Thick cloud is detected using an efficient Fuzzy C-Means (FCM) clustering algorithm, while thin cloud is detected using a simple region growing technique. In order to reconstruct the missing pixels, we utilize the prior knowledge about the statistics of the specific image class. Kernel principal component analysis (KPCA)-based image model is obtained using a set of training images. Missing area in the image is restored after an iterative projection operation and gradient descent algorithm. In short, an image lying out of the modelled image space is iteratively modified to obtain the restored image and that would be in the image space according to the obtained nonlinear low-dimensional and sparse KPCA image model. To illustrate the performance of the proposed method, a thorough experimental analysis on FORMOSAT multi-spectral images is done using MATLAB platform. When compared to the two recent existing techniques, our proposed method is superior and makes a promising tool for thick and thin cloud removal in multi-spectral satellite images.     

新疆苇湖梁煤矿塌陷区遥感监测研究

苇湖梁煤矿已有50多年的开采历史, 长期的地下开采造成了严重的地面塌陷。本文将新疆苇湖梁煤矿塌陷区作为研究区, 以2013年的IKONOS高分遥感影像为主要数据源, 以ArcGIS为支撑平台, 采用人机交互的解译方法对塌陷区的相关信息进行提取, 结合实地调查验证的方法, 分析研究区内地质灾害的特征。通过遥感调查发现: 研究区内发育规模较大、位于塌陷区周围的拉伸区的地裂缝约有64条;塌陷坑(群)约有94个, 主要分布于南北塌陷槽及两侧;塌陷回填区总面积达0.92 km², 并在回填区发现了3个新塌陷坑。本文充分发挥了遥感技术宏观性强、速度快的特点, 调查结果反映了塌陷区灾害的实际情况。     

基于地质统计学原理的会泽铅锌矿遥感线性构造解析

地质统计学在自然科学领域发展迅速,凡是要研究空间数据的结构性和随机性, 亦或是要模拟对象的离散性、波动性等数学性质均可运用地质统计学——其理论方法在地质环境评价、石油储量计算、矿山建模及矿床地球化学等领域的研究中都取得了一系列成果。而遥感解译的复杂地质构造信息虽也符合作为地质统计学研究对象的条件, 但却鲜见其理论方法应用于遥感构造研究,从而更深入地分析地质事件及过程。本文则以会泽铅锌矿为例,在通过印度IRS-P6卫星数据解译的线性构造基础上,运用了多种地质统计学方法,综合前人研究和区域地质背景,将已知的NE、NW、EW和SN 4组方位构造再划分为“成矿有利”和“非成矿有利”2组构造;在通过方差分析检验了分类的合理性后,通过主成分分析和克里格插值法作出了成矿有利度图,并比对已知的的矿山厂和麒麟厂矿床,最终从构造角度为找矿工作提供了一定的参考依据。     

基于IDL的资源一号02C星HR影像RFM几何校正

资源一号(ZY-1)02C卫星作为我国高分辨率遥感数据卫星之一,正广泛应用于各个领域.该卫星数据采用国际上广泛使用的有理函数模型(Rational Function Model,RFM),并提供有理函数多项式系数(Rational PolynomialCoefficients,RPC).针对ZY-1 02C星1级辐射校正数据格式特点,在充分了解数据文件结构基础上,使用IDL语言进行开发,通过RFM反演其RPC,实现HR影像的几何校正等预处理.实验结果表明,程序执行快速稳定,精度可靠,影像镶嵌效果好,可以有效使用RFM实现大数据量影像的系统几何校正,避免重复操作中间数据的繁琐步骤,提高工作效率,具有实用价值.     

A study of coastal transformation at Tuticorin as a result of emerged and submerged natural breakwaters of Van Island,Gulf of Mannar

Similar to artificial offshore structures, natural structures such as an island also protect the low-lying shoreline and shape the pocket beaches. In the Gulf of Mannar, many long islands have formed protruding from shorelines. Formation of these indirect morphological structures depends upon a number of factors, such as the width of the island, offshore distance of the island to the morphological structure, supply of sediments and other geological conditions. The hydrodynamic conditions between Tuticorin Harbour and Van Island have changed because of various factors over the years. Van Island coral reef formation had attracted man-induced mining causing irreparable damage to the fragile ecosystem. The emerged natural breakwater and the submerged coral reef, together with the artificial breakwaters of Tuticorin Harbour, have been contributory to quantifiable change in hydrodynamic conditions around Tuticorin. As a result, the transmission of wave power to the shoreline has increased and the salient growth at Salaipatorri Point, Tuticorin, has started to recede. The bathymetric chart published in 1978 is used for all dimensions of Van Island and receding salient growth at Salaipatorri Point.     

闽江南北港冲淤变迁遥感动态监测与分析

基于多期次遥感影像数据,应用图像差值法、光谱特征差异法、波段替换法对闽江南北港进行冲淤变迁遥感动态监测,综合分析冲淤变迁剧烈程度可划分为3个区间,依次是南港上段,变迁最大,其次是南港下段和北港,系人为因素引起。运用遥感解译分析其变迁情况,具有高效、准确等动态监测优势,为闽江综合整治提供科学依据。     

Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery

Frequent human activity and rapid urbanization have led to an assortment of environmental issues. Monitoring land-cover change is critical to efficient environmental management and urban planning. The current study had two objectives. The first was to compare pixel-based random forest (RF) and decision tree (DT) classifier methods and a support vector machine (SVM) algorithm both in pixel-based and object-based approaches for classification of land-cover in a heterogeneous landscape for 2010. The second was to examine spatio-temporal land-cover change over the last two decades (1990–2010) using Landsat data. This study found that the object-based SVM classifier is the most accurate with an overall classification accuracy of 93.54% and a kappa value of 0.88. A post-classification change detection algorithm was used to determine the trend of change between land-cover classes. The most significant change from 1990 to 2010 was caused by the expansion of built-up areas. In addition to the net changes, the rate of annual change for each phenomenon was calculated to obtain a better understanding of the process of change. Between 1990 and 2010, an average of 4.53% of lands turned to the built-up annually and there was an annual decrease of about 0.81% in natural land. If the current trend of change continues, regardless of the actions of sustainable development, drastic declines in natural areas will ensue. The results of this study can be a valuable baseline for land-cover managers in the region to better understand the current situation and adopt appropriate strategies for management of land-cover.     

菲律宾宿务岛地区矿化蚀变图像识别与提取分析

文章通过遥感多波段数据的背景端元数M值的估计方法、回归偏度曲线分析和地物光谱反射特征来选择信息提取的特征波段;在综合分析的基础上,选择基于Crosta法的F因子与H因子分析方法,根据不同蚀变信息提取的波段选择,以自行开发的软件对律宾宿务岛地区遥感图像进行矿化蚀变信息检测与提取.提取结果表明,区内已知的矿床(点)和矿化蚀...     

Shape analysis and multispectral classification in geological remote sensing

This paper discusses the usage of mathematical morphology in image processing of remotely-sensed data for geologic interpretation. Particular attention is given to noise-reducing transformations of spectral bands before and after different methods of classification, and to the usage of textural context. The development of a viable processing strategy requires a multidisciplinary approach and expert knowledge in different areas: (a) geology, geomorphology, and vegetation in a study area, (b) properties of the sensor for imagery photointerpretation, (c) spectral/spatial properties of the digital data within an integrated dataset (remote sensing and ancillary data), and (d) data-processing tools including mathematical morphology theory. Examples of geometric characterization of Canadian LANDSAT scenes are described in which shape measurements are obtained using a PC-based hybrid image-processing and geographic information system, termed ILWIS, which was developed at ITC, in the Netherlands. Classes from supervised and unsupervised classification are compared to guide in geological mapping. Classes over individual occurrences of broad vegetation-landform units are studied to aid in environmental mapping. Field knowledge is the context necessary to construct expert procedures to drive sequences of data-processing steps toward a target result such as optimal classification, enhancement, or feature extraction. The interaction between expert rules and the image-processing steps can be based on synthetic measurements of shape to quantize the information either spatially or spectrally. Many useful geometrical transformations of spatially-distributed data are extensions or generalizations of spatial analysis functions typical of geographic information systems.     

金沙江干热河谷区滑坡遥感解译研究

“三江”(即金沙江、澜沧江及怒江)地区自然条件恶劣,交通不便,解决该地区水电大开发地质灾害调查中一条切实可行的方法是利用遥感。论文分析了金沙江干热河谷气候环境和地形条件的特点及他们对滑坡解译产生的影响。通过研究这一地区的滑坡在多种遥感影像上的图斑特征配合实地考察,分析并列举了干扰滑坡识别的图斑,总结出该地区的古滑坡、活动滑坡和典型滑坡的识别特征。同时对该地区滑坡不发育,所占地质灾害比例低的现象进行了分析。指出当地气候、地形、构造特征、岩土类型对滑坡发育存在的影响。为该类型地区的遥感地质调查提供借鉴。     

Selection of shelters after earthquake using probabilistic seismic aftershock hazard analysis and remote sensing

Natural Hazards - After a major earthquake, one of the main tasks is to identify temporary shelters. In this study, we utilized probabilistic aftershock hazard analysis based on land use...     

A multitemporal analysis of tsunami impact on coastal vegetation using remote sensing: a case study on Koh Phra Thong Island, Thailand

The Indian Ocean tsunami event of December 26, 2004 not only left massive casualties and economic damages, but also raised concerns about the destruction and recovery of coastal ecosystems. This work aimed to analyze the spatial patterns and temporal trajectories of vegetation damage and recovery using a multisensor multitemporal remote sensing, dataset. Using the study area of Koh Phra Thong, Thailand as a case study, we demonstrate the capabilities of remote sensing analysis in assessing the consequences of an extreme flooding event on the dynamics of coastal vegetation. Field surveys and satellite mid-resolution multispectral satellite data covering the period from February 2003 to December 2009 were used to map flooded areas and coastal vegetation loss and recovery following the tsunami. Normalized Difference Reflectance change detection was performed to map the extent of flooded areas. Vegetation Fraction Cover derived using spectral unmixing techniques was used to study the multitemporal changes in coastal vegetation after the event. Vegetation change detection techniques were applied to characterize the vegetation cover changes in two different time frames: short-term changes (from 4?days to 1?year after the event), and long-term dynamics (up to 5?years after). Estimates of vegetation change (decline, recovery, and gain) were quantified and mapped, with extreme vegetation losses found directly after the tsunami (up to 79?% in flooded areas). After 1?year, different trends had developed, indicating that recovering vegetation had reached up to 55?% of pre-tsunami land cover, but with different trajectories for each vegetation type.     

Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models

This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the geologic database; land cover from TM satellite image; the vegetation index value was taken from Landsat images; and precipitation distribution from meteorological data. Landslide hazard area was analyzed and mapped using the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model.     

Forecasting future changes in Manzala Lake surface area by considering variations in land use and land cover using remote sensing approach

This study assesses the changes in surface area of Manzala Lake, the largest coastal lake in Egypt, with respect to changes in land use and land cover based on a multi-temporal classification process. A regression model is provided to predict the temporal changes in the different detected classes and to assess the sustainability of the lake waterbody. Remote sensing is an effective method for detecting the impact of anthropogenic activities on the surface area of a lagoon such as Manzala Lake. The techniques used in this study include unsupervised classification, Mahalanobis distance supervised classification, minimum distance supervised classification, maximum likelihood supervised classification, and normalized difference water index. Data extracted from satellite images are used to predict the future temporal change in each class, using a statistical regression model and considering calibration, validation, and prediction phases. It was found that the maximum likelihood classification technique has the highest overall accuracy of 93.33%. This technique is selected to observe the changes in the surface area of the lake for the period from 1984 to 2015. Study results show that the waterbody surface area of the lake declined by 46% and the area of floating vegetation, islands, and land agriculture increased by 153.52, 42.86, and 42.35% respectively during the study period. Linear regression model prediction indicates that the waterbody surface area of the lake will decrease by 25.24% during the period from 2015 to 2030, which reflects the negative impact of human activities on lake sustainability represented by a severe reduction of the waterbody area.     

The Indus flood of 2010 in Pakistan: a perspective analysis using remote sensing data

The Indus flood in 2010 was one of the greatest river disasters in recent history, which affected more than 14 million people in Pakistan. Although excessive rainfall between July and September 2010 has been cited as the major causative factor for this disaster, the human interventions in the river system over the years made this disaster a catastrophe. Geomorphic analysis suggests that the Indus River has had a very dynamic regime in the past. However, the river has now been constrained by embankments on both sides, and several barrages have been constructed along the river. As a result, the river has been aggrading rapidly during the last few decades due to its exceptionally high sediment load particularly in reaches upstream of the barrages. This in turn has caused significant increase in cross-valley gradient leading to breaches upstream of the barrages and inundation of large areas. Our flow accumulation analysis using SRTM data not only supports this interpretation but also points out that there are several reaches along the Indus River, which are still vulnerable to such breaches and flooding. Even though the Indus flood in 2010 was characterized by exceptionally high discharges, our experience in working on Himalayan rivers and similar recent events in rivers in Nepal and India suggest that such events can occur at relatively low discharges. It is therefore of utmost importance to identify such areas and plan mitigation measures as soon as possible. We emphasize the role of geomorphology in flood analysis and management and urge the river managers to take urgent steps to incorporate the geomorphic understanding of Himalayan rivers in river management plans.     

高光谱遥感岩矿端元提取与分析方法研究

端元提取是高光谱遥感信息提取与分析的基础,也是解决高光谱图像混合像元分解的关键.本文针对研究区高光谱遥感数据特点,进行了辐射校正、最小噪声分离变换(MNF)及纯净像元指数(PPI)处理,在此基础上,应用二维散点图和三维散点图分别提取了端元波谱,并开展了端元属性的判别研究.岩矿端元的提取与分析为后续岩矿种类识别奠定了基础,直接影响最终成果的准确度.     

Synoptic and remote sensing analysis of dust events in southwestern Iran

Southwest regions of Iran, due to dry environmental and climatic conditions, have been identified as one of the five major regions in the world. In recent years, large parts of Iran have been affected by suspended particles from the dust storms. The studied area is located in foothills of the Zagros Mountain Range just north of Persian Gulf in southwest of Iran. Dust and other meteorological data were prepared in 3-hour intervals from 12 synoptic weather stations. For tracking dust storms, satellite images of MODIS were used. Atmospheric conditions during the occurrence of dust storms were determined using NCEP reanalysis data. According to the statistical calculations, most storms occurred in the spring and summer. The lowest number of dust events occurred in the fall and winter particularly in December and January, when there are high possibilities of rainfall occurrence and dynamical instability conditions in the north and west of the region. The results illustrated that the highest amounts of hourly dust occurred in the afternoon and the lowest amounts occurred at 00UTC (3.30 am local times). It seems that it is closely related to the heating surface and the occurrence of local dry instabilities. Analyses of data showed that dust amounts (or volumes) in all the stations have two climactic peaks, first between 1982 and 1990 and second between 2005 and 2008 periods. These peaks can be related to a variety of factors including anthropogenic factors such as war, agricultural activities, dam construction, and widespread droughts.