利用Landsat 8影像制作解译样本点辅助高分影像分类可行性的研究

地理国情普查项目使用的高分影像质量的良莠不齐给地表覆盖数据生产带来了巨大障碍。本文剖析了目前收集到的高分影像资料的主要缺陷,如多分辨率、多传感器、多年份和跨季节,给地表覆盖数据解译带来极大的局限性;阐述了Landsat 8影像自身的特点,如像幅面积大、获取周期短、波段信息丰富,同时提出利用Landsat8影像辅助解译的思路,并通过试验验证这种方法的可行性。     

基于GF-2卫星数据的国情普查地表覆盖解译研究

以广东某地地理国情普查地表覆盖解译为例,阐述了利用eCognition(易康)软件对高分二号卫星遥感影像进行分类解译,并结合ArcGIS软件作对比分析,对地表覆盖解译的数据进行精度评定。结果表明,利用eCognition(易康)软件对地表覆盖解译的数据精度准、效率高,适合大规模的高分卫星数据的地表覆盖数据解译。     

基于多尺度遥感数据地表覆盖分类的一致性研究

借助计算机自动分类和人工目视解译修正相结合的方法,研究多源影像地表覆盖分类的一致性.利用资源三号卫星影像数据地表覆盖分类结果作为检验数据,验证环境减灾卫星影像数据分类精度的可靠性.结果证明采用高分辨率和中分辨率数据相结合的方法获取地表覆盖分类,对于缺乏高分影像或者考虑节约成本的情况,用中分影像解决地表覆盖分类的宏观分析是可行的.     

基于SVM的高分一号卫星影像分类

以辽宁阜新为研究区,运用支持向量机(SVM)的方法对高分一号8 m,16 m和Landsat8多光谱影像进行土地利用分类对比研究。实验表明,SVM的分类精度高于最小距离和最大似然方法,高分一号多光谱数据的分类精度高于Landsat8数据,可以应用于土地利用的分类。     

GF-3全极化影像在地表浅覆盖区进行地质构造解译的新方法

地质构造信息对地质矿产调查具有重要意义,野外实测和光学遥感等常规手段在一些地表浅覆盖区获取的地质构造信息十分有限,而合成孔径雷达(SAR)对地表具有一定的穿透性,在探测地表浅层覆盖区域的地质构造特征中具有独特优势。利用高分三号(GF-3)全极化影像,在典型的地表浅层覆盖区域,开展了断裂构造等信息的解译探索,提出了一种地表浅覆盖区域地质构造解译的新方法。首先对西藏改则、林芝、贵阳、北京千家店等4个研究区内的断裂构造和环带构造进行分析;接着,提出了GF-3全极化影像用于浅覆盖区地质解译的处理流程,通过引入DEM数据对GF-3影像进行地形校正,充分利用微地形微地貌特征,并采用不同极化方式的RGB合成,增强了影像的判读性,并进行地质构造解译;最后,将解译结果与1∶5万实测数据进行对比,断层的位置和方向与实测结果基本一致,同时获取了大量野外实测未能探明的浅覆盖层以下的断层信息,进一步丰富了研究区的地质构造信息。结果表明,GF-3全极化影像可用于浅覆盖区的地质构造解译,并且具有野外实测和光学遥感等常规手段所不能替代的独特优势。     

基于高分遥感影像地表覆盖草地信息人工解译技术方法

地理国情普查是开展地理国情监测的前提和基础,对于准确掌握国情国力,提高政府管理的科学性、规范性和前瞻性,推动生态文明建设和重大战略、重大工程实施具有重要意义。基于高分遥感影像进行地表覆盖草地分类中天然草地及人工草地等信息的人工解译方法,主要是通过不同影像纹理特征的判别作为分类的依据,为以后同类地表分类的解译奠定了基础。     

基于卷积神经网络的高分遥感影像内陆盐沼湿地信息提取

本文以宁夏盐池Landsat 8影像、高分二号影像、LiDAR数据插值生成的DEM数据、地理国情普查数据等为数据源,首先利用一年多期的Landsat 8影像确定提取内陆盐沼湿地的最佳时相;然后对最佳时相的高分二号（GF-2）融合影像等数据进行多尺度叠置分割,获取NDVI、DEM、穗帽变换等特征,采用最邻近分类器提取内陆盐沼湿地信息,构建内陆盐沼湿地样本库;最后在此基础上探讨卷积神经网络用于高分卫星影像提取内陆盐沼湿地方法。试验结果表明,设计的卷积神经方法适用于内陆盐沼湿地提取,与最近邻分类法提取的结果相比,内陆盐沼湿地边界的提取效果有明显提高。     

滇池周边典型城镇地表覆盖分类遥感分析研究

滇池作为滇中最大高原湖泊,其水环境保护及水污染治理工作至关重要. 针对滇池周边典型城镇地表覆盖变化,发挥遥感探测技术先进性,快速了解污染源,并实时监测滇池污染治理工程进展,为及时优化治理方案提供数据支撑具有重大意义. 选取滇池周边晋宁区典型城镇为研究区,基于Landsat8影像利用遥感提取地表覆盖分类信息,运用混淆矩阵对比了最大似然法和目视解译的分类提取精度. 可知Landsat8影像通过目视解译可以快速、高效地提取城镇地表覆盖分类信息,也适用于滇池流域昆明市区、呈贡区等城镇,为滇池流域治理保护的宏观动态监测提供技术支持,为滇池流域城镇的发展、规划和治理提供决策信息.      

基于Landsat8影像的厚云及云影去除方法

Landsat系列卫星对推动遥感应用技术的发展起到了重要作用,其遥感图像数据在多领域得到了广泛应用。但是Landsat影像常受到云的污染,使得其在地表动态监测时有效性大大下降。本文首先利用云和云阴影匹配算法Fmask实现快速、准确地识别云与云影区域并进行掩膜,然后基于时空数据融合算法ESTARFM利用多时相MODIS和Landsat数据,对Landsat8影像缺失的云及云影区域进行插补。结果表明这是一种有效地去除云及云影的方法,对Landsat8数据的定量分析或时序研究具有重要价值。     

利用光学影像相关获取2019年Ridgecrest地震序列同震形变

光学影像覆盖范围广,质量越来越高,可以应用于地表形变监测研究.文中以基于ENVI的COSI-Corr作为数据处理平台,通过光学影像获取地表形变.文中选取2019年加州Ridgecrest地震序列覆盖区域.首先,探究哨兵2号影像误差处理过程中横向条带处理方法,对其进行改进;然后,使用哨兵2号光学影像获取Ridgecrest地震序列同震形变,分析地震的地表形变情况,结果显示该次地震产生多个地表破裂,东西向呈拉伸趋势,南北向呈挤压趋势,东西向形变略小于南北向形变,两个方向形变特征表明Ridgecrest地震序列主震是一个右旋走滑地震;最后对Landsat 7和Landsat 8影像分别进行相关,将3种影像进行比较.结果表明在光学影像地表形变监测中,影像分辨率越高,效果和精度越好;同种分辨率的光学影像进行相关处理,也会有不同的结果.研究成果可以为地震反演提供形变数据和约束条件,以及为光学影像的地表形变监测提供参考.     

Land use change detection based on multi‐date imagery from different satellite sensor systems

An empirical study was performed assessing the accuracy of land use change detection when using satellite image data acquired ten years apart by sensors with differing spatial resolutions. Landsat/Multi‐spectral Scanner (MSS) with Landsat/Thematic Mapper (TM) or SPOT/High Resolution Visible (HRV) multi‐spectral (XS) data were used as a multi‐data pair for detecting land use change. The primary objectives of the study were to: (1) compare standard change detection methods (e.g. multi‐date ratioing and principal components analysis) applied to image data of varying spatial resolution; (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice‐versa in the registration process: and (3) determine if Landsat/TM or SPOT/ HRV(XS) data provides more accurate detection of land use changes when registered to historical Landsat/MSS data.

Ratioing multi‐sensor, multi‐date satellite image data produced higher change detection accuracies than did principal components analysis and is useful as a land use change enhancement technique. Ratioing red and near infrared bands of a Landsat/MSS‐SPOT/HRV(XS) multi‐date pair produced substantially higher change detection accuracies (～10%) than ratioing similar bands of a Landsat/MSS ‐ Landsat/TM multi‐data pair. Using a higher‐resolution raster grid of 20 meters when registering Landsat/MSS and SPOTZHRV(XS) images produced a slightly higher change detection accuracy than when both images were registered to an 80 meter raster grid. Applying a “majority”; moving window filter whose size approximated a minimum mapping unit of 1 hectare increased change detection accuracies by 1–3% and reduced commission errors by 10–25%.     

基于TM影像的城市建筑用地信息提取方法研究

本文选用金华市Landsat TM影像为研究的数据源,在归一化裸露指数基础上,利用归一化植被指数提取出非植被信息,通过图像二值化、叠加分析以及掩膜处理去除了低密度植被覆盖区域的噪音信息,自动提取了金华城市建筑用地信息。研究结果表明,归一化裸露指数和归一化植被指数相结合的方法弥补了单一利用归一化裸露指数来提取城市建筑用地信息的不足,提高了提取精度,而且结果客观可信,是一种不经人为干预的、快速有效的提取城市建筑用地方法。     

Land cover classification using Landsat 8 Operational Land Imager data in Beijing,China

The successful launch of Landsat 8 provides a new data source for monitoring land cover, which has the potential to significantly improve the characterization of the earth’s surface. To assess data performance, Landsat 8 Operational Land Imager (OLI) data were first compared with Landsat 7 ETM + data using texture features as the indicators. Furthermore, the OLI data were investigated for land cover classification using the maximum likelihood and support vector machine classifiers in Beijing. The results indicated that (1) the OLI data quality was slightly better than the ETM + data quality in the visible bands, especially the near-infrared band of OLI the data, which had a clear improvement; clear improvement was not founded in the shortwave-infrared bands. Moreover, (2) OLI data had a satisfactory performance in terms of land cover classification. In summary, OLI data were a reliable data source for monitoring land cover and provided the continuity in the Landsat earth observation.     

结合 DEM 的地表覆盖自相关性分析

地表覆盖的空间变化可以反映人类的生存环境,其空间数据的相关性也反映着一些重要信息。在维多利亚州中,耕地一直占着比较大的比重,本文根据landsat TM影像分类得到地表覆盖数据,发现各地表覆盖间类型间的关系,并结合DEM数据研究耕地在维多利亚州的空间分布特点。     

Temporal replication of the national land cover database using active machine learning

Regional and national level land cover datasets, such as the National Land Cover Database (NLCD) in the United States, have become an important resource in physical and social science research. Updates to the NLCD have been conducted every 5 years since 2001; however, the procedure for producing a new release is labor-intensive and time-consuming, taking 3 or 4 years to complete. Furthermore, in most countries very few, if any, such releases exist, and thus there is high demand for efficient production of land cover data at different points in time. In this paper, an active machine learning framework for temporal updating (or backcasting) of land cover data is proposed and tested for three study sites covered by the NLCD. The approach employs a maximum entropy classifier to extract information from one Landsat image using the NLCD, and then replicate the classification on a Landsat image for the same geographic extent from a different point in time to create land cover data of similar quality. Results show that this framework can effectively replicate the land cover database in the temporal domain with similar levels of overall and within class agreement when compared against high resolution reference land cover datasets. These results demonstrate that the land cover information encapsulated in the NLCD can effectively be extracted using solely Landsat imagery for replication purposes. The algorithm is fully automated and scalable for applications at landscape and regional scales for multiple points in time.     

Exploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area

Remote sensing data utilize valuable information via various satellite sensors that have different specifications. Image fusion allows the user to combine different spatial and spectral resolutions to improve the information for purposes such as forest monitoring and land cover mapping. In this study, I assessed the contribution of dual-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar data to multispectral Landsat imagery. The research investigated the separability of forested areas using different image fusion techniques. Quality analysis of the fused images was conducted using qualitative and quantitative analyses. I applied the support vector machine image classification method for land cover mapping. Among all methods examined, the à trous wavelet transform method best differentiated the forested area with an overall accuracy (OA) of 94.316%, while Landsat had an OA of 92.626%. The findings of this study indicated that optical-SAR-fused images improve land cover classification, which results in higher quality forest inventory data and mapping.     

Use of Landsat Thematic Mapper for urban land use/land cover mapping

Visual interpretation of Landsat Thematic Mapper data coupled with ground checking has been used to extract information for urban areas. The emphasis has been given on development of land use/land cover scheme and image interpretation keys for interpretation and delineation purposes using satellite remote sensing data. Lucknow city and its surroundings have been studied to evaluate the usefulness and potentiality of satellite data particularly Landsat Thematic Mapper for urban area studies. This study has demonstrated that remote sensing can provide a valuable tool for urban data acquisition.     

Assessing the potential of integrated Landsat 8 thermal bands,with the traditional reflective bands and derived vegetation indices in classifying urban landscapes

Reliable and up-to-date urban land cover information is valuable in urban planning and policy development. Due to the increasing demand for reliable land cover information there has been a growing need for robust methods and datasets to improve the classification accuracy from remotely sensed imagery. This study sought to assess the potential of the newly launched Landsat 8 sensor’s thermal bands and derived vegetation indices in improving land cover classification in a complex urban landscape using the support vector machine classifier. This study compared the individual and combined performance of Landsat 8’s reflective, thermal bands and vegetation indices in classifying urban land use-land cover. The integration of Landsat 8 reflective bands, derived vegetation indices and thermal bands overall produced significantly higher accuracy classification results than using traditional bands as standalone (i.e. overall, user and producer accuracies). An overall accuracy above 89.33% and a kappa index of 0.86, significantly higher than the one obtained with the use of the traditional reflective bands as a standalone data-set and other analysis stages. On average, the results also indicate high producer and user accuracies (i.e. above 80%) for most of the classes with a McNemar’s Z score of 9.00 at 95% confidence interval showing significant improvement compared with classification using reflective bands as standalone. Overall, the results of this study indicate that the integration of the Landsat 8’s OLI and TIR data presents an invaluable potential for accurate and robust land cover classification in a complex urban landscape, especially in areas where the availability of high resolution datasets remains a challenge.     

老挝Phonxay区土地利用/土地覆盖遥感调查

老挝是一个发展中国家,境内的大多数地方没有开展过土地利用/土地覆盖调查。本文选择老挝琅勃拉邦省的Phonxay区为研究区域,利用Landsat OLI数据进行土地利用/土地覆盖遥感调查与分析。研究过程中,利用ArcGIS Desktop选择训练样本和验证样本,通过Python和ArcPy编程开发了图像分类、精度评价以及面积统计的工作流程序,实现了快速得到分类结果和精度评价信息,分类结果的总精度为89.53%,Kappa系数为0.81。     

High spatial resolution image object classification for terrestrial oil spill contamination mapping in West Siberia

This work is a part of the OSCaR pilot study (Oil Spill Contamination mapping in Russia). A synergetic concept for an object based and multi temporal mapping and classification system for terrestrial oil spill pollution using a test area in West Siberia is presented. An object oriented image classification system is created to map contaminated soils, vegetation and changes in the oil exploration well infrastructure in high resolution data. Due to the limited spectral resolution of Quickbird data context information and image object structure are used as additional features building a structural object knowledge base for the area. The distance of potentially polluted areas to industrial land use and infrastructure objects is utilized to classify crude oil contaminated surfaces. Additionally the potential of Landsat data for dating of oil spill events using change indicators is tested with multi temporal Landsat data from 1987, 1995 and 2001. OSCaR defined three sub-projects: (1) high resolution mapping of crude oil contaminated surfaces, (2) mapping of industrial infrastructure change, (3) dating of oil spill events using multi temporal Landsat data. Validation of the contamination mapping results has been done with field data from Russian experts provided by the Yugra State University in Khanty-Mansiyskiy. The developed image object structure classification system has shown good results for the severely polluted areas with good overall classification accuracy. However it has also revealed the need for direct mapping of hydrocarbon substances. Oil spill event dating with Landsat data was very much limited by the low spatial resolution of Landsat TM 5 data, small scale character of oil spilled surfaces and limited information about oil spill dates.