基于TM影像的面向对象地表覆被信息提取

为验证基于TM影像的面向对象分类方法对复杂地区地表覆被信息提取的可行性,以地处西南地区的渝北为例进行实验。利用样本数据对各个波段的光谱特征进行分析,取得对各波段覆被探测能力的初步认识;基于光谱特征的多尺度分割,运用面向对象分类方法对其分类。面向对象的分类方法总精度和Kappa系数分别为88.42%和0.854 7,将其与监督、非监督分类结果对比分析。结果表明,该方法有效抑制了"椒盐"现象,取得较好的分类结果。     

都江堰震后土地利用/覆被变化信息提取方法研究

快速和精确评估"5·12"汶川地震后的土地利用/覆被变化对科学减灾、灾后重建及生态环境恢复具有重要意义。常规方法从遥感图像上提取土地利用/覆被变化信息时,多以研究区整体为处理对象,直接对全图像进行分类提取,容易忽略地形地貌和地质构造等背景因素对分类结果的影响。本文以都江堰为试验区,根据地质构造展布特征及地形地貌发育形态将其分成平原区(Ⅰ区)、低山区(Ⅱ区)、中高山区(Ⅲ区)和高山区(Ⅳ区)。对Ⅰ区采用ISODATA方法进行非监督分类;对Ⅱ区和Ⅲ区分别采用最大似然(ML)分类法进行监督分类;对Ⅳ区采用人机交互解译方法进行分类。试验结果表明,基于地理地质环境的分块分类方法是高效、精确的。     

基于遥感技术的土地利用／覆被变化研究

遥感技术是研究土地利用/覆被变化状况的重要手段之一,目前该技术已有一套较成熟的研究方法,本文从遥感数据选择、图像预处理方法、土地利用/覆被变化遥感分类、土地利用/覆被变化动态信息提取等方面对土地利用/覆被变化遥感动态监测技术进行了系统的论述,最后指出多信息源、综合的信息处理机制将是土地利用/覆被变化遥感动态监测技术发展的必然方向。     

面向对象的高分辨率影像土地利用信息提取

运用面向对象的分类方法对实验区高分辨率影像进行最优分割尺度的影像分割。首先利用ESP算法确定影像的最佳分割尺度,再运用CART决策树分类对土地利用信息进行提取,并对分类结果进行后处理,进一步提高分类精度。最终分类结果精度达到80.38%。     

基于分类回归树分析的遥感影像土地利用／覆被分类研究

以专家知识和经验为基础,综合影像光谱信息和其他辅助信息进行分类的基于知识的遥感影像解译方法,是提高遥感影像分类精度,实现自动解译的有效途径之一。然而,知识的获取一直是其得以广泛应用的“瓶颈”问题。以江苏省江宁试验区土地利用/覆被分类为例,利用分类回归树分析(CART)从训练样本数据集中发现分类规则,集成遥感影像的光谱特征、纹理特征和空间分布特征进行分类实验,并与传统的监督分类和逻辑通道分类方法进行比较。结果表明,基于CART的分类方法的精度基本在80%以上,与另两种方法相比,有了较大的提高,而且该算法复杂性低,效率高。由此说明,利用CART算法构建决策树获取的分类规则是合理的。它可以快速、有效地获取大量分类规则,是促进基于知识的遥感影像分类方法在土地利用/覆被分类中广泛应用的一项有效手段。     

基于遥感数据的哈尔滨土地利用/覆被变化研究

以哈尔滨市为例,基于Landsat数据,利用遥感技术和GIS技术对2000和2015年的遥感影像数据进行解译分类,通过对分类结果的统计分析,定量研究哈尔滨市15年间的土地利用变化过程,结果显示:哈尔滨市土地综合利用程度较高,区域内耕地面积减少;相反,城市建成区面积大幅增加,区域内绿地面积逐年减少,应注意生态环境维护保持。     

干旱区绿洲土地利用/土地覆被动态演变研究

选择生态环境脆弱的艾比湖流域内绿洲为研究对象,以1990年、2001年、2010年三期TM和ETM+影像为数据源,采用SVM分类方法进行土地利用/覆盖类型分类、统计与分析研究区20年土地利用/覆盖时空动态变化。研究结果表明:1)1990～2001年除了裸地面积减少外,其他地类均有增加,且耕地面积增加幅度最大,面积增加了483.42 km2。2001～2010年除了耕地面积增加了488.87 km2,其余地类的面积都有不同程度的减少。而研究期间裸地面积一直呈现不断下降的趋势,在20年间共减少了1 186.43 km2。2)经济发展、政策、人口以及自然环境等驱动因素对研究区土地利用结构变化影响甚为显著,而人口迅速增加对农产品的大量需求促使耕地变化剧烈,面积大幅增长。因此,合理地控制人口数量,正确的政府政策导向和合理经济发展需求是干旱区绿洲土地可持续发展的关键。     

武汉市近20年土地利用/覆被变化遥感监测与分析

为探究武汉市土地利用时空变化特征,基于Landsat遥感影像数据,从土地利用动态度、土地利用状态指数两个方面分析了武汉市2000—2019年的土地利用时空变化格局.结果表明:①武汉市水域面积直线下降,下降速度较稳定,平均年缩减率为1.76％;而建设用地面积逐年增加,由市中心向四周呈"辐射式"扩张,增速逐渐减缓;②耕地和...     

四川省及成都地区土地利用/覆被变化建库与对比研究

为探讨近年四川省部分地区严重旱涝灾害天气成因,利用遥感(RS)、地理信息系统(G IS)和全球定位系统(GPS)技术建立了四川省及成都地区2000~2004年土地利用/覆被变化数据库,并对四川省及成都地区2000~2004年土地利用/覆被变化信息进行对比研究,对四川省部分地区与成都地区近年气候差异成因进行探讨。     

面向对象的分类方法在土地利用数据库建设中的应用

土地利用数据库对于土地管理、土地动态检测和土地变更调查具有重要的意义。本文主要介绍了建立土地利用数据库的技术方法和工作流程。以张家界市武陵源区为例,采用面向对象的分类方法对高分辨率遥感影像进行分割和自动分类,将分类结果导入GIS环境下,进行拓扑构建和属性编辑,实现图形数据、属性数据的关联,最后完成土地利用现状数据建库。上述方法有效提高了建库质量和建库水平,并取得了很好的效果。     

Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products,using Landsat data and C-CAP products

This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.     

条件随机场框架下基于随机森林的城市土地利用/覆盖遥感分类

针对"基于像素的条件随机场(conditional random fields,CRFs)模型能否在m级分辨率的多光谱遥感图像分类中表现良好"的问题,提出了集成图像的光谱、方向梯度直方图和多尺度多方向Texton纹理等多种线索的CRFs模型定义方法。利用上述特征,选择随机森林(random forests,RF)定义CRFs关联势函数;利用特征对比度加权的Potts函数定义CRFs交互势函数,并且建立了多标签的RF-CRFs模型;对该模型进行分项参数训练以及基于图割的α-膨胀算法推理;利用典型城区的Quick Bird多光谱图像进行模型的验证与精度评价。结果表明RF-CRFs模型的分类精度可达82.52%以上,比RF分类器的分类精度提高了3.35%。     

面向对象的高分辨率遥感影像土地覆盖信息提取

利用高分辨率影象提取土地覆盖信息的关键技术在于如何利用丰富的纹理信息来弥补光谱信息的不足。面向对象的图像分类技术改变了传统的面向像素的分类技术:(1)用来解译图像的信息并不在单个像元中,而是在图像对象和其相互关系中;采用多分辨率对象分割方法生成图像对象,提高了分类信息的信噪比;基于对象的分类技术不同于纯粹的光谱信息分类,图像对象还包含了许多的可用于分类的一些其他特征:形状、纹理、相互关系、上下关系等信息。面向对象的土地覆盖分类结果与传统分类方法相比,其特征提取算子更加地适合于几何信息和结构信息丰富的高分辨率图像的自动识别分类。     

Analysis of coastal land use/land cover changes in the Indian Sunderbans using remotely sensed data

The world’s largest mangrove ecosystem, the Sunderbans is experiencing multidimensional threats of degradation. The present study was aimed to understand these problems and search for proper remedies by applying suitable remote sensing technologies. South-western parts of Indian Sunderbans Biosphere Reserve had been chosen for assessment of land use/land cover changes in between 1975 and 2006 by using multitemporal Landsat data. Results indicated considerable reduction of open mangrove stands and associated biodiversity mainly in the forest-habitation interference zones of Sunderbans. On the contrary, increase in the coverage of dense mangroves in the reserved forests had been observed indicating the existence of proper centralized management regimes. Overall, a cumulative loss of approximately 0.42% of its original mangrove cover in between 1975 and 2006 had been estimated for this part of the Sunderbans which was at parity with the findings of other studies in the Sunderbans or similar mangrove ecosystems of the tropics. Expansion of non agricultural lands in the last two decades was found to be related with the growth of new settlements, tourism infrastructure, and facilities. This transformation was attributed to the shifting of local peoples’ interest from traditional forestry and subsistence farming towards alternative occupations like shrimp culture, coastal tourism, and commercial fishing although environmentally hazardous livelihood activities like collection of prawn seeds along the riverbanks were still persistent.     

基于遥感技术的海岸带土地利用、覆被信息提取方法研究

本文以地处莱州湾东海岸的莱州市为研究区域,以TM影像为主要信息源,在分析各类地物分类效果的基础上,结合区域特点和地物光谱特征,综合利用两期典型季相的遥感图像,并针对每一种土地利用类型确定最佳信息提取影像,提出了基于光谱、地形等多种知识的分层信息提取方法.结果表明该方法分层目标明确,分类精度和计算效率都有明显提高,且在数...     

Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery

Accelerated soil erosion, high sediment yields, floods and debris flow are serious problems in many areas of Iran, and in particular in the Golestan dam watershed, which is the area that was investigated in this study. Accurate land use and land cover (LULC) maps can be effective tools to help soil erosion control efforts. The principal objective of this research was to propose a new protocol for LULC classification for large areas based on readily available ancillary information and analysis of three single date Landsat ETM+ images, and to demonstrate that successful mapping depends on more than just analysis of reflectance values. In this research, it was found that incorporating climatic and topographic conditions helped delineate what was otherwise overlapping information. This study determined that a late summer Landsat ETM+ image yields the best results with an overall accuracy of 95%, while a spring image yields the poorest accuracy (82%). A summer image yields an intermediate accuracy of 92%. In future studies where funding is limited to obtaining one image, late summer images would be most suitable for LULC mapping. The analysis as presented in this paper could also be done with satellite images taken at different times of the season. It may be, particularly for other climatic zones, that there is a better time of season for image acquisition that would present more information.     

Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data

Integrating multiple images with artificial neural networks (ANN) improves classification accuracy. ANN performance is sensitive to training datasets. Complexity and errors compound when merging multiple data, pointing to needs for new techniques. Kohonen's self-organizing mapping (KSOM) neural network was adapted as an automated data selector (ADS) to replace manual training data processes. The multilayer perceptron (MLP) network was then trained using automatically extracted datasets and used for classification. Two hypotheses were tested: ADS adapted from the KSOM network provides adequate and reliable training datasets, improving MLP classification performance; and fusion of Landsat thematic mapper (TM) and SPOT images using the modified ANN approach increases accuracy. ADS adapted from the KSOM network improved training data quality and increased classification accuracy and efficiency. Fusion of compatible multiple data can improve performance if appropriate training datasets are collected. This proved to be a viable classification scheme particularly where acquiring sufficient and reliable training datasets is difficult.     

基于光谱和纹理特征的山区高分辨率遥感影像分类

本文在只做阴影补偿而不做地形校正的情况下,使用光谱和纹理特征相结合的方法进行山区高分辨率遥感影像分类。实验取得了78%的分类精度,表明该方法合理可行,具有一定的实用性。