空间逐步寻优数据挖掘在遥感影像分类中的应用

在遥感地学分析模型中,由于遥感信息模糊和不确定性的特点,其特征空间中的特征分布并不完全符合特定的高斯密度分布,而是分布形状各异,相互交错,用传统模型是很难获得特征的最优分布解的,而空间逐步寻优数据挖掘方法(SOMM)是在演化寻优理论的基础上,融合知识的参数化分布函数,来逐步分离特征空间,逐步降解的获得特征树状的层次结构。结合实例,用SOMM方法对遥感影像进行分类计算,并与传统的最大似然分类方法的分类结果进行了比较。     

规划辅助决策专家系统设计与应用

本文介绍了土地合理利用规划辅助决策专家系统(DADES)的构建原理与实现方法,讨论了DADES中专家知识的表示方法与推理控制策略。系统将领域专家知识划分成不同的层次和类型,采用框架网络数据结构和产生式规则表示地理学领域专家的专门性知识与经验性知识,使知识的表示简洁易于理解。在知识的推理策略中引入不精确推理机制,保证了推理机制的实现。以乌鲁木齐河流域为试验区,阐述了流域土地合理利用规划决策专家知识的表示方法,以及专家知识表示的基本规则和系统专家知识的组织方式。此外,还讨论了系统推理机推理规则的设计原理与组织方法及推理算法。     

利用视觉词袋模型和颜色直方图进行遥感影像检索

基于内容的遥感影像检索已经成为遥感领域的研究热点,因此,本文提出了一种综合视觉词袋模型和颜色直方图的遥感影像检索方法,利用尺度不变特征算子提取影像的局部不变特征,通过视觉词袋模型组合局部特征,生成每幅影像的金字塔直方图,接着结合每幅影像的颜色直方图生成更有区分性的特征向量,利用新的特征向量集训练支持向量机分类器,通过分类器输出与查询属于一类的影像,完成遥感影像检索。试验结果表明,本文方法不仅提高了影像检索的查准率和查全率,并且验证了该方法能有效克服影像光照、噪声、方向等变化,鲁棒性较好。     

Expansion of landscape characterisation methods within the Hydrogeological Landscape Framework: application in the Australian Capital Territory

The Hydrogeological Landscape (HGL) Framework is a landscape-characterisation tool that is used to discern areas of similar physical, hydrogeological, hydrological, chemical and biological properties, referred to as HGL Units. The HGL Framework facilitates prioritisation of natural-resource management investment by identifying current and potential hazards in the landscape. Within prioritised regions, on-ground management actions are tailored for specific Management Areas within individual HGL Units. The HGL Unit boundaries are determined through expert interpretation of spatial and field based datasets, such as climate, landform, geology, regolith, soil, stream network, groundwater flow systems, water quality and vegetation assemblages. The resulting HGL Units are validated by an interdisciplinary team using field assessment and biophysical testing. The use of the HGL Framework for new applications creates opportunities for refinement of the existing methodology and products for end users. This paper uses an application in the Australian Capital Territory as a case study to illustrate two enhanced techniques for the landscape characterisation component of the HGL Framework: use of an unsupervised statistical learning algorithm, Self-Organising Maps (SOM), to further validate HGL Units; and landform modelling to assist in delineation of Management Areas. The combined use of SOM and landform modelling techniques provides statistical support to the existing expert and field-based techniques, ensuring greater rigour and confidence in determination of landscape patterns. This creates a more refined HGL Framework landscape-characterisation tool, facilitating more precise hazard assessment and strategic natural-resource management by end users.     

矿区煤及矸石压占灾害决策树分类提取方法

矿业活动为现代工业提供物质基础的同时,极大扰动矿区原有生态结构。煤与矸石压占是矿区多种地质灾害的一种,煤与矸石压占直接造成矿区珍贵的土地资源的浪费,同时,煤与矸石自燃、淋溶、扬尘以及天然放射性对矿区环境有严重影响。近年来,遥感技术以其大范围、实时性等技术优势,在探索矿业活动与生态保护共同途径方面做了大量有益的尝试,尤其在地质灾害监测预警方面体现了广阔应用前景。本文通过对矿区遥感影像的一系列处理(如图像校正,图像增强,图像分类),探索性地采用基于人工智能的决策树分类方法,有效地排除掉同谱异物的现象的影响,高精度地提取出矿区煤与矸石堆放场,为统计煤与矸石压占,预知煤与矸石自燃,判断煤与矸石淋溶、扬尘以及天然放射性对周围环境的影响,以及为今后煤与矸石场的选址规划,提供第一手定量化的地理属性资料。     

专家主持气象节目的优势与问题分析

目前专家主持播讲的电视节目正在悄然兴起,而在电视气象节目中专家型主持人非常少见。本文探讨了专家主持播讲气象节目的优势、可行性以及应注意的问题,对专家主持播讲气象节目的形式给予了充分肯定,同时指出必须切实避免一些容易出现的问题。     

遥感影像居民地信息自动提取方法研究进展

快速掌握居民地的时空分布和变化特征对于区域可持续发展具有重要意义。遥感技术的发展，为居民地的快速提取提供了新的途径。传统的人工目视解译费时费力，基于遥感影像的居民地信息自动提取势在必行。主要探讨了居民地信息自动提取的5种方法，即基于统计的分类法、谱间结构阀值法、归一化指数法、纹理分析法、地学专家知识分类法，讨论了各种方法的优缺点，总结了目前的研究进展。最后提出了居民地信息自动提取方法的发展趋势．认为多种分类方法相结合，建立居民地信息提取的专家系统将是今后研究的重点。     

基于专家证据权重法的成矿远景区划与评价--以东昆仑地区金矿为例

在介绍专家证据权重法的基础上,将东昆仑地区的基础地质、区域成矿规律及典型矿床研究等方面取得的认识(知识系统)与证据权重法结合,提出并建立了一个基于专家证据权重法的矿产资源定量预测评价系统,该系统在根据知识系统对各证据因子赋权重的过程中体现了地质专家的思想.应用专家证据权重法对东昆仑地区金矿的成矿概率进行了定量计算,并进行了金矿成矿远景区划.研究结果表明远景区内预测含矿单元的后验概率二重积分的大小可代表远景区的总体成矿潜力,据此将各远景区进行分级排队,由高到低划分为A、B、C 3级.     

Expert Classification Method Based on Patch-Based Neighborhood Searching Algorithm

The expert knowledge has been widely used to improve the remotely sensed classification accuracy. Generally, the ex-pert classification system mainly depends on DEM and some thematic maps. The spatial relationship information in pixel level was commonly introduced into the expert classification. Because the geographic objects were found spatially dependent relationship to a certain degree, the commonly used basic unit of spatial relationship information in pixel greatly limited the efficiency of spatial in-formation. A patch-based neighborhood searching algorithm was proposed to implement the expert classification. The homogene-ous spectral unit, patch, was used as the basic unit in the spatial object granularity, and different types of patches' relationship in-formation were obtained through a spatial neighborhood searching algorithm. And then the neighborhood information and DEM data were added into the expert classification system and used to modify the primitive classification errors. In this case, the classi-fication accuracies of wetland, grassland and cropland were obviously improved. In this work, water was used as base object, and different types of water extraction methods were tested to get a result in a high accuracy.     

Prototype development of GEOEXPLORER, a knowledge-based system for supergene deposits: Identification of favorable areas for bauxite

GEOEXPLORER is a knowledge-based system under development to support the prospecting and exploration of supergene deposits. As a new type of multi-stage expert systetn, GEOEXPLORER is not only able to deal with problems of searching for supergene deposits, but will also cover both the evaluation of the spatial occurrences and the economic values of the deposits considered. A first prototype of GEOEXPLORER has been developed for bauxite deposits. It is a rule-based system implemented in PROLOG and can be used to identify favorable areas for bauxite prospecting. This paper discusses this prototype of GEOEXPLORER.