为了查明察尔汗盐湖区的矿产资源开发状况和矿山地质环境现状及变化趋势,利用2008—2010年获取的3期SPOT5卫星图像的遥感解译资料,对该地区的矿山地质环境及矿产资源开发状况进行了研究。结果表明,区内工厂数量和泵站数量均有所增加,开采盐田、尾盐、老卤汇集区占地面积扩展迅速,采卤渠、输卤渠长度均在延展。同时,从监测目标以及遥感数据源入手,进一步探讨了对青海省察尔汗盐湖进行矿山遥感监测的技术方案,为最终形成系统的监测方法和技术规范奠定了基础。

<p>随着高分辨率国产遥感卫星数据的推广应用，矿山遥感监测必将成为该数据的重要应用领域之一。结合西藏自治区矿山遥感监测工作的特点，以资源一号02C（ZY-1 02C）及高分一号（GF-1）卫星影像为数据源，在ArcGIS环境下，提出并实现了从国产卫星遥感数据管理、增强与校正、信息提取、统计分析以及成果图制作等一体化解决方案。该研究成果有助于推进国产卫星遥感数据在矿山遥感监测领域的应用广度和深度，为大规模开展多期次动态矿山遥感监测工作提供技术支持和应用范例。</p>

利用资源一号(ZY-1)02C卫星(以下简称02C星)全色高分辨率相机(HR)和多光谱相机(MUX)数据,以河北承德多金属矿区和江西寻乌稀土矿区为实验区,开展了1:5万矿山遥感监测应用研究; 分析了应用过程中存在的问题,提出了解决方案和建议,对02C星数据在矿山遥感监测中的规模化应用起到了示范效果。研究结果表明: 在数据处理流程上,对空间分辨率为2.36 m的02C星全色HR1与HR2数据需先进行正射纠正再拼接,对空间分辨率为10 m的多光谱数据MUX可采用假彩色合成方法进行模拟真彩色合成; 在矿山地物识别上,与SPOT5图像比较,02C星图像可更好地反映出开采面、开采点、中转场和矿山建筑等矿山地物类型,不足之处是纹理信息较平滑、矿山地物的边界相对模糊,因而增加了室内解译的难度; 在经济效益上,02C星数据的性价比优势明显。因此,尽管02C星数据处理和解译工作量较大,但其能满足1:5万矿山遥感监测要求,总体经济效益显著。

利用遥感技术,以会理多金属矿区为研究区,以国产卫星高分一号卫星影像为数据源,探讨了高分一号卫星影像在15万矿山开发遥感监测中的可行性。通过对高分一号遥感图像的几何纠正精度、图像融合质量、遥感解译标志、制图精度进行分析,高分一号影像完全满足15万矿山开发遥感监测的需要。通过利用总结的矿山开采遥感信息提取技术方法和建立的遥感解译标志,快速提取了研究区内矿山分布和开采信息,较全面准确地掌握了区内矿产资源开发现状与矿山环境状态。在监测中使用高分一号卫星影像,既提高15万矿山开发遥感调查的监测频率,也降低了监测成本。

随着遥感影像数据量以及复杂程度的日益增加,遥感图像的快速处理成为实际应用过程中亟需解决的问题。为了实现遥感影像的实时变化检测,针对基于变化矢量分析CVA的变化检测算法,设计了一种基于统一计算设备构架CUDA的并行处理模型。首先利用地理空间数据提取库GDAL实现大数据量遥感影像的分块读取、操作和保存;其次将基于变化矢量分析的变化检测过程分为变化强度检测、映射表构建和变化方向检测,并借助CUDA C将变化矢量分析算法的3个步骤嵌入到CPU和GPU组成的异构平台上进行实验;最后利用该模型对不同数据量的遥感影像进行CVA变化检测并作对比分析。实验结果表明：与CPU串行相比,基于GPU/CUDA的遥感影像CVA的变化检测速度提高了10倍左右;在一定程度上,达到了实时变化检测的效果。

<p>为实现对土地覆盖变化的遥感监测,研究了一种基于不同年份单时相遥感数据提取差异影像、自动确定变化阈值提取变化区域的新方法。以南通市Landsat8 OLI影像为例,对2期影像分别进行主成分分析(principal component analysis, PCA);取前3个主分量进行变化向量分析(change vector analysis, CVA),构造变化检测差异影像,并与传统PCA法和CVA法构造的差异影像进行对比;对3景差异影像分别用传统全局阈值法和局部最小错分概率法自动确定阈值,分别提取变化区域,得到6景变化区域图。利用目视解译样点进行精度评价的结果表明,改进后的基于PCA的CVA法提取的变化区域总体精度可达92.78%,Kappa系数可达0.8426,证明使用该方法可有效地进行不同年份单时相遥感数据的变化检测。</p>

A key issue in decision tree (DT) induction with continuous valued attributes is to design an effective strategy for splitting nodes. The traditional approach to solving this problem is adopting the candidate cut point (CCP) with the highest discriminative ability, which is evaluated by some frequency based heuristic measures. However, such methods ignore the class permutation of examples in the node, and they cannot distinguish the CCPs with the same or similar frequency information, thus may fail to induce a better and smaller tree. In this paper, a new concept, i.e., segment of examples, is proposed to differentiate the CCPs with same frequency information. Then, a new hybrid scheme that combines the two heuristic measures, i.e., frequency and segment, is developed for splitting DT nodes. The relationship between frequency and the expected number of segments, which is regarded as a random variable, is also given. Experimental comparisons demonstrate that the proposed scheme is not only effective to improve the generalization capability, but also valid to reduce the size of the tree.

In this paper, an efficient semi-supervised support vector machine (SVM) with segmentation-based ensemble (S2SVMSE) algorithm is proposed for hyperspectral image classification. The algorithm utilizes spatial information extracted by a segmentation algorithm for unlabeled sample selection. The unlabeled samples that are the most similar to the labeled ones are found and the candidate set of unlabeled samples to be chosen is enlarged to the corresponding image segments. To ensure the finally selected unlabeled samples be spatially widely distributed and less correlated, random selection is conducted with the flexibility of the number of unlabeled samples actually participating in semi-supervised learning. Classification is also refined through a spectral–spatial feature ensemble technique. The proposed method with very limited labeled training samples is evaluated via experiments with two real hyperspectral images, where it outperforms the fully supervised SVM and the semi-supervised version without spectral–spatial ensemble.

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.

The appetite for up-to-date information about earth surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

A novel approach is presented for change detection of very high resolution images, which is accomplished by fast object-level change feature extraction and progressive change feature classification. Object-level change feature is helpful for improving the discriminability between the changed class and the unchanged class. Progressive change feature classification helps improve the accuracy and the degree of automation, which is implemented by dynamically adjusting the training samples and gradually tuning the separating hyperplane. Experiments demonstrate the effectiveness of the proposed approach.