An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

改进的模糊聚类遥感影像分割

聚类是数据挖掘的重要分支之一,引入模糊理论的模糊聚类分析为显示数据提供了模糊处理能力,在许多领域被广泛应用。本文应用考虑邻域关系的约束模糊C均值(Fuzzy C-Means with Constrains,FCM_S)算法,将邻域像素引入到目标函数中,进而有效地利用邻域像素信息,提高分割精度。本文应用FCM_S算法对模拟彩色纹理图像进行分割,计算其混淆矩阵,定性定量地与FCM算法进行对比分析,证明了该算法的鲁棒性。     

如何利用计算机技术进行地图投影变换

在目前流行的计算机制图中，必须面对如何解决使用不同投影的地图资料的问题，本文从实际应用的角度出发，介绍并讨论解决方法及应用手段。     

山岳型旅游目的地形象感知研究——以五台山风景名胜区为例

以山岳型旅游目的地五台山风景区为例,利用百度、Google以及国内各旅游网站收集游客记述五台山旅游形象的87篇网络游记和620条网上点评,运用质性研究之扎根理论的方法对五台山申遗后旅游形象的感知进行研究,以期为山岳型旅游目的地形象研究拓展方法和为其管理提供参考和依据.结果表明:1.从71篇网络游记和490条网络点评中提取出佛教胜地、静心拜佛、避暑胜地、建筑精湛、许愿还愿和朝台览胜6大积极感知因素;2.从226句负面评论中,归纳出五台山旅游形象的6个消极感知因素为旅游供给、旅游环境、旅游设施、旅游管理、旅游吸引和旅游服务;3.同时,典故传说、佛缘之地、民风淳朴亦为五台山旅游形象的积极感知因素.     

The prediction for seismicity trends of M≥8 in the world by analysis of seismicity patterns

After completion of a study on predicting risky zones of earthquake of M≥8 for 1-3 years in the mainland of China,which was supported by Chinese Joint Seismological Science Foundation(D07018 and D08009),a further study was extended to that of greater magnitudes in the world.Based on the historical earthquake cases,we finished the research report,forecasting global earthquakes with magnitude more than 8.0 using the image analysis of seismicity.In this research report,we summarize 11 abnormal seismicity images for predicting earthquake of M≥8.0 around the world.In our research report,we predicted earthquakes of M≥8.0 from June 2009 to June 2014,the medium term predicting within 5 years period.Among these 5 predictive areas,three earthquakes occurred,which is Samoa M8.1 Earthquake on 29 September 2009,Talca M8.5 Earthquake on 27 January 2010,Chile,and Eastern Sendai M9.0 Earthquake on 11 March 2011,Japan respectively.Here we introduce the main items of the image analysis of seismicity and we predict three earthquakes and think that the image analysis of seismicity can be of help.     

一种用于海底类型聚类的新方法

By using sonar imaging, this paper presents a new algorithm for the clustering of seabed types based on the self-organizing feature maps （SOFM） neural network. The theory as well as data processing is studied in detail. Some valuable conclusions and suggestions are given     

基于RS、GIS的东喜马拉雅南迦巴瓦峰地区断裂构造的定量研究

东喜马拉雅南迦巴瓦峰地区地质构造十分复杂,对该区板块缝合带的空间位置问题,长期以来,一直认为沿雅鲁藏布江呈弧形展布。笔者以ETM+为主要信息源,以遥感与GIS为技术支撑,对南峰地区的断裂构造进行了详细的遥感解译分析,从构造统计分析的角度对断裂构造进行定量研究,从分维值及趋势值的空间分布及变化特征分析,认为缝合带的南东段、北段大致沿雅鲁藏布江展布,而北西段位于雅鲁藏布江的北西侧,沿东久-米林(断裂带)展布,这也与近几年来开展了1∶25万区域地质调查获得的新认识相一致。     

高分一号遥感影像地质灾害信息提取方法研究

山区地质灾害发生频繁,受灾范围大,危害严重。提取地质灾害信息、估算受灾情况,对救灾工作极为重要。传统的遥感人工解译方法速度慢、效率低;计算机自动解译速度快但解译精度受影像质量影响大,大范围地区很难建立起普适性的解译模型。本文利用洮南市西部高分一号遥感数据,结合DEM生成三维影像,建立解译标志,解译基础地质信息,分析地质灾害成因;根据研究区泥石流地质灾害的主要影像特征,提取影像的分类属性,基于面向对象的分类方法,建立信息提取模型,快速提取出地质灾害敏感区域;再进行人机交互,精确提取出地质灾害的类型和范围,通过野外验证,该方法十分可靠,为大范围地质灾害信息快速提取和灾后救援提供科学依据。     

Cartographically Wordy but not Necessarily Worthy

Abstract

A novel method called multidirectional visibility index (MVI) has been developed and verified. The MVI improves standard cartographic analytical shading with a number of enhancements to topographic detail and prominent structures, i.e. the portrayal of flat areas in lighter tones, the accentuation of morphologic edges, and the multiscale visualisation of morphologic terrain features. The procedure requires a digital elevation model (DEM) and involves the following steps: visibility mask computation; the respective multidirectional altering of the azimuth and elevation angle; the generation of continuous grid MVIs that indicate upper/lower views, quasi-slope, and relative relief; and an appropriate visualisation of the relevant MVI as a standalone technique or in combination with standard hill-shaded relief. The modelling parameters are robust and therefore highly adaptive to different landforms.     

海岛礁光学和雷达影像基于改进非子采样小波变换的自动配准

Homologous feature point extraction is a key problem in the optical and synthetic aperture radar （SAR） image registration for islands. A new feature point extraction method using a threshold shrink operator and non-subsampled wavelet transform （TSO-NSWT） for optical and SAR image registration was proposed. Moreover, the matching for this proposed feature was different from the traditional feature matching strategies and was performed using a similarity measure computed from neighborhood circles in low-frequency bands. Then, a number of reliably matched couples with even distributions were obtained, which assured the accuracy of the registration. Application of the proposed algorithm to SPOT-5 （multi-spectral） and YG-1 （SAR） images showed that a large number of accurately matched couples could be identified. Additionally, both of the root mean square error （RMSE） patterns of the registration parameters computed based on the TSO-NSWT algorithm and traditional NSWT algorithm were analyzed and compared, which further demonstrated the effectiveness of the proposed algorithm. The algorithm can supply the crucial step for island image registration and island recognition.