Xianfeng Song

Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image

This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg–Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested. The LM–ANN and RBF–ANN, which outperform the other two, were selected to make a detailed comparison with support vector machines (SVMs). The experiments show that those well-trained ANNs and SVMs have no significant difference in classification accuracy, but the SVM usually performs slightly better. Analysis of the effect of the training set size highlights that the SVM classifier has great tolerance on a small training set and avoids the problem of insufficient training of ANN classifiers. The testing also illustrates that the ANNs and SVMs can vary greatly with regard to training time. The LM–ANN can converge very quickly but not in a stable manner. By contrast, the training of RBF–ANN and SVM classifiers is fast and can be repeatable.

Early detection system of drought in East Asia using NDVI from NOAA/AVHRR data

Early detection system of drought in East Asia using NDVI from NOAA/ AVHRR data X. Song Corresponding author a , G. Saito b , M. Kodama c & H. Sawada d a Institute of Geographical Sciences & Natural Resources Research , Chinese Academy of Sciences , Building 917, Datun Road, Anwai, Beijing 100101, China E-mail: b National Institute for Agro-Environmental Sciences, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-864 , Japan c Computer Center for Agriculture , Forestry & Fisheries Research , 1-2-1 Kasumigaseki, Chiyodaku, Tokyo 100-8950, Japan d Forestry and Forest Products Research Institute, 1 Matunosato, Tsukuba, Ibaraki 305-8687 , Japan Published online: 03 Jun 2010.

A novel approach for generating routable road maps from vehicle GPS traces

Public vehicles and personal navigation assistants have become increasingly equipped with single-frequency global positioning system (GPS) receivers or loggers. These commonly used terminals offer an inexpensive way for acquiring large volumes of GPS traces, which contain information pertaining to road position and traffic rules. Using this new type of spatial data resource, we propose a novel approach for generating high-quality routable road maps. In this approach, a simplified road network graph model uses circular boundaries to separate all GPS traces into road intersections and road segments and builds road networks that maintain their identical geometric topologies through the entry/exit points at the original boundaries. One difficulty inherent to this type of approach is how to best determine the appropriate spatial coverage for road intersections. Conflict points among GPS traces that have large intersection angles usually occur within the physical areas of road intersections, particularly those involving left turns. Therefore, we determined a proper circle boundary for individual road intersections by conducting a spatial analysis of such feature points. This approach was implemented using Python and PostgreSQL/PostGIS and was tested in Huaibei City, China. Based on a comparison with human-interpreted results, the automatically generated routable road map was demonstrated to be of high quality and displayed detailed road networks with turning at various at-grade intersections, interchanges and U-turns.

Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China

Coal fires are common and serious phenomena in most coal-producing countries in the world. Coal fires not only burn valuable non-renewable coal reserves but also severely affect the local and global environment. The Rujigou coalfield in Shizuishan City, Ningxia, NW China, is well known for being a storehouse of anthracite coal. This coalfield is also known for having more coal fires than most other coalfields in China. In this study, an attempt was made to study the dynamics of coal fires in the Rujigou coalfield, from 2001 to 2007, using multi-temporal nighttime Landsat data. The multi-temporal nighttime short wave infrared (SWIR) data sets based on a fixed thresholding technique were used to detect and monitor the surface coal fires and the nighttime enhanced thematic mapper (ETM+) thermal infrared (TIR) data sets, based on a dynamic thresholding technique, were used to identify the thermal anomalies related to subsurface coal fires. By validating the coal fires identified in the nighttime satellite data and the coal fires extracted from daytime satellite data with the coal fire map (CFM) manufactured by field survey, we found that the results from the daytime satellite data had higher omission and commission errors than the results from the nighttime satellite data. Then, two aspects of coal fire dynamics were analyzed: first, a quantitative analysis of the spatial changes in the extent of coal fires was conducted and the results showed that, from 2001 to 2007, the spatial extent of coal fires increased greatly to an annual average area of 0.167 km2; second, the spreading direction and propagation of coal fires was analyzed and predicted from 2001 to 2007, and these results showed that the coal fires generally spread towards the north or northeast, but also spread in some places toward the east.

Automatic intersection and traffic rule detection by mining motor-vehicle GPS trajectories

Abstract The generation of road networks from ubiquitous motor-vehicle GPS trajectories has recently gained wide interest. However, few attempts have been made to automatically extract road network properties such as intersections and traffic rules to facilitate the production of high-quality routable maps. For urban street networks, the vehicle trajectory logged by a GPS receiver tends to be straight on streets and curved at intersections although the local deviation exists due to vehicle paths deviating from road centrelines and GPS positioning errors. This paper uses large curved trajectories at traffic intersections and presents novel algorithms for automatically detecting road intersections and traffic rules. Two inherent issues related to GPS trajectories have been resolved using the proposed approach. First, the serious fluctuations of vehicle trajectories due to multipath reflectivity from high-rise buildings have been eliminated, thereby enabling the effective detection of real curved trajectories occurring at traffic intersections. Second, the heterogeneity of traffic density has been considered when using the curved trajectories to automatically detect road intersections. The proposed algorithm was implemented using open-source software libraries and tested using large taxi trajectories collected in Suzhou City, China. A total of 285 at-grade intersections were detected automatically, and dynamic traffic rules were elucidated for each intersection. Compared with the manually interpreted results, the detection results were high quality and provided detailed information for the construction of a routable map.

Real-time monitoring portal for urban environment using sensor web technology

Taking advantage of the large-volume but low-cost datasets remotely sensed in urban areas by ubiquitous sensors or sensor networks, there is a need of real-time environmental monitoring portal or geospatial infrastructure for effectively or efficiently collecting and serving vast field data over web. Based on Open GIS Service Specifications defined by OGC Sensor Web Enablement (SWE), this paper presents an Internet based urban environment observation system that can real-time monitor environmental changes of temperature, humidity, illumination or air components in urban area. This system two functionality components. One is to collect and archive the environmental data from low-cost sensor network with user-customized time frequency through the proprietary system interfaces, the other is to support those datasets to be reformatted following Earth Observation (EO) specification and to be browsed or retrieved through the open interoperation interface of Sensor Observation Service (SOS). The real-time monitoring system is developed using Open GIS Specifications and Open Source Geospatial Software. The test-bed system is demonstrated as follows. The sensor kit deployed for constructing environmental monitoring network refers to the hardware implementation by GIS Research Group of OCU under Prof. Venkatesh. Currently, the factors of temperature, humidity, carbon dioxide and oxygen are sensed through outdoor sensors. The sensor data archive database is established using PostgreSQL and PostGIS platform and the web service system is run over Apache server. The data collection service and sensor data web service are programmed using Python, while the data visualization services are implemented using OpenLayers and Mapserver.

A Study of Coal Fire Propagation with Remotely Sensed Thermal Infrared Data

Coal fires are a common and serious problem in most coal-bearing countries. Thus, it is very important to monitor changes in coal fires. Remote sensing provides a useful technique for investigating coal fields at a large scale and for detecting coal fires. In this study, the spreading direction of a coal fire in the Wuda Coal Field (WCF), northwest China, was analyzed using multi-temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) thermal infrared (TIR) data. Using an automated method and based on the land surface temperatures (LST) that were retrieved from these thermal data, coal fires related to thermal anomalies were identified; the locations of these fires were validated using a coal fire map (CFM) that was developed via field surveys; and the cross-validation of the results was also carried out using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images. Based on the results from longtime series of satellite TIR data set, the spreading directions of the coal fires were determined and the coal fire development on the scale of the entire coal field was predicted. The study delineated the spreading direction using the results of the coal fire dynamics analysis, and a coal fire spreading direction map was generated. The results showed that the coal fires primarily spread north or northeast in the central part of the WCF and south or southwest in the southern part of the WCF. In the northern part of the WCF, some coal fires were spreading north, perhaps coinciding with the orientation of the coal belt. Certain coal fires scattered in the northern and southern parts of the WCF were extending in bilateral directions. A quantitative analysis of the coal fires was also performed; the results indicate that the area of the coal fires increased an average of approximately 0.101 km2 per year.

An OGC standard-oriented architecture for distributed coal mine map services

Abstract GIS- or CAD-based technology has been widely used for cartographic maps in coal mines, but structural gaps between such maps make it difficult to provide an integrated map service, for any specific purpose, at higher levels. There is no uniform platform that can be used to manage all involved maps. The main reason for this is that datasets are submitted by individual coal mines using their individual, diverse software. No consistent model is used within the software for data abstraction and symbolization. This paper first reviews all the essential specifications concerning OGC (Open Geospatial Consortium) interoperability. Then an OGC standard-oriented architecture is proposed to provide distributed coal mine map services. Within this new architecture the management of spatial data archives, and the integration of coal mine maps, are achieved through the interfaces of geospatial services. Finally an open source geospatial approach is suggested to implement the proposed scheme. A case study of the Huaibei Coal Group is used to demonstrate the proposal.

Spectral unmixing using linear unmixing under spatial autocorrelation constraints

This paper presents a spectral unmixing approach that is implemented using linear unminxing method by a genetic algorithm. The unmixing is constrained not only by the negativity and sum-to-one of the abundances of endmembers at each pixel but also by the spatial autocorrelation of their abundances among eight neighbor pixels. The Morans I indices are proposed to describe the spatial autocorrelation among a pixel and its neighborhood. Based on the above constraints, the objective of unmixing by genetic algorithm is to minimize the mean square error of mixed spectral values. We tested this approach using Chinese HJ-satellite images and obtained an acceptable result.

A weighted clustering algorithm for clarifying vehicle GPS traces

This paper presents a weighted clustering algorithm based on the physical attraction model, which improves the physical attraction model by assigning a different weight to the position points on a GPS trace for a fast convergence according to their velocity and directional changes. The physical attraction model pulls together traces that belong on the same road in response to simulated potential energy wells created around each trace. Assuming a vehicle with a high velocity has a little derivation to the road it runs on, we assign a high weight to its trace in the physical attraction model so that the clustering progress converges rapidly and closely to road. Within the clustering process, an angle-threshold based smoothing filter is appended to keep the consistency between the changes of those points on the adjusted trace at each loop. This algorithm was demonstrated to enable to effectively clarify those vehicle GPS traces on the same road. In comparison of previous work, it also shows an improved quality grouping GPS traces near road-crossing area by embedding the smoothing filter in clustering process.