Remote Sensing Satellite Sensor Information Retrieval and Visualization based on SensorML Chuli Hu, Nengcheng Chen*, Chao Wang State Key Lab for Information Engineering in Surveying, Mapping and Remote Sensing Wuhan University 129 Luoyu Road, Wuhan 430079, China *Corresponding author, e-mail:
[email protected] Abstract—In the era of high-frequency occurrence of natural disasters, users are more urgently concerned with the sharing of the satellite sensor resources information and coordinating the complement of sensor observation. However, the capacity of discovering, retrieving and visualizing the sensor resource information accurately based on heterogeneous sensors over sensor network is very limited. This paper proposes the system architecture for effectively managing those heterogeneous and multiple sensors and their information, which is inspired by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) Initiative and based on one of its information model--Sensor Model Language (SensorML) of which Process Model is the core. The prototype "SensorModel V1.0" is designed and implemented used to construct the standard model for unified management of multiple remote sensing satellite sensor resources information and demonstrate the model-based retrieval and visualization of related remote sensors and their information, which promotes the comprehensive accessing and collaborative planning/controlling the available remote sensor’s information in time-critical disaster emergency. Index Terms- Remote sensing; Sensor Resource Intensive Information; Satellite Sensor Information Model; SensorML; Modelbased Management;
I.
INTRODUCTION
Nowadays, the natural disaster happens with quite highfrequency, especially the earthquakes. The discovery [1] of remote sensing satellite sensor information resources (i.e. basic information about the measurement hardware, sensor metadata, spatial-temporal based dynamic location and sensor observation processing) is an emerging topic to disaster emergency. Namely, timely and accurately retrieving the available sensors guarantees the time-critical disaster emergency processing, which can better serves to emergency processing/response and mitigates the loss of disaster damage. However, as the distribution and diversity of those remote sensing satellite sensor information resources, the abilities of how to effectively manage [2] and share, and collaborative find and visualize those sensor-intensive information resources [3-5] are very limited. A Sensor Web can be imagined or thought of as a “global sensor” that connects web-resident sensing devices and sensor databases, as well as people, machines and other users of these
resources [6]. Also the concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The generation of SWE 2.0 [7] initiative of the OGC standardizes web service interfaces and data encoding which can be used as building blocks for a Sensor Web. Sensor Model Language (SensorML)[8] is one of SWE standard information model, based on the SensorML 1.0, the version of 2.0 is in progress. SensorML specifies a model and XML-encoding for the description of all kinds of sensor system related processes. The processes can be atomic or composite and allow a detailed description of a process including a listing of its inputs, outputs, parameters, and process methods, they are capable of describing the metadata of sensor system, observing a phenomenon and returning an observed value. Also the SensorML process model can be executed by the related engine. In other words, the SensorML information model is not just as a description model, but also a function model that can transform the raw observing phenomenon to post processed result. As the diversity of satellite sensors, for which there is no standard description model and query mechanism. The goal of this paper is based on OGC SWE SensorML to establish the standard information model of satellite sensor resources, then model-based retrieve and visualize those resources’ information. Therefore, it promotes the comprehensive accessing and collaborative planning/controlling the available remote sensor’s information in time-critical disaster emergency. II.
SYSTEM ARCHETECTURE
The aim of the system architecture is to effectively manage those heterogeneous and multiple remote sensing satellite sensors and their information. As the OGC SWE SensorML have the following functionalities [9]: z Supporting the discovery and access of described sensors by integrating them into catalogues in Sensor Web. z Providing information that can be used for understanding and analyzing data produced by the sensor z Allowing the description of post processing steps that were performed on sensor data so that it can be reconstructed how a data set has been created.
Supported by National Basic Research program of China under Grant (No.2011CB707101) and Shenzhen R&D program (CXB200903090023A) and Natural Science Foundation of Hubei Province of China (2010CDB08403), LIESMARS Special Research Funding
978-1-4577-1005-6/11/$26.00 ©2011 IEEE
3425
IGARSS 2011
Therefore, SensorML has adopted as the standard description format to establish the remote sensing satellite sensor information model. The system architecture provides a new perspective for model-based management, including integrating those distributed sensors and their diverse information into a standard description model without being worried about integration problems and conflicts that arise due vendor-specific solutions (i.e. usage of proprietary communication protocols, interfaces, measurements data types and formats), retrieving the relevant sensor information based on the standard model and visualizing the result model in intuitive XML-encoding or 3Dbased spatial-temporal dynamic orbit information. As shown in Fig.1. The proposed system architecture includes three layers: Remote sensing satellite sensor information layer; Sensorintensive information resources management layer; Model-based Application layer.
is important to discover and plan which sensor available and which sensors collaborative based on SensorML model. Therefore, the model-based application like retrieval and visualization can aid decision-maker to better plan and rescue the earthquake disaster. III.
MEHODOLOGY
A. Management of disparate sensors and their information Management of disparate sensors and their information is to integrate and fuse them in a more general approach. Interoperability through the use of standard specifications, this paper adopts SensorML as the sensor description specification. For the remote sensing satellite sensor oriented modeling, the International Standard Organization (ISO) 19130[10] has been used since it is the Remote Sensing sensor metadata standard. It is to design the prototype system using common database (e.g. Oracle Sensor Data Repository) and common methods of access (e.g. SQL). In the front-end to provide the functionality of modeling and editing, and the standard query and visualization interface for the application of the corresponding required. B. Modeling of Remote satellite sensor information model
1). Process Oriented Modeling (POM)
Figure 1. Architecture of the system
A. Physical sensor information The layer of remote sensing satellite sensor information is mainly to present the information that should be described into the SensorML model. B. Model-based Sensor-intensive information resources management This layer is built on top of sensor standard specification, by comply with the SensorML description format, user establishes the SensorML-compliant model describing the remote sensing satellite sensor information in a unified pattern. The database can be built upon common databases like Oracle Spatial Database Server, OpenGIS Databases, etc. It is important that these database systems provide data that is encoded in standard formats like OGC SensorML, SWE Common data, etc. In brief, this layer is the core of the whole architecture, it plays a role in managing how to integrate those heterogeneous and diverse information resources into a unified model, then store those model into database. C. Application used in decision-making guidance For the disaster emergency response, such as earthquake, it
3426
In POM, sensors and sensor observation systems are modeled as processes converting the raw observing phenomenon to post-processed data, all processes contain input, output, parameter and MetaDataGroup properties. Especially each process provides sensor metadata as modeled by the MetaDataGroup, the MetaDataGroup contains properties such as the capabilities of the sensor, contact information and documentation references, etc. The most important is that POM divides processes into two types, non-physical and physical process. The core of POM is establishing the sensors and sensor observation data system in terms of process model, which mainly focuses on process of sensor observation data based on discovery of sensors and sensor observation system.
2). Abstract framework for Sensor Information Model Based on the proposed POM, this paper also proposes the abstract modeling framework for constructing remote satellite sensor information model. As shown in Fig.1, the abstract sensor information model framework consists of one or multi-process model including the non-physical processes and physical process model, which can be divided into four sub-modules as follows: Common Model, Process Method Model, Physical-Control Model, Composite Chain model.
Figure 2. RS-SSIM abstract framework based on SensorML
3). RS-SSIM template design How to tie abstract framework up with the rapid modeling? Our solution is to create the SensorML-based template, which has instantiated from the abstract framework and adopted the ISO 19130:2010 “Imagery sensor models for geopositioning” as the metadata standard. According to previously proposed four submodules, we formulate the corresponding templates, namely: Common template sets, Process Method template sets, PhysicalControl template sets, Composite Chain template sets. Table 1 shows the part of the mapping relationship from the ISO 19130 metadata sets to the proposed MetadataGroup template included in Common template sets.
of satellite sensor’s real-time dynamic orbit information (i.e. altitude, latitude, longitude and speed). All the other sub-modules of sensorML-based model are the same as Fig.3 that should be constructed from the value-blanked template to value-filled model through loading the template into the SensorModel prototype.
TABLE ǿ. MAPPING RELATIONSHIP FROM THE ISO 19130 METADATA SETS TO PROPOSED METADATAGROUP TEMPLATES SensorML metadata-group identification
Metadata sets defined by ISO 19130
classification
SenosrType, PlatformType, OrbitType, ScannerType, Application Sensor Rotation about Z-axis, Sensor Rotation about Yaxis, Sensor Rotation About X-axis, Sensor Coordinate Reference Orientation, Sensor position and altitude accuracy variance data, Column Spacing, Row Spacing, Various distortions a1, b1, c1, a2, b2, c2
characteristics
Sensor UID, Platform UID
C. Model-based to Retrieve and visualize RS-SSI It is the same as the conventional GIS query pattern, here retrieval to the sensor and its information relies on the unified RS-SSIM which has been stored in the database. The process of the visualization is to intuitive present the retrieved model in XML-encoding or 3D simulation. IV.
EXPERIMENT
Figure 3. Template-based modeling for RS sensors
B. Model-based text query As SensorML-based model is a structure within complex information contents, while the arrangement of them is clear. Therefore, the prototype uses the Lucene.Net library to build up the index of the whole model contents. Lucene query includes the composite Boolean Query (Accurate Query) and single Fuzzy Query. The index catalogue is constructed based on the inherent structure of SensorML, including: keyword, identifier, classifier, characteristic, etc. As shown in Fig.4, it is a retrieval example of composite Boolean Query where “keyword MUST SPOT-5 && classifier MUST_NOT CCD”.
SensorModel V1.0 is a process oriented modeling system developed by the State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) of Wuhan University in China. Based on the methods proposed in above sections, SensorModel can manage heterogeneous remote sensors efficiently. SensorModel V1.0 system consists of three parts: modeling and editing module, model-based resources query module, resource information visualization module. A. SPOT-5 sensor system modeling Here we use SPOT-5 sensor system as the modeling example; the modeling elements include platform system, the equipped sensors metadata description and the related sensor processing. As shown in Fig. 3, it presents the rapid transformation from the metadataGroup template to concrete metadataGroup model based on the above proposed method. The related processing here is to adopt the Simplified General Perturbations Satellite Orbit Model 4(SGP4)[11] algorithm as the process method, the satellite sensor Two Lines Element (TLE)[12] data regularly updated by NASA as the parameters, then enter a space-time query as the input, by executing the process method algorithm, last to yield the output
3427
Figure 4. Model-based text query
C. Model-based temporal -spatial query This section illustrates how the SensorML-based process models play their processing functionality. It assumes that there are a large number of RS-SSIMs have been store in model repository, and those RS-SSIMs are related to orbit predication
process adopting the SGP4 algorithm as process method respectively. Namely, user/client can enter the space-time query as Fig.5 shown to retrieve the satellite sensor from the RS-SSIM, the whole procedure reflects the process functionality of sensorML-based process model realized by executing the SGP4 process method through the process engine.
sensor standard description model lays the foundation for the heterogeneous and multiple remote sensors’ effective management. Also this SensorML-based model provides a single portal or query interface for those multi-sensors. Therefore, for disaster decision-makers, it is through such models that they can gain knowledge about the sensors they retrieved, the model-based retrieval and visualization of remote sensing satellite sensor information can help them to comprehensive and collaborative access and plan/control sensor, such as: which sensors are available in the specific condition and which sensors can complementary work in a single application. REFERENCES [1]
Figure 5. Model-based temporal-spatial query
D. Visualization of retrieved RS-SSI The Visualization of retrieved RS-SSI can be classified into two types, one is directly unfold the retrieved model into a XMLencoding; another is to simulate the dynamic orbit and real-time display the track information on the World Wind (an open source virtual globe developed by NASA) which has been integrated into our prototype. Fig.6 shows the 3D dynamic simulation information of the retrieved satellite sensor.
Figure 6. Visualization of retrieved RS-SSI
V.
CONCLUSIONS
A model-based architecture for the remote sensing satellite sensor information management system has been developed. OGC SWE sensorML information model is the core for enabling heterogeneous remote sensor unified management. This paper harmonizes SensorML with the remote sensor metadata standard like ISO 19130, and constructs the RS-SSIM, which can promote the sharing of those distributed sensors. This metadata-compliant
3428
S. Jirka, A. Bröring, and C. Stasch, “Discovery Mechanisms for the Sensor Web,” Sensors., vol. 9, no. 4, pp. 2661-2681, 2009. [2] G. W. Ng and K. H. Ng, “Sensor management - what, why and how,” Information Fusion., vol. 1, no. 2, pp. 67-75, 2000. [3] Y.Liu and S.K.Das, “Information-intensive wireless sensor networks: potential and challenges ,” Communications Magazine, IEEE ., Vol.44, no. 11, pp.142-147, 2006. [4] M.F.Goodchild, “Citizens as Sensors: The World of Volunteered Geography,” GeoJournal ., Vol. 69, no. 4. pp. 211-221, 2007. [5] B. Declan, “2020 computing: Everything, everywhere,” Nature, Vol.440, no.7083, pp. 402-405, 2006. [6] V. Tao, S. Liang, A. Croitoru, Z. Haider, and C. Wang, “GeoSWIFT: an Open Geospatial Sensing Services for Sensor Web,” GeoSensors Networks, pp. 267-274, 2003. [7] A. Bröring, J. Echterhoff, S. Jirka, I. Simonis, T. Everding, C. Stasch, S. Liang, and R. Lemmens. “New Generation Sensor Web Enablement,” sensors, vol. 11, no.3, pp. 2652-2699, 2011. [8] M. Botts. OpenGIS® Sensor Model Language (SensorML) Implementation Specification(Version 0.3.0). In: Geospatial Consortium, OGC, Document Number: 09-063, Wayland, MA, USA,2009,p. 180. [9] M. Botts, Sensor Model Language (SensorML) Introduction, [Online] Available http://www.ogcnetwork.net/SensorML. visited {12 Jan 2008} [10] L. Di, W. Kresse, and B. Kobler, “The current status and future plan of the ISO 19130 project,” Proceedings of the XXth ISPRS Congress., Istanbul, 2004. [11] T. Kim, "Investigation of rigorous sensor models and adjustment parameters for modeling satellite orbits," in Proc. Dig Int Geosci Remote Sens Symp (IGARSS), Jun. 23 -27, Barcelona, Spain, pp. 1299-1302, 2007. [12] R. V. De Moraes, H. K. Kuga and D. Y. Campos, “Orbital propagation for Brazilian satellites using NORAD models,”Adv. Space Res., vol. 30, no. 2, pp. 331-335, 2002.
