Toward a Standardized Encoding of Remote Sensing Geo-Positioning Sensor Models

Abstract

Geolocation information is an important feature of remote sensing image data that is captured through a variety of passive or active observation sensors, such as push-broom electro-optical sensor, synthetic aperture radar (SAR), light detection and ranging (LIDAR) and sound navigation and ranging (SONAR). As a fundamental processing step to locate an image, geo-positioning is used to determine the ground coordinates of an object from image coordinates. A variety of sensor models have been created to describe geo-positioning process. In particular, Open Geospatial Consortium (OGC) has defined the Sensor Model Language (SensorML) specification in its Sensor Web Enablement (SWE) initiative to describe sensors including the geo-positioning process. It has been realized using syntax from the extensible markup language (XML). Besides, two standards defined by the International Organization for Standardization (ISO), ISO 19130-1 and ISO 19130-2, introduced a physical sensor model, a true replacement model, and a correspondence model for the geo-positioning process. However, a standardized encoding for geo-positioning sensor models is still missing for the remote sensing community. Thus, the interoperability of remote sensing data between application systems cannot be ensured. In this paper, a standardized encoding of remote sensing geo-positioning sensor models is introduced. It is semantically based on ISO 19130-1 and ISO 19130-2, and syntactically based on OGC SensorML. It defines a cross mapping of the sensor models defined in ISO 19130-1 and ISO 19130-2 to the SensorML, and then proposes a detailed encoding method to finalize the XML schema (an XML schema here is the structure to define an XML document), which will become a profile of OGC SensorML. It seamlessly unifies the sensor models defined in ISO 19130-1, ISO 19130-2, and OGC SensorML. By enabling a standardized description of sensor models used to produce remote sensing data, this standard is very promising in promoting data interoperability, mobility, and integration in the remote sensing domain.

Cognitive Soil Digital Twin for Monitoring the Soil Ecosystem: A Conceptual Framework

“…Data may be subject to curation (e.g., outlier removal) [64] or other forms of pre-processing (e.g., the generation of second-order products like the transformation of dielectric permittivity to soil moisture). The data are then stored in a distributed big data management system ideally in an analysis-ready format using pre-specified protocols and standards [65], which offers multifaceted advantages such as scalability, performance, resilience, flexibility, and data consistency.…”

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

“…In order to acquire the terrain information of the target and solve the problem that the positioning algorithm based on the earth's ellipsoid model is too affected by the elevation, plenty of active positioning algorithms have been developed [19][20][21][22][23][24][25]. This type of algorithm mainly relies on ranging equipment such as laser range finder (LRF) to compute the distance between the camera and the target point, and calculates based on the positional relationship between the object images.…”

“…W k and V k are zero-mean white Gaussian noise, which are uncorrelated and represent systematic and random noise, respectively. Since the target is fixed, the state transition matrix φ k+1,k and the covariance matrix Q k of the systematic error can be expressed as Equation (23).…”

“…[12] realizes the localization of the ground target by the aerial camera through a single image while reducing the influence of the curvature of the earth on the localization outcome. In addition, the sourced positioning algorithms proposed gradually achieve higher level accuracy through using distance information generated by devices such as the laser range finder (LRF) or synthetic aperture radar (SAR) [18][19][20][21][22][23][24][25][26][27]. By utilizing the angle information provided by the airborne photoelectric platform, Tan [26] established a multi-target positioning model and proposed the pixel line-of-sight vector method through the parameters of the laser range finder and angle sensor.…”