Data Quality in Remote Sensing

Batini, Carlo; Blaschke, Thomas; Lang, Stefan; Albrecht, Florian; Abdulmutalib, H. M.; Barsi, Árpád; Szabó, György; Kugler, Zsofia

doi:10.5194/isprs-archives-xlii-2-w7-447-2017

Cited by 25 publications

(9 citation statements)

References 8 publications

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“…Many studies emphasize the significance of spatial data quality indicators. The growing interest in data quality is partly due to the proliferation of digital services that utilize remote sensing data [17,18]. The Quality Assurance for Earth Observation (QA4EO) initiative points out that all data and derived products must have a Quality Indicator based on a statistically derived value that must be unequivocal and universal in terms of its definition and derivation [18].…”

Section: Introductionmentioning

confidence: 99%

Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications

2020

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Nanosatellites are increasingly being used in space-related applications to demonstrate and test scientific capability and engineering ingenuity of space-borne instruments and for educational purposes due to their favourable low manufacturing costs, cheaper launch costs, and short development time. The use of CubeSat to demonstrate earth imaging capability has also grown in the last two decades. In 2017, a South African company known as Space Commercial Services launched a low-orbit nanosatellite named nSight-1. The demonstration nanosatellite has three payloads that include a modular designed SCS Gecko imaging payload, FIPEX atmospheric science instrument developed by the University of Dresden and a Radiation mitigation VHDL coding experiment supplied by Nelson Mandela University. The Gecko imager has a swath width of 64 km and captures 30 m spatial resolution images using the red, green, and blue (RGB) spectral bands. The objective of this study was to assess the interpretability of nSight-1 in the spatial dimension using Landsat 8 as a reference and to recommend potential earth observation applications for the mission. A blind image spatial quality evaluator known as Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) was used to compute the image quality for nSight-1 and Landsat 8 imagery in the spatial domain and the National Imagery Interpretability Rating Scale (NIIRS) method to quantify the interpretability of the images. A visual interpretation was used to propose some potential applications for the nSight1 images. The results indicate that Landsat 8 OLI images had significantly higher image quality scores and NIIRS results compared to nSight-1. Landsat 8 has a mean of 19.299 for the image quality score while nSight-1 achieved a mean of 25.873. Landsat 8 had NIIRS mean of 2.345 while nSight-1 had a mean of 1.622. The superior image quality and image interpretability of Landsat could be attributed for the mature optical design on the Landsat 8 satellite that is aimed for operational purposes. Landsat 8 has a GDS of 30-m compared to 32-m on nSight-1. The image degradation resulting from the lossy compression implemented on nSight-1 from 12-bit to 8-bit also has a negative impact on image visual quality and interpretability. Whereas it is evident that Landsat 8 has the better visual quality and NIIRS scores, the results also showed that nSight-1 are still very good if one considers that the categorical ratings consider that images to be of good to excellent quality and a NIIRS mean of 1.6 indicates that the images are interpretable. Our interpretation of the imagery shows that the data has considerable potential for use in geo-visualization and cartographic land use and land cover mapping applications. The image analysis also showed the capability of the nSight-1 sensor to capture features related to structural geology, geomorphology and topography quite prominently.

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Section: Introductionmentioning

confidence: 99%

Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications

2020

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“…Based on the prior papers of the working group (Batini et al 2017, Albrecht et al 2018, Barsi et al 2018, Kugler et al 2018 this paper aims at giving a brief overview of existing data quality schemes described by standards. Followed by the establishment of a quality model scheme for RS domain based on Batini's concept for information technology domain (Batini and Scannapieco 2016).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing data quality model: from data sources to lifecycle phases

Barsi

Kugler

Juhász

et al. 2019

International Journal of Image and Data Fusion

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The importance of data quality assessment has significantly increased with the boom of information technology and the growing demand for remote sensing (RS) data. The Remote Sensing Data Quality Working Group of the International Society for Photogrammetry and Remote Sensing aimed to conduct an investigation on the principles of data quality. Literature review revealed that most publications introduce data quality models for application specific processing chains and quality schemes are built case by case with particular domain indicators only. Yet no general concept independent from applications has been developed so far. This paper focuses on the formulation of a RS quality concept adopted from information technology domain describing a triangular RS data quality scheme that relates data sources, quality dimensions and lifecycle phases. Following the introduction it provides examples of international standards and fundamentals of theoretic quality modelling. After a short overview on platforms/sensors, definitions of different quality dimensions are presented with their metrics organised in clusters (like resolution or accuracy). The main achievement of the paper relates lifecycle phases to different quality dimensions of high relevance. The objective is not only to address experts of RS but to raise awareness of uncertainty for the general RS user community.

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“…In addition, the business model includes further parameters like a minimum AOI size and maximum complexity of AOI (by number of vertices), as well as tasking priority, that ensure EO return of investments for data providers. Further information provides Batini et al (2017) that discuss the cost-benefit relation in context of an economic perspective on RS DQ improvement.…”

Section: Rs Quality Dimension Used In Relevant For Relevant Formentioning

confidence: 99%

“…Beyond QC related to structural properties of RS images and RS-derived information products (as handled by Batini et al 2017), the implementation of a service that provides RS-derived information on a regular basis introduces processrelated image QC like the timeliness of information provisioning. While the RS analyst is in control of the time they spend on producing RS-derived information from RS images, the time to acquisition is outside of their control and resides with the EO data provider.…”

Section: Rs Quality Dimension Used In Relevant For Relevant Formentioning

confidence: 99%

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Providing Data Quality Information for Remote Sensing Applications

Albrecht

Blaschke

Lang

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The availability and accessibility of remote sensing (RS) data, cloud processing platforms and provided information products and services has increased the size and diversity of the RS user community. This development also generates a need for validation approaches to assess data quality. Validation approaches employ quality criteria in their assessment. Data Quality (DQ) dimensions as the basis for quality criteria have been deeply investigated in the database area and in the remote sensing domain. Several standards exist within the RS domain but a general classification -established for databases -has been adapted only recently. For an easier identification of research opportunities, a better understanding is required how quality criteria are employed in the RS lifecycle. Therefore, this research investigates how quality criteria support decisions that guide the RS lifecycle and how they relate to the measured DQ dimensions. Subsequently follows an overview of the relevant standards in the RS domain that is matched to the RS lifecycle. Conclusively, the required research needs are identified that would enable a complete understanding of the interrelationships between the RS lifecycle, the data sources and the DQ dimensions, an understanding that would be very valuable for designing validation approaches in RS.

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Data Quality in Remote Sensing

Cited by 25 publications

References 8 publications

Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications

Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications

Remote sensing data quality model: from data sources to lifecycle phases

Providing Data Quality Information for Remote Sensing Applications

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