How to Specify an Electro-optical Earth Observation Camera? A Review of the Terminologies Used and its Interpretation

Joseph, George

doi:10.1007/s12524-020-01105-8

Cited by 6 publications

(10 citation statements)

References 18 publications

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“…As a consequence, the exploitation of L1T data was not considered an impeding factor for their exploitation in this exercise. Importantly, the current analysis based on L8 OLI-L1T data allowed us to perform a sharpness assessment specifically referred to this processing level (i.e., following [5], it is quality information provided at the "product level").…”

Section: Validation Of the Sasbem: Methodological Approach 231 Data Sources And Study Areasmentioning

confidence: 99%

“…Importantly, results should be then interpreted accordingly, especially when comparing the outcome of the sharpness assessment against other results obtained by different analyses. The importance of providing quality information at "product level", in addition to the one provided at "instrument level", was highlighted by [5]. Schematic representation of the EM that from an edge target imaged by a sensor (a) allows first to estimate the its position with a sub-pixel precision (b), then to retrieve the corresponding ESF (c), LSF (d) and MTF (e); from which different sharpness metrics can be derived: the RER (c′), the FWHM (d′), and the MTF computed at different sampling frequencies (e').…”

Section: Image Sharpnessmentioning

confidence: 99%

“…In this framework, the overall image quality depends on several factors, among which the most relevant are the spatial resolution, the radiometric resolution, and the image sharpness [1][2][3][4][5]. Although important, the temporal resolution is not accounted in the abovementioned list because the image quality we are referring to is the one of a single product, without taking into account the added-value provided by the exploitation of a higher temporal resolution to perform multitemporal analyses (e.g., due to the exploitation of constellations of satellites carrying the same sensor).…”

Section: Introductionmentioning

confidence: 99%

“…The angle subtended by a single detector element on the axis of the optical system is called instantaneous field of view (IFOV), whereas its projection onto the Earth's surface is called ground-projected instantaneous field of view (GIFOV; also known as instantaneous geometric field of view-IGFOV) [1]. The mathematical description of these parameters can be found in [1,5,11], as well as additional details about the topic. End-users of EOderived products generally prefer to use the term GIFOV.…”

Section: Introductionmentioning

confidence: 99%

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Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

Cenci¹,

Pampanoni

Laneve

et al. 2021

Remote Sensing

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Developing reliable methodologies of data quality assessment is of paramount importance for maximizing the exploitation of Earth observation (EO) products. Among the different factors influencing EO optical image quality, sharpness has a relevant role. When implementing on-orbit approaches of sharpness assessment, such as the edge method, a crucial step that strongly affects the final results is the selection of suitable edges to use for the analysis. Within this context, this paper aims at proposing a semi-automatic, statistically-based edge method (SaSbEM) that exploits edges extracted from natural targets easily and largely available on Earth: agricultural fields. For each image that is analyzed, SaSbEM detects numerous suitable edges (e.g., dozens-hundreds) characterized by specific geometrical and statistical criteria. This guarantees the repeatability and reliability of the analysis. Then, it implements a standard edge method to assess the sharpness level of each edge. Finally, it performs a statistical analysis of the results to have a robust characterization of the image sharpness level and its uncertainty. The method was validated by using Landsat 8 L1T products. Results proved that: SaSbEM is capable of performing a reliable and repeatable sharpness assessment; Landsat 8 L1T data are characterized by very good sharpness performance.

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Section: Validation Of the Sasbem: Methodological Approach 231 Data Sources And Study Areasmentioning

confidence: 99%

Section: Image Sharpnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

Cenci¹,

Pampanoni

Laneve

et al. 2021

Remote Sensing

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show abstract

“…Nevertheless, a complete description of the quality of a spaceborne acquisition system can be given in terms of temporal resolution, spectral resolution, radiometric resolution and spatial resolution [2,3]. While the general concept of satellite image quality in its broadest sense depends on all these factors [4], in this paper we focus specifically on its declination in the context of high-resolution optical imaging systems [5].…”

Section: Introductionmentioning

confidence: 99%

Analysing the Relationship between Spatial Resolution, Sharpness and Signal-to-Noise Ratio of Very High Resolution Satellite Imagery Using an Automatic Edge Method

Pampanoni,

Fascetti,

Cenci

et al. 2024

Remote Sensing

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Assessing the performance of optical imaging systems is crucial to evaluate their capability to satisfy the product requirements for an Earth Observation (EO) mission. In particular, the evaluation of image quality is undoubtedly one of the most important, critical and problematic aspects of remote sensing. It involves not only pre-flight analyses, but also continuous monitoring throughout the operational lifetime of the observing system. The Ground Sampling Distance (GSD) of the imaging system is often the only parameter used to quantify its spatial resolution, i.e., its capability to resolve objects on the ground. In practice, this feature is also heavily influenced by other image quality parameters such as the image sharpness and Signal-to-Noise Ratio (SNR). However, these last two aspects are often analysed separately, using unrelated methodologies, complicating the image quality assessment and posing standardisation issues. To this end, we expanded the features of our Automatic Edge Method (AEM), which was originally developed to simplify and automate the estimate of sharpness metrics, to also extract the image SNR. In this paper we applied the AEM to a wide range of optical satellite images characterised by different GSD and Pixel Size (PS) with the objective to explore the nature of the relationship between the components of overall image quality (image sharpness, SNR) and product geometric resampling (expressed in terms of GSD/PS ratio). Our main objective is to quantify how the sharpness and the radiometric quality of an image product are affected by different product geometric resampling strategies, i.e., by distributing imagery with a PS larger or smaller than the GSD of the imaging system. The AEM allowed us to explore this relationship by relying on a vast amount of data points, which provide a robust statistical significance to the results expressed in terms of sharpness metrics and SNR means. The results indicate the existence of a direct relationship between the product geometric resampling and the overall image quality, and also highlight a good degree of correlation between the image sharpness and SNR.

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Target acquisition performance in the presence of JPEG image compression

Bondžulić,

Stojanović,

Lukin

et al. 2024

Defence Technology

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How to Specify an Electro-optical Earth Observation Camera? A Review of the Terminologies Used and its Interpretation

Cited by 6 publications

References 18 publications

Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

Presenting a Semi-Automatic, Statistically-Based Approach to Assess the Sharpness Level of Optical Images from Natural Targets via the Edge Method. Case Study: The Landsat 8 OLI–L1T Data

Analysing the Relationship between Spatial Resolution, Sharpness and Signal-to-Noise Ratio of Very High Resolution Satellite Imagery Using an Automatic Edge Method

Target acquisition performance in the presence of JPEG image compression

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