Methods and metrics for the assessment of Pan-sharpening algorithms

Disaggregation of remotely sensed land surface temperature: A simple yet flexible index (SIFI) to assess method performances

“…Practitioners may also turn to other advanced indexes in OIF that were recently developed for evaluations, such as the four bands multispectral images fusion index (Q4), the Quality with No Reference (QNR) (Vivone et al, 2014), or the combination of various indexes (Despini et al, 2014). However, although it inherited some traits from the OIF, DLST has differed from its counterpart in the following two regards.…”

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

“…EO value-adding products and services delivered by existing hybrid EO-IUSs whose input is a MS image class-conditioned (masked) by static color names encompass a large variety of low-level EO image enhancement tasks, ranging from MS image compositing to atmospheric and topographic correction of top-of-atmosphere reflectance (TOARF) into SURF values (Ackerman et al, 1998; Baraldi et al, 2010c; Baraldi & Humber, 2015; Baraldi et al, 2013; Despini, Teggi, & Baraldi, 2014; DLR and VEGA, 2011; Dorigo et al, 2009; Lück & Van Niekerk, 2016; Luo, Trishchenko, & Khlopenkov, 2008; Richter & Schläpfer, 2012a, 2012b; Vermote & Saleous, 2007) and high-level EO image understanding applications, including EO image time-series classification, ranging from cloud/cloud-shadow detection to burned area recognition (Arvor, Madiela, & Corpetti, 2016; Baraldi, 2015; Baraldi et al, 2010a, 2010b; Boschetti, Roy, Justice, & Humber, 2015; DLR & VEGA, 2011; Lück & Van Niekerk, 2016; Muirhead & Malkawi, 1989; Simonetti, Simonetti, Szantoi, Lupi, & Eva, 2015; GeoTerraImage, 2015). To the best of these authors’ knowledge, none of these prior knowledge-based MS reflectance space partitioners presented in the RS literature has ever been submitted to a GEO-CEOS stage 4 Val process (GEO-CEOS WGCV, 2015), in compliance with the GEO-CEOS QA4EO Val requirements (GEO-CEOS, 2010).…”

“…There is a long history of prior knowledge-based MS reflectance space partitioners for static color naming developed but never validated by space agencies, public organizations and private companies for use in hybrid EO-IUSs in operating mode. EO value-adding products and services targeted by existing hybrid EO-IUSs conditioned by static color naming encompass a large variety of low-level EO image enhancement tasks (Ackerman et al 1998;Luo et al 2008;Lück and van Niekerk 2016;Richter and D. Schläpfer 2012b;Baraldi, Humber and Boschetti 2013; Baraldi and Humber 2015;Dorigo et al 2009;Vermote and Saleous 2007;DLR and VEGA 2011;Lück and van Niekerk 2016;Baraldi et al 2010c;Despini et al 2014) and high-level EO image understanding applications DLR and VEGA 2011;Lück and van Niekerk 2016;Muirhead and Malkawi 1989;Simonetti et al 2015;GeoTerraImage 2015;Arvor et al 2016;Boschetti et al 2015;Baraldi et al 2010aBaraldi et al , 2010b. The potential impact on existing or future hybrid EO-IUSs in operating mode of an original (to the best of these authors' knowledge, the first) outcome and process Stage 4 Val of an off-the-shelf SIAM lightweight computer program for prior knowledge-based MS reflectance space hyperpolyhedralization, superpixel detection and per-pixel VQ quality assessment is expected to be relevant.…”

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

“…In the RS discipline, there is a long history of prior knowledge-based MS reflectance space partitioners for static color naming, alternative to SIAM’s, developed but never validated by space agencies, public organizations and private companies for use in hybrid EO-IUSs in operating mode, see Figure 11. Examples of hybrid EO image pre-processing applications in the quantitative/sub-symbolic domain of information-as-thing , where a numeric input variable is statistically class-conditioned (masked) by a static color naming first stage to generate as output another numeric variable considered more informative than the input one,  are large-scale MS image compositing (Ackerman et al 1998; Lück & van Niekerk, 2016; Luo, Trishchenko, & Khlopenkov, 2008), MS image atmospheric correction and topographic correction (Baraldi, 2017; Baraldi et al, 2010b; Baraldi & Humber, 2015; Baraldi et al, 2013; Bishop & Colby, 2002; Bishop et al, 2003; DLR & VEGA, 2011; Dorigo et al, 2009; Lück & van Niekerk, 2016; Riano et al, 2003; Richter & Schläpfer, 2012a, 2012b; Vermote & Saleous, 2007), see Figure 12, MS image adjacency effect correction (DLR & VEGA, 2011) and radiometric quality assessment of pan-sharpened MS imagery (Baraldi, 2017; Despini, Teggi, & Baraldi, 2014). Examples of hybrid EO image classification applications in the qualitative/equivocal/categorical domain of information-as-data-interpretation and statistically class-conditioned by a static color naming first stage are cloud and cloud-shadow quality layer detection (Baraldi, 2015, 2017; Baraldil., DLR & VEGA, 2011; Lück & van Niekerk, 2016), single-date LC classification (DLR & VEGA, 2011; GeoTerraImage, 2015; Lück & van Niekerk, 2016; Muirhead & Malkawi, 1989; Simonetti et al 2015a), multi-temporal post-classification LC change (LCC)/no-change detection (Baraldi, 2017; Baraldi et al, 2016; Simonetti et al, 2015a; Tiede, Baraldi, Sudmanns, Belgiu, & Lang, 2016), multi-temporal vegetation gradient detection and quantization into fuzzy sets (Arvor, Madiela, & Corpetti, 2016), multi-temporal burned area detection (Boschetti, Roy, Justice, & Humber, 2015), and prior knowledge-based LC mask refinement (cleaning) of supervised data samples employed as input to supervised data learning EO-IUSs (Baraldi et al, 2010a, 2010b).…”

“…In the RS discipline there is a long history of prior knowledge-based MS reflectance space partitioners for static color naming, alternative to SIAM's, developed but never validated by space agencies, public organizations and private companies for use in hybrid EO-IUSs in operating mode, see Figure 1-10. Examples of hybrid EO data pre-processing applications (information-as-thing) conditioned by static color naming are large-scale MS image compositing (Ackerman et al 1998;Luo et al 2008;Lück and van Niekerk 2016), MS image atmospheric correction (Richter and D. Schläpfer 2012a;Richter and D. Schläpfer 2012b;Baraldi, Humber and Boschetti 2013;Baraldi and Humber 2015;Dorigo et al 2009;Vermote and Saleous 2007;DLR and VEGA 2011;Lück and van Niekerk 2016), MS image topographic correction (Richter and D. Schläpfer 2012a;Richter and D. Schläpfer 2012b;Baraldi, Humber and Boschetti 2013;Baraldi and Humber 2015;Dorigo et al 2009;Baraldi et al 2010c;DLR and VEGA 2011;Lück and van Niekerk 2016), see Figure 1-11, MS image adjacency effect correction (DLR and VEGA 2011) and radiometric quality assurance of pan-sharpened MS imagery (Despini et al 2014). Examples of hybrid EO image classification applications (informationas-data-interpretation) conditioned by static color naming are cloud and cloud-shadow quality layer detection DLR and VEGA 2011;Lück and van Niekerk 2016), single-date LC classification (Muirhead and Malkawi 1989;Simonetti et al 2015a;GeoTerraImage 2015;DLR and VEGA 2011;Lück and van Niekerk 2016), multi-temporal post-classification LC change (LCC)/nochange detection (Baraldi et al 2016;…”