16f36fce87a7a87a595fc0a9f44f4d37 remsen_a2021v13n14p2662.pdf 6bc51db8dc3500acf61bb5b47f7e1d1ced5593ce remsen_a2021v13n14p2662.pdf 4843f927441b020b36ad348c39eeaa2c25adf8837a1d74cf9763a9937643d266 remsen_a2021v13n14p2662.pdf Title: A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps Subject: Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered “true” per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential. Keywords: land-cover change mapping; Landsat; digital image classification; ground truth samples; filtering rules Author: Mario Padial-Iglesias, Pere Serra, Miquel Ninyerola and Xavier Pons Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Mon Jul 12 05:55:59 2021 CEST ModDate: Mon Jul 12 06:10:43 2021 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 36 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 20482190 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- QGKMEZ+VnURWPalladioL Type 1 Custom yes yes no 10 0 MIRMPO+URWPalladioL-Ital Type 1 Custom yes yes no 15 0 GHOMWW+URWPalladioL-Bold Type 1 Custom yes yes no 20 0 YAXOSN+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 ZKJPJD+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 32 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes 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yes yes no 560 0 DFDKKG+CambriaMath CID TrueType Identity-H yes yes yes 563 0 DFCODM+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 576 0 DFCODO+PalatinoLinotype TrueType WinAnsi yes yes no 579 0 DFCOEA+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 582 0 DFCODM+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 599 0 DFCODO+PalatinoLinotype TrueType WinAnsi yes yes no 602 0 DFCOEA+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 605 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-05-27 03:30:09 CEST RepresentationInformation: remsen_a2021v13n14p2662.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-03-31 11:58:07 CEST Size: 20482190 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 929 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@44c8afef Item: Title: Study Area Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Item: Title: Materials Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Children: Item: Title: Remote Sensing Images Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Ancillary Data Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: Ground Truth from a Reference Dataset (rDS) Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Children: Item: Title: Image Pre-Processing Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Children: Item: Title: Geometric and Radiometric Correction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Spectral Indices Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: Ground Truth (Training and Test Samples) Treatment Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Children: Item: Title: Spectral-Temporal Signatures Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Identification and Analysis of Inconsistencies Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: NDVI Filtering Rules Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Classification Process and Accuracy Assessment Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Children: Item: Title: The Performance of the Rules in Visual Examples Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@3aeaafa6 Item: Title: The Performance of the Rules in Confusion Matrices Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@76a3e297 Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4d3167f4 Item: Title: Limitations and Future Research Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@ed9d034 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6121c9d6 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@87f383f Info: Title: A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps Author: Mario Padial-Iglesias, Pere Serra, Miquel Ninyerola and Xavier Pons Subject: Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered “true” per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential. 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