7d4623b055cea4a546f299f5756d2a2f remsen_a2021v13n19p3808.pdf 5118e3c8a26e32c8ff8c5c2e2b992916d48474b3 remsen_a2021v13n19p3808.pdf 8e6b40fcdd2869969854b2f8035756b2e161f2b26a312017340c0be64b0a2671 remsen_a2021v13n19p3808.pdf Title: An Operational Radiometric Correction Technique for Shadow Reduction in Multispectral UAV Imagery Subject: This study focuses on the recovery of information from shadowed pixels in RGB or multispectral imagery sensed from unmanned aerial vehicles (UAVs). The proposed technique is based on the concept that a property characterizing a given surface is its spectral reflectance, i.e., the ratio between the flux reflected by the surface and the radiant flux received by the surface, and this ratio is usually similar under direct-plus-diffuse irradiance and under diffuse irradiance when a Lambertian behavior can be assumed. Scene-dependent elements, such as trees, shrubs, man-made constructions, or terrain relief, can block part of the direct irradiance (usually sunbeams), in which part of the surface only receives diffuse irradiance. As a consequence, shadowed surfaces comprising pixels of the image created by the UAV remote sensor appear. Regardless of whether the imagery is analyzed by means of photointerpretation or digital classification methods, when the objective is to create land cover maps, it is hard to treat these areas in a coherent way in terms of the areas receiving direct and diffuse irradiance. The hypothesis of the present work is that the relationship between irradiance conditions in shadowed areas and non-shadowed areas can be determined by following classical empirical line techniques for fulfilling the objective of a coherent treatment in both kinds of areas. The novelty of the presented method relies on the simultaneous recovery of information in non-shadowed and shadowed areas by the in situ spectral reflectance measurements of characterized Lambertian targets followed by smoothing of the penumbra area. Once in the lab, firstly, we accurately detected the shadowed pixels by combining two well-known techniques for the detection of the shadowed areas: (1) using a physical approach based on the sun’s position and the digital surface model of the area covered by the imagery; and (2) the image-based approach using the histogram properties of the intensity image. In this paper, we present the benefits of the combined usage of both techniques. Secondly, we applied a fit between non-shadowed and shadowed areas by using a twin set of spectrally characterized target sets. One set was placed under direct and diffuse irradiance (non-shadowed targets), whereas the second set (with the same spectral characteristics) was placed under diffuse irradiance (shadowed targets). Assuming that the reflectance of the homologous targets of each set was the same, we approximated the diffuse incoming irradiance through an empirical line correction. The model was applied to all detected shadowed areas in the whole scene. Finally, a smoothing filter was applied to the penumbra transitions. The presented empirical method allowed the operational and coherent recovery of information from shadowed areas, which is very common in high-resolution UAV imagery. Keywords: shadow detection; shadow reduction; field spectroradiometry; radiometric correction; UAS; UAV; drone Author: Xavier Pons and Joan-Cristian Padró Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Sep 24 02:45:06 2021 CEST ModDate: Fri Sep 24 08:22:07 2021 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 6462600 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- MAXBFI+URWPalladioL-Ital Type 1 Custom yes yes no 10 0 WZOAES+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 SVTIND+URWPalladioL-Roma Type 1 Custom yes yes no 19 0 SOZKOC+VnURWPalladioL-Bold Type 1 Custom yes yes no 23 0 CIQLLF+VnURWPalladioL Type 1 Custom yes yes no 28 0 ZKJPJD+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 36 0 MCBCCI+SourceSansRoman-Regular 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WinAnsi yes yes no 261 0 PMJHHL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 275 0 PMJHHN+PalatinoLinotype TrueType WinAnsi yes yes no 278 0 PMJHHP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 281 0 PMJHHL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 296 0 PMJHHN+PalatinoLinotype TrueType WinAnsi yes yes no 299 0 PMJHHP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 302 0 PMJHHL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 318 0 PMJHHN+PalatinoLinotype TrueType WinAnsi yes yes no 321 0 PMJHHP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 324 0 PMJHHL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 339 0 PMJHHN+PalatinoLinotype TrueType WinAnsi yes yes no 342 0 PMJHHP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 345 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2022-02-11 02:27:24 CET RepresentationInformation: remsen_a2021v13n19p3808.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2022-02-10 11:34:07 CET Size: 6462600 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 530 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Children: Item: Title: Experiment Conceptualization and Materials Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Methods: Shadow Detection, Shadow Reduction, Edge Filtering Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Children: Item: Title: Shadow Detection Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Shadow Reduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Edge Filtering Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Methods: Statistical Data Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Info: Title: An Operational Radiometric Correction Technique for Shadow Reduction in Multispectral UAV Imagery Author: Xavier Pons and Joan-Cristian Padró Subject: This study focuses on the recovery of information from shadowed pixels in RGB or multispectral imagery sensed from unmanned aerial vehicles (UAVs). The proposed technique is based on the concept that a property characterizing a given surface is its spectral reflectance, i.e., the ratio between the flux reflected by the surface and the radiant flux received by the surface, and this ratio is usually similar under direct-plus-diffuse irradiance and under diffuse irradiance when a Lambertian behavior can be assumed. Scene-dependent elements, such as trees, shrubs, man-made constructions, or terrain relief, can block part of the direct irradiance (usually sunbeams), in which part of the surface only receives diffuse irradiance. As a consequence, shadowed surfaces comprising pixels of the image created by the UAV remote sensor appear. Regardless of whether the imagery is analyzed by means of photointerpretation or digital classification methods, when the objective is to create land cover maps, it is hard to treat these areas in a coherent way in terms of the areas receiving direct and diffuse irradiance. The hypothesis of the present work is that the relationship between irradiance conditions in shadowed areas and non-shadowed areas can be determined by following classical empirical line techniques for fulfilling the objective of a coherent treatment in both kinds of areas. The novelty of the presented method relies on the simultaneous recovery of information in non-shadowed and shadowed areas by the in situ spectral reflectance measurements of characterized Lambertian targets followed by smoothing of the penumbra area. Once in the lab, firstly, we accurately detected the shadowed pixels by combining two well-known techniques for the detection of the shadowed areas: (1) using a physical approach based on the sun’s position and the digital surface model of the area covered by the imagery; and (2) the image-based approach using the histogram properties of the intensity image. In this paper, we present the benefits of the combined usage of both techniques. Secondly, we applied a fit between non-shadowed and shadowed areas by using a twin set of spectrally characterized target sets. One set was placed under direct and diffuse irradiance (non-shadowed targets), whereas the second set (with the same spectral characteristics) was placed under diffuse irradiance (shadowed targets). Assuming that the reflectance of the homologous targets of each set was the same, we approximated the diffuse incoming irradiance through an empirical line correction. The model was applied to all detected shadowed areas in the whole scene. Finally, a smoothing filter was applied to the penumbra transitions. The presented empirical method allowed the operational and coherent recovery of information from shadowed areas, which is very common in high-resolution UAV imagery. 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