Fusion of optical and SAR satellite data for environmental monitoring: assessment of damages and disturbances originated by forest fires

Alparone, Luciano; Arienzo, Alberto; Garzelli, Andrea; Lolli, Simone; Zoppetti, Claudia

doi:10.1117/12.2638984

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…1), are provided at 20 m spatial resolution. Thus, MS hyper-sharpening may improve the spatial resolution of the Sentinel-2 bands acquired at 20 and 60 m and the derived spectral indexes, specifically those computed from the RE bands, suitable for studies on vegetation [17], [18].…”

Section: Scenario and Motivationsmentioning

confidence: 99%

Spatial Resolution Enhancement of Satellite Hyperspectral Data via Nested Hypersharpening With Sentinel-2 Multispectral Data

Alparone,

Arienzo,

Garzelli

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The paper presents an original method for the spatial resolution enhancement of satellite hyperspectral (HS) data by means of the Sentinel-2 visible-near infrared (VNIR) and shortwave infrared (SWIR) bands at 10 and 20 m spatial resolution. Presently, HS data are available from PRISMA (Italian acronym for hyperspectral precursor of the application mission) and EnMAP (environmental mapping and analysis program): both map the spectral interval of the solar radiation onto 240 and 224 bands, respectively, with 10 nm and 6.5/10 nm widths. A 5 m×5 m panchromatic (PAN) band is also acquired by PRISMA.

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Section: Scenario and Motivationsmentioning

confidence: 99%

Spatial Resolution Enhancement of Satellite Hyperspectral Data via Nested Hypersharpening With Sentinel-2 Multispectral Data

Alparone,

Arienzo,

Garzelli

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Increasing the Detection Accuracy of Bi-Temporal Changes via Speckle Whitening of Single-Look Complex Synthetic Aperture Radar Images

Alparone,

Argenti,

Arienzo

et al. 2024

IEEE Access

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This study employs an unsupervised procedure to spatially decorrelate fully-developed speckle in single-look complex (SLC) synthetic aperture radar (SAR) images. The goal is evaluating the extent to which the spatial correlation of the noise induced by the SAR processor affects the detection accuracy of temporal variations of land-cover between two one-look images of the same landscape acquired on different dates. To simulate the scenario, we have spatially correlated a synthetic map of white complex circular symmetric Gaussian noise by using a two-dimensional separable Hamming window in the Fourier domain. The correlated complex speckle field has been modulated by a noise-free optical view, to simulate an SLC SAR image. Subsequently, we have reduced the correlation of the SLC image through a whitening process and calculated the modulus of the complex image. We have applied various methods of statistical change detection for real-valued SAR data, and compared the accuracy of change maps in the following cases: i) ideally uncorrelated noise, ii) correlated noise, iii) correlated noise that has been decorrelated. The study considers three change detection algorithms, ranging from the basic Log-Ratio operator preceded by despeckling to advanced parametric and nonparametric methods based on Kullback-Leibler distance and mean-shift clustering of bivariate scatterplots of local means. Simulation results demonstrate consistent performance improvements, in terms of both geometric accuracy and reduced number of false alarms.INDEX TERMS Inverse filtering, noise whitening, SAR image change detection, SAR processor, singlelook complex (SLC) images, synthetic aperture radar (SAR), tapering windows.

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Full-scale assessment of pansharpening: why literature indexes may give contradictory results and how to avoid such an inconvenience

Alparone,

Garzelli,

Lolli

et al. 2023

Image and Signal Processing for Remote Sensing XXIX

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In this paper, we wish to explain the contradiction of quality assessments of pansharpening carried out at full and reduced spatial scales. It seems that at full scale, methods based on Component Substitution (CS) are quantitatively poorer than the other methods, but this depends on the intrinsic space varying misregistration between the two datasets. At reduced scale, the local shifts are divided by the MS-to-Pan scale ratio and thus they tend to vanish. The problem of full-scale quality indexes is that they were originally validated on aerial Multispectral (MS) data, with synthetic panchromatic (Pan) and thus total absence of misregistration. In the presence of local misregistration due to inaccurate information of the height of the imaged surface, CS methods locally align the lowpass MS components towards the sharpening Pan, thereby preserving the geometry of the scene; all the other methods produce fading contours because of shifts. The favorable property of CS, however, impacts against the (spectral) consistency property of Wald’s protocol, developed when the misalignments between MS and Pan was a small fraction of the pixel size, and hence negligible. In this perspective, methods that do not shift the original MS information are better, even though the visual quality of fading contours is worse. After exposing and explaining the contradiction between full- and reduced-scale assessments, we perform an in-depth analysis of the spectral and spatial consistency indexes of three widespread full-scale protocols: QNR, KQNR and HQNR. We investigate the robustness to shifts of all consistency indexes and propose to couple the spectral index and the spatial index that are least sensitive to shifts. In this way, the ranking of methods of reduced-scale assessments is preserved in full-scale assessments.

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Fusion of optical and SAR satellite data for environmental monitoring: assessment of damages and disturbances originated by forest fires

Cited by 3 publications

References 60 publications

Spatial Resolution Enhancement of Satellite Hyperspectral Data via Nested Hypersharpening With Sentinel-2 Multispectral Data

Spatial Resolution Enhancement of Satellite Hyperspectral Data via Nested Hypersharpening With Sentinel-2 Multispectral Data

Increasing the Detection Accuracy of Bi-Temporal Changes via Speckle Whitening of Single-Look Complex Synthetic Aperture Radar Images

Full-scale assessment of pansharpening: why literature indexes may give contradictory results and how to avoid such an inconvenience

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