A New Approach to Satellite Time-series Co-registration for Landslide Monitoring

Barazzetti, Luigi; Gianinetto, Marco; Scaioni, Marco

doi:10.1007/978-3-662-45931-7_12

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Characteristic indices constructed by combining different bands of remote sensing data with topographic landslide features are often used to extract geomorphological precursors [60,61]. In this context, due to its advantages of a long time span [62,63] and the ease of obtaining continuous time-series data [63,64], medium-resolution remote sensing often can provide more spectral information than high-resolution remote sensing data, and the combination of spectral features with algebraic band operations can efficiently distinguish landslide geomorphological precursors and other geomorphic features. Consequently, Landsat, Sentinel-2, and other medium-resolution remote sensing satellites represent important data sources for acquiring time-series data [65][66][67] and continue to play an important role in disaster identification [68,69].…”

Section: Identification Methodsmentioning

confidence: 99%

Remote Sensing Precursors Analysis for Giant Landslides

Lan

Liu

et al. 2022

Remote Sensing

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Monitoring and early warning systems for landslides are urgently needed worldwide to effectively reduce the losses of life and property caused by these natural disasters. Detecting the precursors of giant landslides constitutes the premise of landslide monitoring and early warning, and remote sensing is a powerful means to achieve this goal. In this work, we aim to summarize the basic types and evolutionary principles of giant landslide precursors, describe the remote sensing methods capable of identifying those precursors, and present typical cases of related sliding. Based on a review of the literature and an analysis of remote sensing imagery, the three main types of remote sensing techniques for capturing the geomorphological, geotechnical, and geoenvironmental precursors of giant landslides are optical, synthetic aperture radar (SAR), and thermal infrared methods, respectively. Time-series optical remote sensing data from medium-resolution satellites can be used to obtain abundant information on geomorphological changes, such as the extension of cracks and erosion ditches, and band algebraic analysis, image enhancement, and segmentation techniques are valuable for focusing on the locations of geomorphological landslide precursors. SAR sensors have the ability to monitor the slight slope deformation caused by unfavorable geological structures and can provide precursor information on imminent failure several days before a landslide; furthermore, persistent scatterer interferometric SAR has significant advantages in large-scale surface displacement monitoring. Thermal infrared imagery can identify landslide precursors by monitoring geoenvironmental information, especially in permafrost regions where glaciers are widely distributed; the reason may be that freeze–thaw cycles and snowmelt caused by increased temperatures affect the stability of the surface. Optical, SAR, and thermal remote sensing all exhibit unique advantages and play an essential role in the identification of giant landslide precursors. The combined application of these three remote sensing technologies to obtain the synthetic geomorphological, geotechnical, and geoenvironmental precursors of giant landslides would greatly promote the development of landslide early warning systems.

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Section: Identification Methodsmentioning

confidence: 99%

Remote Sensing Precursors Analysis for Giant Landslides

Lan

Liu

et al. 2022

Remote Sensing

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“…In addition, shorter wavelengths in the visible domain (mainly bands TM1/TM2) are more affected by atmospheric scattering. In a previous paper [44], the robustness of MIRA against atmospheric effects was demonstrated. For this reason, FBM has been applied to the images in all bands without any preprocessing to correct these effects.…”

Section: Dataset and Data Processingmentioning

confidence: 97%

Multi-Image Robust Alignment of Medium-Resolution Satellite Imagery

2018

Self Cite

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This paper describes an automatic multi-image robust alignment (MIRA) procedure able to simultaneously co-register a time series of medium-resolution satellite images in a bundle block adjustment (BBA) fashion. Instead of the direct co-registration of each image with respect to a reference ‘master’ image on the basis of corresponding features, MIRA also considers those tie points that may be not be shared with the master, but they only connect the other images (‘slaves’) among them. In a first stage, tie points are automatically extracted by using pairwise feature-based matching based on the SURF operator. In a second stage, such extracted features are re-ordered to find corresponding tie points visible on multiple image pairs. A ‘master’ image is then selected with the only purpose to establish the datum of the final image alignment and to instantiate the computation of approximate registration parameters. All the available information obtained so far is fed into a least-squares BBA to estimate the unknowns, which include the registration parameters and the coordinates of tie points re-projected in the ‘master’ image space. The analysis of inner and outer reliability of the observations is applied to assess whether the residual blunders may be located using data snooping, and to evaluate the influence of undetected outliers on the final registration results. Three experiments with simulated datasets and one example consisting of eleven Landsat-5/TM images are reported and discussed. In the case of real data, results have been positively checked against the ones obtained by using alternative procedures (BBA with manual measurements and ‘slave-to-master’ registration based on automatically extracted tie points). These experiments have confirmed the correctness of the MIRA approach and have highlighted the potential of the inner control on the final quality of the solution that may come from the reliability analysis.

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Subpixel geocoding of COSMO-SkyMed and Sentinel-1 time series imaged with different geometry

Gianinetto

Monno

Barazzetti

et al. 2016

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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This paper describes the use of a new multi-image co-registration method tuned for SAR multi-temporal data collected with different orbits, viewing angles and polarization. Tests were performed using COSMO-SkyMed (X-band) and Sentinel-1 data (C-band) time series imaged in stripmap and spotlight modes. Results shows an overview sub-pixel accuracy also for the challenging co-registration of dual orbit image stacks (ascending vs. descending), where other methods fail

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A New Approach to Satellite Time-series Co-registration for Landslide Monitoring

Cited by 4 publications

References 40 publications

Remote Sensing Precursors Analysis for Giant Landslides

Remote Sensing Precursors Analysis for Giant Landslides

Multi-Image Robust Alignment of Medium-Resolution Satellite Imagery

Subpixel geocoding of COSMO-SkyMed and Sentinel-1 time series imaged with different geometry

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