Multi-Image Robust Alignment of Medium-Resolution Satellite Imagery

Scaioni, Marco; Barazzetti, Luigi; Gianinetto, Marco

doi:10.3390/rs10121969

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…In this study, we overlooked this problem and mainly focused on the testing the M2CTool methodology. At any rate, when the objective is of analyzing more images in a time series, although images refer to the same sensor and same spatial resolutions, images co-registration, based on selected Ground Control Points (GCP), should be performed (Gao, Zhang, & Gu, 2017;Scaioni, Barazzetti, & Gianinetto, 2018).…”

Section: Methodsmentioning

confidence: 99%

Testing a Combined Multispectral-Multitemporal Approach for Getting Cloudless Imagery for Sentinel-2

Colaninno

Marambio

Cladera

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Earth observation and land cover monitoring are among major applications for satellite data. However, the use of primary satellite information is often limited by clouds, cloud shadows, and haze, which generally contaminate optical imagery. For purposes of hazard assessment, for instance, such as flooding, drought, or seismic events, the availability of uncontaminated optical data is required. Different approaches exist for masking and replacing cloud/haze related contamination. However, most common algorithms take advantage by employing thermal data. Hence, we tested an algorithm suitable for optical imagery only. The approach combines a multispectral-multitemporal strategy to retrieve daytime cloudless and shadow-free imagery. While the approach has been explored for Landsat information, namely Landsat 5 TM and Landsat 8 OLI, here we aim at testing the suitability of the method for Sentinel-2 Multi-Spectral Instrument. A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over a temporal period of few months. Besides, in order to emphasize the effectiveness of optical imagery for monitoring post-disaster events, two temporal stages have been processed, before and after a critical seismic event occurred in Lombok Island, Indonesia, in summer 2018. The approach relies on a clouds and cloud shadows masking algorithm, based on spectral features, and a data reconstruction phase based on automatic selection of the most suitable pixels from a multitemporal stack. Results have been tested with uncontaminated image samples for the same scene. High accuracy is achieved.

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Section: Methodsmentioning

confidence: 99%

Testing a Combined Multispectral-Multitemporal Approach for Getting Cloudless Imagery for Sentinel-2

Colaninno

Marambio

Cladera

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Co-registration between S2 and PRISMA images is performed in an unsupervised way, by using the wellestablished feature-based technique that exploits the SURF algorithm ( [47], [48]). In this Sub-Section, we briefly 1 Bicubic interpolation is considered here and in the rest of the paper.…”

Section: B Co-registration Of S2 and Prisma Imagesmentioning

confidence: 99%

PRISMA Spatial Resolution Enhancement by Fusion With Sentinel-2 Data

Acito

Diani²,

Corsini

2022

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

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This paper deals with the problem of improving the spatial resolution of hyperspectral data from the PRISMA mission. For this purpose, higher spatial resolution data from the Sentinel-2 (S2) mission are exploited. Particularly, the ten S2 bands at 10 m and 20 m spatial resolution are used to accomplish the PRISMA super-resolution (SR) task. The paper presents a new end-to-end procedure, called PRISMA-SR, that starting from the S2 data and the low resolution PRISMA image, provides a superresolved image with a spatial resolution of 10 m and the same spectral resolution as the PRISMA hyperspectral sensor. The first step of the PRISMA-SR procedure consists in fusing S2 data at different spatial resolutions to obtain a synthetic MS image with 10 m spatial resolution and 10 spectral bands. Then, an unsupervised procedure is applied to co-register the fused S2 image and the PRISMA image. Finally, the two images at different spatial resolutions are properly combined in order to obtain the super-resolved hyperspectral image. Solutions for each step of the PRISMA-SR processing chain are proposed and discussed. Simulated data are used to show the effectiveness of the PRISMA-SR scheme and to investigate the impact on its performance of each step of the processing chain. Real S2 and PRISMA images are finally considered to provide an example of the application of the PRISMA-SR.

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“…Panoramic images are able to provide a wide field-of-view (FOV) of a scene, which is demanded in a number of applications, such as remote sensing [1,2], environment monitoring [3,4], robot localization [5,6], autonomous transportation [7,8], etc. The ways in which panoramas are created can be divided into three categories [9].…”

Section: Introductionmentioning

confidence: 99%

Robust Cylindrical Panorama Stitching for Low-Texture Scenes Based on Image Alignment Using Deep Learning and Iterative Optimization

Lai

Wei

Jiang

et al. 2019

Sensors

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Cylindrical panorama stitching is able to generate high resolution images of a scene with a wide field-of-view (FOV), making it a useful scene representation for applications like environmental sensing and robot localization. Traditional image stitching methods based on hand-crafted features are effective for constructing a cylindrical panorama from a sequence of images in the case when there are sufficient reliable features in the scene. However, these methods are unable to handle low-texture environments where no reliable feature correspondence can be established. This paper proposes a novel two-step image alignment method based on deep learning and iterative optimization to address the above issue. In particular, a light-weight end-to-end trainable convolutional neural network (CNN) architecture called ShiftNet is proposed to estimate the initial shifts between images, which is further optimized in a sub-pixel refinement procedure based on a specified camera motion model. Extensive experiments on a synthetic dataset, rendered photo-realistic images, and real images were carried out to evaluate the performance of our proposed method. Both qualitative and quantitative experimental results demonstrate that cylindrical panorama stitching based on our proposed image alignment method leads to significant improvements over traditional feature based methods and recent deep learning based methods for challenging low-texture environments.

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Multi-Image Robust Alignment of Medium-Resolution Satellite Imagery

Cited by 10 publications

References 42 publications

Testing a Combined Multispectral-Multitemporal Approach for Getting Cloudless Imagery for Sentinel-2

Testing a Combined Multispectral-Multitemporal Approach for Getting Cloudless Imagery for Sentinel-2

PRISMA Spatial Resolution Enhancement by Fusion With Sentinel-2 Data

Robust Cylindrical Panorama Stitching for Low-Texture Scenes Based on Image Alignment Using Deep Learning and Iterative Optimization

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