Preliminary Investigation Into the Geometric Calibration of the Micasense Rededge-M Multispectral Camera

Frontera, F.; Smith, Martin J.; Marsh, Stuart

doi:10.5194/isprs-archives-xliii-b2-2020-17-2020

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The need for geometric calibration of the RedEdge-M camera has been recognized before [55], and specialized SfM software such as MetaShapePro provides means to carry out this calibration. Although the resulting orthomosaics using this camera lens calibration tool offer better spatial structure accuracy, the difference in the lenses position for each band detector is an unresolved issue, especially for this short-distance approach.…”

Section: Spatial Coherence Of Multispectral Orthomosaicsmentioning

confidence: 99%

Coral Reef Benthos Classification Using Data from a Short-Range Multispectral Sensor

Garza-Pérez

Barrón-Coronel

2022

Remote Sensing

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A five-band short-range multispectral sensor (MicaSense RedEdge-M) was adapted to an underwater housing and used to obtain data from coral reef benthos. Artificial illumination was required to obtain data from most of the spectral range of the sensor; the optimal distance for obtaining these data was 0.5 m, from the sensor to the bottom. Multispectral orthomosaics were developed using structure-from-motion software; these have the advantage of producing ultra-high spatial resolution (down to 0.4 × 0.4 mm/pixel) images over larger areas. Pixel-based supervised classification of a multispectral (R, G, B, RE bands) orthomosaic accurately discriminated among different benthic components; classification schemes defined 9 to 14 different benthic components such as brown algae, green algae, sponges, crustose coralline algae, and different coral species with high accuracy (up to 84% overall accuracy, and 0.83 for Kappa and Tau coefficients). The least useful band acquired by the camera for this underwater application was the near-infrared (820–860 nm) associated with its rapid absorption in the water column. Further testing is required to explore possible applications of these multispectral orthomosaics, including the assessment of the health of coral colonies, as well as the automation of their processing.

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Section: Spatial Coherence Of Multispectral Orthomosaicsmentioning

confidence: 99%

Coral Reef Benthos Classification Using Data from a Short-Range Multispectral Sensor

Garza-Pérez

Barrón-Coronel

2022

Remote Sensing

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“…The measurement of these distortion components is important as the presence of these distortions has an impact on the image texture. If the distortion is not removed, the corners and margins of the image would present a narrower field of view when compared to an undistorted image [21].…”

Section: Metadata Extractionmentioning

confidence: 99%

How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained

Vong

Matos-Carvalho

Toffanin³

et al. 2021

Remote Sensing

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The increased development of camera resolution, processing power, and aerial platforms helped to create more cost-efficient approaches to capture and generate point clouds to assist in scientific fields. The continuous development of methods to produce three-dimensional models based on two-dimensional images such as Structure from Motion (SfM) and Multi-View Stereopsis (MVS) allowed to improve the resolution of the produced models by a significant amount. By taking inspiration from the free and accessible workflow made available by OpenDroneMap, a detailed analysis of the processes is displayed in this paper. As of the writing of this paper, no literature was found that described in detail the necessary steps and processes that would allow the creation of digital models in two or three dimensions based on aerial images. With this, and based on the workflow of OpenDroneMap, a detailed study was performed. The digital model reconstruction process takes the initial aerial images obtained from the field survey and passes them through a series of stages. From each stage, a product is acquired and used for the following stage, for example, at the end of the initial stage a sparse reconstruction is produced, obtained by extracting features of the images and matching them, which is used in the following step, to increase its resolution. Additionally, from the analysis of the workflow, adaptations were made to the standard workflow in order to increase the compatibility of the developed system to different types of image sets. Particularly, adaptations focused on thermal imagery were made. Due to the low presence of strong features and therefore difficulty to match features across thermal images, a modification was implemented, so thermal models could be produced alongside the already implemented processes for multispectral and RGB image sets.

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Preliminary Investigation Into the Geometric Calibration of the Micasense Rededge-M Multispectral Camera

Cited by 2 publications

References 26 publications

Coral Reef Benthos Classification Using Data from a Short-Range Multispectral Sensor

Coral Reef Benthos Classification Using Data from a Short-Range Multispectral Sensor

How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained

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