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紧凑型混合线阵光谱相机的半自动光谱图像拼接及其在马铃薯作物叶片近场遥测中的应用

Semi-Automatic Spectral Image Stitching for a Compact Hybrid Linescan Hyperspectral Camera towards Near Field Remote Monitoring of Potato Crop Leaves.

机构信息

Laboratoire d'Informatique, Signal et Image de la Côte d'Opale (LISIC, UR 4491), Université du Littoral Côte d'Opale (ULCO), F-62228 Calais, France.

出版信息

Sensors (Basel). 2021 Nov 16;21(22):7616. doi: 10.3390/s21227616.

DOI:10.3390/s21227616
PMID:34833696
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8619573/
Abstract

The miniaturization of hyperspectral cameras has opened a new path to capture spectral information. One such camera, called the hybrid linescan camera, requires accurate control of its movement. Contrary to classical linescan cameras, where one line is available for every band in one shot, the latter asks for multiple shots to fill a line with multiple bands. Unfortunately, the reconstruction is corrupted by a parallax effect, which affects each band differently. In this article, we propose a two-step procedure, which first reconstructs an approximate datacube in two different ways, and second, performs a corrective warping on each band based on a multiple homography framework. The second step combines different stitching methods to perform this reconstruction. A complete synthetic and experimental comparison is performed by using geometric indicators of reference points. It appears throughout the course of our experimentation that misalignment is significantly reduced but remains non-negligible at the potato leaf scale.

摘要

高光谱相机的小型化为获取光谱信息开辟了新的途径。其中一种相机称为混合线扫描相机,需要精确控制其运动。与每次拍摄中每个波段只有一行的传统线扫描相机不同,后者需要多次拍摄才能用多行填充一个波段。不幸的是,重建过程会受到视差效应的影响,而这种影响会对每个波段产生不同的影响。在本文中,我们提出了一个两步的过程,首先以两种不同的方式重建一个近似的立方体,然后根据多个单应性框架对每个波段进行纠正变形。第二步结合了不同的拼接方法来执行这种重建。通过使用参考点的几何指标,进行了完整的合成和实验比较。在我们的实验过程中,明显减少了错位,但在马铃薯叶片尺度上仍然不可忽视。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/03e51c5e84d3/sensors-21-07616-g024.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/c66de34e5799/sensors-21-07616-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/85ce6789a211/sensors-21-07616-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/8d25355ccf22/sensors-21-07616-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/7b851e8aa39c/sensors-21-07616-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/cb4d7ae7b856/sensors-21-07616-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/6b700a1e63cc/sensors-21-07616-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/b4c6cb5e29cd/sensors-21-07616-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/42ebb8a7ac51/sensors-21-07616-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/856c562eb03b/sensors-21-07616-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/e66e7b412584/sensors-21-07616-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/16bd8709c639/sensors-21-07616-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/4b45467a2f24/sensors-21-07616-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/d20971d3cf5e/sensors-21-07616-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/94335eae03c3/sensors-21-07616-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b4e/8619573/03e51c5e84d3/sensors-21-07616-g024.jpg

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