Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment
This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approac...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/10/3/61 |
| _version_ | 1797240454302924800 |
|---|---|
| author | Shubham Rana Salvatore Gerbino Mariano Crimaldi Valerio Cirillo Petronia Carillo Fabrizio Sarghini Albino Maggio |
| author_facet | Shubham Rana Salvatore Gerbino Mariano Crimaldi Valerio Cirillo Petronia Carillo Fabrizio Sarghini Albino Maggio |
| author_sort | Shubham Rana |
| collection | DOAJ |
| description | This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing <i>Triticum aestivum</i> crop and <i>Raphanus raphanistrum</i> weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a mAP@0.5 of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances. |
| first_indexed | 2024-04-24T18:07:41Z |
| format | Article |
| id | doaj.art-08ef9faab6ea4f0587bfaaecd19c3347 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-04-24T18:07:41Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-08ef9faab6ea4f0587bfaaecd19c33472024-03-27T13:48:52ZengMDPI AGJournal of Imaging2313-433X2024-02-011036110.3390/jimaging10030061Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture EnvironmentShubham Rana0Salvatore Gerbino1Mariano Crimaldi2Valerio Cirillo3Petronia Carillo4Fabrizio Sarghini5Albino Maggio6Department of Engineering, University of Campania “L. Vanvitelli”, Via Roma 29, 81031 Aversa, ItalyDepartment of Engineering, University of Campania “L. Vanvitelli”, Via Roma 29, 81031 Aversa, ItalyDepartment of Agricultural Sciences, University of Naples “Federico II”, Via Università 100, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples “Federico II”, Via Università 100, 80055 Naples, ItalyDepartment of Biological and Pharmaceutical Environmental Sciences and Technologies, University of Campania “L. Vanvitelli”, Via Antonio Vivaldi, 43, 81100 Caserta, ItalyDepartment of Agricultural Sciences, University of Naples “Federico II”, Via Università 100, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples “Federico II”, Via Università 100, 80055 Naples, ItalyThis article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing <i>Triticum aestivum</i> crop and <i>Raphanus raphanistrum</i> weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a mAP@0.5 of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances.https://www.mdpi.com/2313-433X/10/3/61binary maskhomography matrixmasked pixelsMS (multispectral)SIFT (scale-invariant feature transform) |
| spellingShingle | Shubham Rana Salvatore Gerbino Mariano Crimaldi Valerio Cirillo Petronia Carillo Fabrizio Sarghini Albino Maggio Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment Journal of Imaging binary mask homography matrix masked pixels MS (multispectral) SIFT (scale-invariant feature transform) |
| title | Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment |
| title_full | Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment |
| title_fullStr | Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment |
| title_full_unstemmed | Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment |
| title_short | Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment |
| title_sort | comprehensive evaluation of multispectral image registration strategies in heterogenous agriculture environment |
| topic | binary mask homography matrix masked pixels MS (multispectral) SIFT (scale-invariant feature transform) |
| url | https://www.mdpi.com/2313-433X/10/3/61 |
| work_keys_str_mv | AT shubhamrana comprehensiveevaluationofmultispectralimageregistrationstrategiesinheterogenousagricultureenvironment AT salvatoregerbino comprehensiveevaluationofmultispectralimageregistrationstrategiesinheterogenousagricultureenvironment AT marianocrimaldi comprehensiveevaluationofmultispectralimageregistrationstrategiesinheterogenousagricultureenvironment AT valeriocirillo comprehensiveevaluationofmultispectralimageregistrationstrategiesinheterogenousagricultureenvironment AT petroniacarillo comprehensiveevaluationofmultispectralimageregistrationstrategiesinheterogenousagricultureenvironment AT fabriziosarghini comprehensiveevaluationofmultispectralimageregistrationstrategiesinheterogenousagricultureenvironment AT albinomaggio comprehensiveevaluationofmultispectralimageregistrationstrategiesinheterogenousagricultureenvironment |