Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach
| العنوان: | Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach |
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| المؤلفون: | Roberto Arnaldo Trancoso Gomes, Anesmar Olino de Albuquerque, Cristiano Rosa Silva, Pablo Pozzobon de Bem, Díbio Leandro Borges, Osmar Luiz Ferreira de Carvalho, Pedro Henrique Guimarães Ferreira, Renato Fontes Guimarães, Rebeca dos Santos de Moura, Osmar Abílio de Carvalho Júnior |
| المصدر: | Remote Sensing, Vol 13, Iss 39, p 39 (2021) Remote Sensing; Volume 13; Issue 1; Pages: 39 |
| بيانات النشر: | MDPI AG, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Source code, 010504 meteorology & atmospheric sciences, Computer science, media_common.quotation_subject, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, 01 natural sciences, COCO, Landsat-8, mask R-CNN, Segmentation, Pyramid (image processing), lcsh:Science, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, media_common, Ground truth, deep learning, Object detection, Feature (computer vision), instance segmentation, multi-channel imagery, General Earth and Planetary Sciences, RGB color model, lcsh:Q, center pivot |
| الوصف: | Instance segmentation is the state-of-the-art in object detection, and there are numerous applications in remote sensing data where these algorithms can produce significant results. Nevertheless, one of the main problems is that most algorithms use Red, Green, and Blue (RGB) images, whereas Satellite images often present more channels that can be crucial to improve performance. Therefore, the present work brings three contributions: (a) conversion system from ground truth polygon data into the Creating Common Object in Context (COCO) annotation format; (b) Detectron2 software source code adaptation and application on multi-channel imagery; and (c) large scene image mosaicking. We applied the procedure in a Center Pivot Irrigation System (CPIS) dataset with ground truth produced by the Brazilian National Water Agency (ANA) and Landsat-8 Operational Land Imager (OLI) imagery (7 channels with 30-m resolution). Center pivots are a modern irrigation system technique with massive growth potential in Brazil and other world areas. The round shapes with different textures, colors, and spectral behaviors make it appropriate to use Deep Learning instance segmentation. We trained the model using 512 × 512-pixel sized patches using seven different backbone structures (ResNet50- Feature Pyramid Network (FPN), Resnet50-DC5, ResNet50-C4, Resnet101-FPN, Resnet101-DC5, ResNet101-FPN, and ResNeXt101-FPN). The model evaluation used standard COCO metrics (Average Precision (AP), AP50, AP75, APsmall, APmedium, and AR100). ResNeXt101-FPN had the best results, with a 3% advantage over the second-best model (ResNet101-FPN). We also compared the ResNeXt101-FPN model in the seven-channel and RGB imagery, where the multi-channel model had a 3% advantage, demonstrating great improvement using a larger number of channels. This research is also the first with a mosaicking algorithm using instance segmentation models, where we tested in a 1536 × 1536-pixel image using a non-max suppression sorted by area method. The proposed methodology is innovative and suitable for many other remote sensing problems and medical imagery that often present more channels. |
| وصف الملف: | application/pdf |
| تدمد: | 2072-4292 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b41992e0169497de784c5a5b1c846d81 https://doi.org/10.3390/rs13010039 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....b41992e0169497de784c5a5b1c846d81 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20724292 |
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