Two-step ResUp&amp;Down generative adversarial network to reconstruct multispectral image from aerial RGB image

Zhang, Yanchao; Wen, Yang; Zhang, Wenbo; Yu, Jiya; Zhang, Jianxin; Yang, Yongjie; Lu, Yongliang; Tang, Wei

doi:10.1016/j.compag.2021.106617

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…La utilización de redes neuronales convolucional ha traído consigo avances en la investigación de reconstrucción de imágenes multiespectrales [13]. Centrado en gran medida en la reconstrucción de MSI utilizando los canales R-G-B del MSI como entradas del modelo para la posterior de recopilación de información y análisis [14].…”

Section: Introductionunclassified

Optimización de la evaluación de pastos mediante la implementación de imágenes multiespectrales y vehículo aéreo no tripulado

Pelaez Carrillo,

Gualdron Guerrero,

Torres Chavez

2024

RCTA

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Este artículo desarrolla una forma de optimizar la evaluación de pastos utilizando vehículos aéreos no tripulados (UAV) y el análisis de imágenes multiespectrales. La investigación se llevó a cabo en el Municipio de Pamplona, Colombia, con el objetivo de comprender y documentar el crecimiento y evolución del pasto en áreas agrícolas. La metodología utilizada en la investigación incluyó la zonificación del terreno para identificar condiciones favorables para el estudio, de tal manera que se pudiera garantizar que existieran áreas adecuadas para observar el desarrollo del pasto y facilitar el acceso a los instrumentos experimentales. Se destacan aspectos importantes como la obtención de puntos GPS en tierra con el fin de crear polígonos que se disponen como el área de estudio y que permitirán la planificación de vuelos de vehículos aéreos no tripulados (UAV), que dan paso al uso de software de gestión de vuelos autónomos. La adquisición de imágenes multiespectrales se da gracias al uso de cámaras multiespectral incorporadas en el UAV, capaces de registrar información en múltiples bandas espectrales dentro y fuera del espectro visible, como el infrarrojo cercano y el borde de rojo. El análisis estadístico proporcionó una visión detallada de las condiciones agrícolas al revelar correlaciones significativas entre el Índice de Vegetación Normalizado (NDVI) y varios parámetros del suelo como el potasio (K) y el fosforo (P). Este método innovador proporciona datos y visualizaciones precisos que ayudan a tomar decisiones sobre la gestión sostenible de pastos en la región.

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Section: Introductionunclassified

Optimización de la evaluación de pastos mediante la implementación de imágenes multiespectrales y vehículo aéreo no tripulado

Pelaez Carrillo,

Gualdron Guerrero,

Torres Chavez

2024

RCTA

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“…These advantages have made UAV one of the most optimal platforms for local regional scale remote sensing. The rapid developments in both engineering and sensor technology have enabled UAVs to contribute to a variety of uses, such as constructing three-dimensional terrain models of mountainous areas [1], water bodies [2], farming land [3][4][5], forests [6][7][8][9][10], and artificial structures [11], as well as estimating biomass storage of wood or grass resources [12,13],…”

Section: Introductionmentioning

confidence: 99%

Optimization of Ground Control Point Distribution for Unmanned Aerial Vehicle Photogrammetry for Inaccessible Fields

et al. 2022

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Ground control point (GCP) is an important calibration factor when correcting position information during unmanned aerial vehicle (UAV) remote sensing. Studies of the optimal number and distribution shape of GCPs have been conducted worldwide in recent years. However, when conducting surveys at houses, construction sites, farming lands, forests, and some other locations, it is both difficult and destructive to install GCP inside the subject area. In many cases, it is only possible to install GCP at the outer edge around the area. Therefore, this study aims to suggest the optimal GCP distribution pattern, which can provide the highest accuracy, when only the outer edge of a particular area is available. In this research, 88 GCP patterns have been validated and compared at an 18 ha farm. Results show that the patterns with GCPs distributed evenly around the field provided the best calibration (RMSE = 0.15 m). If this kind of pattern is not achievable because of obstructions, patterns with GCPs distributed evenly around half of the field or forming an evenly distributed triangle can provide moderate accuracy (RMSE = 0.18 m and 0.43 m, respectively). Patterns with GCPs forming a straight line yielded the worst accuracy (RMSE = 2.10 m). This shows that GCP distributions of a two-dimensional shape, even if the surrounding area is small, are better calibrated than a long, straight line. These results strongly suggest that appropriate GCP distribution patterns in the study areas will provide satisfactory accuracy for constructing integrated monitoring systems of diverse resources.

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“…A higher value correlates with increased vegetation density. Typically, the values can be categorized into five levels: values less than −0.1 depict areas like ground cloud, water, and snow cover; values between −0.1 and 0.1 indicate areas of rock and soil; values between 0.1 and 0.4 signify sparsely vegetated areas; values between 0.4 and 0.8 denote densely vegetated areas; and values greater than or equal to 0.8 characterize super-densely vegetated areas[58]. By comparing the NDVI values extracted from the recovered images to those from the cloud-free images, we are able to evaluate the methods' capability in preserving the distinctive land cover characteristics within the recovered images.…”

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confidence: 99%

Multimodal and Multiresolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark

Xu,

Shi,

Ebel

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Cloud removal is a significant and challenging problem in remote sensing, and in recent years, there have been notable advancements in this area. However, two major issues remain hindering the development of cloud removal: the unavailability of high-resolution imagery for existing datasets and the absence of evaluation regarding the semantic meaningfulness of the generated structures. In this paper, we introduce M3R-CR, a benchmark dataset for high-resolution Cloud Removal with Multi-Modal and Multi-Resolution data fusion. M3R-CR is the first public dataset for cloud removal to feature globally sampled high-resolution optical observations, paired with radar measurements and pixel-level land cover annotations. With this dataset, we consider the problem of cloud removal in highresolution optical remote sensing imagery by integrating multimodal and multi-resolution information. In this context, we have to take into account the alignment errors caused by the multi-resolution nature, along with the more pronounced misalignment issues in high-resolution images due to inherent imaging mechanism differences and other factors. Existing multimodal data fusion based methods, which assume the image pairs are aligned accurately at pixel-level, are thus not appropriate for this problem. To this end, we design a new baseline named Align-CR to perform the low-resolution SAR image guided highresolution optical image cloud removal. It gradually warps and fuses the features of the multi-modal and multi-resolution data during the reconstruction process, effectively mitigating concerns associated with misalignment. In the experiments, we evaluate the performance of cloud removal by analyzing the quality of visually pleasing textures using image reconstruction metrics and further analyze the generation of semantically meaningful structures using a well-established semantic segmentation task. The proposed Align-CR method is superior to other baseline methods in both areas. The project is available at https://github. com/zhu-xlab/M3R-CR.

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Two-step ResUp&Down generative adversarial network to reconstruct multispectral image from aerial RGB image

Cited by 11 publications

References 14 publications

Optimización de la evaluación de pastos mediante la implementación de imágenes multiespectrales y vehículo aéreo no tripulado

Optimización de la evaluación de pastos mediante la implementación de imágenes multiespectrales y vehículo aéreo no tripulado

Optimization of Ground Control Point Distribution for Unmanned Aerial Vehicle Photogrammetry for Inaccessible Fields

Multimodal and Multiresolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark

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