Learning to See More: UAS-Guided Super-Resolution of Satellite Imagery for Precision Agriculture
Esri New York, NY（Esri纽约分校）；Microsoft Research Redmond, WA（微软研究院雷德蒙德分校）；USDA - Agricultural Research Service University Park, PA（美国农业部农业研究服务局帕克分校）；Dept. of Electrical Engineering and Computer Science Alabama A&M University, AL（电气工程与计算机科学系阿拉巴马-阿马大学）；Dept. of Environmental Science and Technology Univ. of Maryland, College Park, MD（环境科学与技术系马里兰大学学院市分校）
Arif Masrur, Peder A. Olsen, Paul R. Adler, Carlan Jackson, Matthew W. Myers, Nathan Sedghi, Ray R. Weil
无人飞行器系统（UAS）和卫星是精准农业的关键数据源，但各自存在权衡。卫星数据提供广泛的空间、时间和光谱覆盖，但缺乏许多精准农业应用所需的分辨率；而UAS提供高空间细节，但受限于覆盖范围和成本，特别是高光谱数据。本研究提出了一个新框架，利用超分辨率方法融合卫星和UAS图像。通过整合空间、光谱和时间域的数据，我们以经济高效的方式利用了两种平台的优势。我们以覆盖作物生物量和氮（N）估算为案例研究来评估该方法。通过将UAS RGB数据光谱扩展到植被红边和近红外区域，我们生成了高分辨率的Sentinel-2图像，并将生物量和氮估算准确度分别提高了18%和31%。结果表明，UAS数据只需从部分田块和时间点采集。农民随后可以：1）增强UAS RGB图像的光谱细节；2）利用卫星数据提高空间分辨率；3）以卫星飞行频率在整个生长季空间上扩展这些增强效果。我们基于SRCNN的光谱扩展模型在Upper and Lower Chesapeake Bay地区的其他种植系统上展现出显著的模型可迁移性前景。此外，即使无云卫星数据不可用时，仅依赖UAS RGB输入，它仍然有效。空间扩展模型比基于原始UAS RGB图像构建的模型产生更好的生物量和氮预测。一旦用目标UAS RGB数据训练完成，空间扩展模型允许农民停止重复的UAS飞行。虽然我们引入了超分辨率技术进步，但核心贡献是一个轻量且可扩展的系统，适用于经济实惠的农场使用。
英文摘要：
Unmanned Aircraft Systems (UAS) and satellites are key data sources for precision agriculture, yet each presents trade-offs. Satellite data offer broad spatial, temporal, and spectral coverage but lack the resolution needed for many precision farming applications, while UAS provide high spatial detail but are limited by coverage and cost, especially for hyperspectral data. This study presents a novel framework that fuses satellite and UAS imagery using super-resolution methods. By integrating data across spatial, spectral, and temporal domains, we leverage the strengths of both platforms cost-effectively. We use estimation of cover crop biomass and nitrogen (N) as a case study to evaluate our approach. By spectrally extending UAS RGB data to the vegetation red edge and near-infrared regions, we generate high-resolution Sentinel-2 imagery and improve biomass and N estimation accuracy by 18% and 31%, respectively. Our results show that UAS data need only be collected from a subset of fields and time points. Farmers can then 1) enhance the spectral detail of UAS RGB imagery; 2) increase the spatial resolution by using satellite data; and 3) extend these enhancements spatially and across the growing season at the frequency of the satellite flights. Our SRCNN-based spectral extension model shows considerable promise for model transferability over other cropping systems in the Upper and Lower Chesapeake Bay regions. Additionally, it remains effective even when cloud-free satellite data are unavailable, relying solely on the UAS RGB input. The spatial extension model produces better biomass and N predictions than models built on raw UAS RGB images. Once trained with targeted UAS RGB data, the spatial extension model allows farmers to stop repeated UAS flights. While we introduce super-resolution advances, the core contribution is a lightweight and scalable system for affordable on-farm use.
North Dakota State University of Agriculture and Applied Science（北达科他州立大学农业与应用科学学院）
Currently, weed control in a corn field is performed by a blanket application of herbicides that do not consider spatial distribution information of weeds and also uses an extensive amount of chemical herbicides. To reduce the amount of chemicals, we used drone-based high-resolution imagery and computer-vision techniques to perform site-specific weed control in corn.
An U-Net-Based Deep Neural Network for Cloud Shadow and Sun-Glint Correction of Unmanned Aerial System (UAS) Imagery
Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA（工业与系统工程系，密苏里州立大学）；Department of Geosciences, Mississippi State University, Mississippi State, MS, USA（地球科学系，密苏里州立大学）
FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images
Annam.AI CoE, MoE, Indian Institute of Technology Ropar, Punjab, India（Annam.AI联合研究所、教育部、印度理工学院罗帕尔分校）；Department of Mathematical Sciences, Rajiv Gandhi Institute of Petroleum Technology, Jais, UP, India（数学科学系、拉贾吉安石油技术学院）；Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Punjab, India（计算机科学与工程系、印度理工学院罗帕尔分校）
Site-specific weed management in corn using UAS imagery analysis and computer vision techniques
Center for Precision & Automated Agricultural Systems（精准与自动化农业系统中心）；Washington State University（华盛顿州立大学）；Agricultural and Biosystems Engineering（农业与生物系统工程学院）；NDSU Carrington Research Extension Center（北达科他州立大学卡灵顿研究推广中心）；North Dakota State University（北达科他州立大学）
Industrial Systems Institute, ATHENA Research Center（雅典娜研究中心工业系统研究所）
Delineation approaches provide significant benefits to various domains, including agriculture, environmental and natural disasters monitoring. Most of the work in the literature utilize traditional segmentation methods that require a large amount of computational and storage resources. Deep learning has transformed computer vision and dramatically improved machine translation, though it requires massive dataset for training and significant resources for inference. More importantly, energy-efficient embedded vision hardware delivering real-time and robust performance is crucial in the aforement…
This article describes a software package called ROS georegistration intended for use with the Robot Operating System (ROS) and the Gazebo 3D simulation environment. ROSgeoregistration provides tools for the simulation, test and deployment of aerial georegistration algorithms and is made available with a link provided in the paper. A model creation package is provided which downloads multi-spectral images from the Google Earth Engine database and, if necessary, incorporates these images into a single, possibly very large, reference image. Additionally a Gazebo plugin which uses the real-time s…
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
Indraprastha Institute of Information Technology, Delhi（德里英迪拉普拉斯塔信息技术学院）；M S Swaminathan Research Foundation（M·S·斯瓦米纳坦研究基金会）
The integration of the modern Machine Learning (ML) models into remote sensing and agriculture has expanded the scope of the application of satellite images in the agriculture domain. In this paper, we present how the accuracy of crop type identification improves as we move from medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution (HSTR) satellite images. We further demonstrate that high spectral resolution in satellite imagery can improve prediction performance for low spatial and temporal resolutions (LSTR) images. The F1-score is increased by 7% when using multispectral…
Politecnico di Torino（都灵理工大学）；Politecnico Interdepartmental Centre for Service Robotics（都灵理工大学跨部门服务机器人中心）；Università degli Studi di Torino（都灵大学）；Institute of Electronics, Computer and Telecommunication Engineering of the National Research Council of Italy（意大利国家研究委员会电子、计算机与电信工程研究所）
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are gr…
Single Image Super Resolution (SISR) is the process of mapping a low-resolution image to a high resolution image. This inherently has applications in remote sensing as a way to increase the spatial resolution in satellite imagery. This suggests a possible improvement to automated target recognition in image classification and object detection. We explore the effect that different training sets have on SISR with the network, Super Resolution Generative Adversarial Network (SRGAN). We train 5 SRGANs on different land-use classes (e.g. agriculture, cities, ports) and test them on the same unseen …
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classifying land use change without high resolution imagery offers the potential to monitor vital aspects of global development agenda including climate smart agriculture, drought resistant crops, and sustainable land management. Utilizing a combination of capsule layers and long-short…
Object detection models commonly deployed on uncrewed aerial systems (UAS) focus on identifying objects in the visible spectrum using Red-Green-Blue (RGB) imagery. However, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) images to increase the performance of object detection machine learning (ML) models. Currently LWIR ML models have received less research attention, especially for both ground- and air-based platforms, leading to a lack of baseline performance metrics evaluating LWIR, RGB and LWIR-RGB fused object detection models. Therefore, this research contri…
Early, precise detection of nutrient deficiency stress (NDS) has key economic as well as environmental impact; precision application of chemicals in place of blanket application reduces operational costs for the growers while reducing the amount of chemicals which may enter the environment unnecessarily. Furthermore, earlier treatment reduces the amount of loss and therefore boosts crop production during a given season. With this in mind, we collect sequences of high-resolution aerial imagery and construct semantic segmentation models to detect and predict NDS across the field. Our work sits a…
Research Center For Modeling and Simulation (RCMS), NUST（国立科技大学建模与仿真研究中心）；RCMS, NUST（国立科技大学建模与仿真研究中心）
Object detection is one of the fundamental objectives in Applied Computer Vision. In some of the applications, object detection becomes very challenging such as in the case of satellite image processing. Satellite image processing has remained the focus of researchers in domains of Precision Agriculture, Climate Change, Disaster Management, etc. Therefore, object detection in satellite imagery is one of the most researched problems in this domain. This paper focuses on aircraft detection. in satellite imagery using deep learning techniques. In this paper, we used YOLO deep learning framework f…
Indraprastha Institute of Information Technology, Delhi（德里英德拉普拉斯塔信息技术学院）；M S Swaminathan Research Foundation（M S 斯瓦米纳坦研究基金会）
The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite the increased access to earth observation data for agriculture, there is a scarcity of curated, labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset, SICKLE, having time-series images at different spatial resolutions from 3 different satellites, annotated with multiple key cropping parameters for paddy cultivation for the Cauvery Delta region in Tamil Nadu, India. The dat…
University of Pittsburgh（匹兹堡大学）；Intelinair Inc.（Intelinair公司）；University of Illinois at Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）
Advances in remote sensing technology have led to the capture of massive amounts of data. Increased image resolution, more frequent revisit times, and additional spectral channels have created an explosion in the amount of data that is available to provide analyses and intelligence across domains, including agriculture. However, the processing of this data comes with a cost in terms of computation time and money, both of which must be considered when the goal of an algorithm is to provide real-time intelligence to improve efficiencies. Specifically, we seek to identify nutrient deficient areas…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Intelinair
A key challenge for much of the machine learning work on remote sensing and earth observation data is the difficulty in acquiring large amounts of accurately labeled data. This is particularly true for semantic segmentation tasks, which are much less common in the remote sensing domain because of the incredible difficulty in collecting precise, accurate, pixel-level annotations at scale. Recent efforts have addressed these challenges both through the creation of supervised datasets as well as the application of self-supervised methods. We continue these efforts on both fronts. First, we genera…
Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation
University of Modena and Reggio Emilia（摩德纳和雷吉奥艾米利亚大学）；ABACO Group SpA（ABACO集团）；Institute of Informatics and Telematics, National Research Council（信息与电信研究所，国家研究 council）
University of Minnesota（明尼苏达大学）；University of Pittsburgh（匹兹堡大学）
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized computer vision applications can accelerate development of novel crop mapping techniques. Currently, the United States Department of Agriculture (USDA) annually releases the Cropland Data Layer (CDL) which contains crop labels at 30m resolution for the entire United States of America. While CDL is state of the art and is widely used for a number of agricultural applications, it has a number of limitations (e.g., pixelated…
Automating grapevine LAI features estimation with UAV imagery and machine learning
TeCIP Institute（TeCIP研究所）；Biorobotics Institute（生物机器人研究所）；Department of Agriculture Food and Environment（农业食品与环境系）；Università di Pisa（比萨大学）；Institute of Crop Science（作物科学研究所）
Satellite imagery is widely used in many application sectors, including agriculture, navigation, and urban planning. Frequently, satellite imagery involves both large numbers of images as well as high pixel counts, making satellite datasets computationally expensive to analyze. Recent approaches to satellite image analysis have largely emphasized deep learning methods. Though extremely powerful, deep learning has some drawbacks, including the requirement of specialized computing hardware and a high reliance on training data. When dealing with large satellite datasets, the cost of both computat…
Unmanned aerial vehicles (UAV) are used in precision agriculture (PA) to enable aerial monitoring of farmlands. Intelligent methods are required to pinpoint weed infestations and make optimal choice of pesticide. UAV can fly a multispectral camera and collect data. However, the classification of multispectral images using supervised machine learning algorithms such as convolutional neural networks (CNN) requires large amount of training data. This is a common drawback in deep learning we try to circumvent making use of a semi-supervised generative adversarial networks (GAN), providing a pixel-…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Microsoft（微软）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satell…
Evaluation of UAV-Based RGB and Multispectral Vegetation Indices for Precision Agriculture in Palm Tree Cultivation
College of Engineering and IT, University of Dubai（迪拜大学工程与信息技术学院）；Department of Electronics and Communication, TKM College of Engineering（TKM工程学院电子与通信系）；Department of Electronics and Communication, MES College of Engineering（MES工程学院电子与通信系）；Applications Development and Analysis Section, Mohammed Bin Rashid Space Centre (MBRSC)（穆罕默德·本·拉希德航天中心应用开发与分析部门）
Linux Polska（波兰Linux公司）；University of Warsaw（华沙大学）；Agency for Restructuring and Modernisation of Agriculture（农业结构调整与现代化署）
Monitoring of land cover and land use is crucial in natural resources management. Automatic visual mapping can carry enormous economic value for agriculture, forestry, or public administration. Satellite or aerial images combined with computer vision and deep learning enable precise assessment and can significantly speed up change detection. Aerial imagery usually provides images with much higher pixel resolution than satellite data allowing more detailed mapping. However, there is still a lack of aerial datasets made for the segmentation, covering rural areas with a resolution of tens centime…
University of Washington（华盛顿大学）；Alpen-Adria-Universität Klagenfurt（克拉根福阿尔卑斯-亚德里亚大学）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；ETH Zürich（苏黎世联邦理工学院）
Recent applications of unmanned aerial systems (UAS) to precision agriculture have shown increased ease and efficiency in data collection at precise remote locations. However, further enhancement of the field requires operation over long periods of time, e.g. days or weeks. This has so far been impractical due to the limited flight times of such platforms and the requirement of humans in the loop for operation. To overcome these limitations, we propose a fully autonomous rotorcraft UAS that is capable of performing repeated flights for long-term observation missions without any human intervent…
AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints
Hunan University of Science and Technology（湖南科技大学）
DAS-SK: An Adaptive Model Integrating Dual Atrous Separable and Selective Kernel CNN for Agriculture Semantic Segmentation
Electrical & Computer Engineering Department of Lakehead University（拉夫莱德大学电气与计算机工程系）；Faculty of Software Engineering of Lakehead University（拉夫莱德大学软件工程系）；Faculty of Applied Science of the University of Alabama（阿拉巴马大学应用科学系）；School of Computer Science Engineering and Information Systems, Vellore Institute of Technology（维洛雷理工学院计算机科学工程与信息系统学院）
Evaluating the Efficacy of Sentinel-2 versus Aerial Imagery in Serrated Tussock Classification
School of Information Technology, Deakin University, Australia（信息技术学院，德堪大学，澳大利亚）；Agriculture Victoria Research, Department of Energy, Environment and Climate Action, Australia（维多利亚农业研究，能源、环境与气候变化行动部，澳大利亚）；Institute of Innovation, Science and Sustainability, Federation University, Australia（创新、科学与可持续性研究所，联邦大学，澳大利亚）
FUELVISION: A Multimodal Data Fusion and Multimodel Ensemble Algorithm for Wildfire Fuels Mapping
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Desert Research Institute（沙漠研究所）；Amazon（亚马逊公司）；United States Forest Service（美国林务局）
本文提出 FUELVISION 算法，融合多源遥感与地形数据，结合生成式 AI 合成数据增强训练集，采用多模型集成方法实现近实时野火可燃物制图，准确率近 80%。
An Efficient Additive Kolmogorov-Arnold Transformer for Point-Level Maize Localization in Unmanned Aerial Vehicle Imagery
Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA（生物系统工程系，威斯康星大学麦迪逊分校）；Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA（农学系，威斯康星大学麦迪逊分校）
