In machine learning based single image super-resolution, the degradation model is embedded in training data generation. However, most existing satellite image super-resolution methods use a simple down-sampling model with a fixed kernel to create training images. These methods work fine on synthetic data, but do not perform well on real satellite images. We propose a realistic training data generation model for commercial satellite imagery products, which includes not only the imaging process on satellites but also the post-process on the ground. We also propose a convolutional neural network optimized for satellite images. Experiments show that the proposed training data generation model is able to improve super-resolution performance on real satellite images.
Queen Mary University of London（伦敦玛丽女王大学）；Vision Semantics Limited（视觉语义有限公司）
Existing facial image super-resolution (SR) methods focus mostly on improving artificially down-sampled low-resolution (LR) imagery. Such SR models, although strong at handling artificial LR images, often suffer from significant performance drop on genuine LR test data. Previous unsupervised domain adaptation (UDA) methods address this issue by training a model using unpaired genuine LR and HR data as well as cycle consistency loss formulation. However, this renders the model overstretched with two tasks: consistifying the visual characteristics and enhancing the image resolution. Importantly,…
Swinburne University of Technology（斯威本科技大学）；Planet Labs Inc.（行星实验室公司）
Modern deep Super-Resolution (SR) networks have established themselves as valuable techniques in image reconstruction and enhancement. However, these networks are normally trained and tested on benchmark image data that lacks the typical image degrading noise present in real images. In this paper, we test the feasibility of using deep SR in real remote sensing payloads by assessing SR performance in reconstructing realistically degraded satellite images. We demonstrate that a state-of-the-art SR technique called Enhanced Deep Super-Resolution Network (EDSR), without domain specific pre-trainin…
The Ohio State University（俄亥俄州立大学）；Byrd Polar and Climate Research Center（伯德极地与气候研究中心）
Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and …
Recent advances have shown how deep neural networks can be extremely effective at super-resolving remote sensing imagery, starting from a multitemporal collection of low-resolution images. However, existing models have neglected the issue of temporal permutation, whereby the temporal ordering of the input images does not carry any relevant information for the super-resolution task and causes such models to be inefficient with the, often scarce, ground truth data that available for training. Thus, models ought not to learn feature extractors that rely on temporal ordering. In this paper, we sho…
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）
Building structures detection and information about these buildings in aerial images is an important solution for city planning and management, land use analysis. It can be the center piece to answer important questions such as planning evacuation routes in case of an earthquake, flood management, etc. These applications rely on being able to accurately retrieve up-to-date information. Being able to accurately detect buildings in a bounding box centered on a specific latitude-longitude value can help greatly. The key challenge is to be able to detect buildings which can be commercial, industri…
Vision Systems Inc.（视觉系统公司）；ExoAnalytic Solutions（ExoAnalytic解决方案公司）；Texas A&M（德州农工大学）
Automated 3D pose estimation of satellites and other known space objects is a critical component of space situational awareness. Ground-based imagery offers a convenient data source for satellite characterization; however, analysis algorithms must contend with atmospheric distortion, variable lighting, and unknown reflectance properties. Traditional feature-based pose estimation approaches are unable to discover an accurate correlation between a known 3D model and imagery given this challenging image environment. This paper presents an innovative method for automated 3D pose estimation of know…
Video and Image Processing Laboratory（视频与图像处理实验室）；School of Electrical Engineering（电气工程学院）；Purdue University（普渡大学）
Satellite imagery is becoming increasingly accessible due to the growing number of orbiting commercial satellites. Many applications make use of such images: agricultural management, meteorological prediction, damage assessment from natural disasters, or cartography are some of the examples. Unfortunately, these images can be easily tampered and modified with image manipulation tools damaging downstream applications. Because the nature of the manipulation applied to the image is typically unknown, unsupervised methods that don't require prior knowledge of the tampering techniques used are pref…
University College London（伦敦大学学院）；University of Oxford（牛津大学）；Universidad EAFIT（EAFIT大学）；Universidad Icesi（伊塞西大学）
Knowledge of the spatial organisation of economic activity within a city is key to policy concerns. However, in developing cities with high levels of informality, this information is often unavailable. Recent progress in machine learning together with the availability of street imagery offers an affordable and easily automated solution. Here we propose an algorithm that can detect what we call 'visible firms' using street view imagery. Using Medellín, Colombia as a case study, we illustrate how this approach can be used to uncover previously unseen economic activity. Applying spatial analysis …
Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image. This thesis explo…
In this work, we compare the detection accuracy and speed of several state-of-the-art models for the task of detecting oil and gas fracking wells and small cars in commercial electro-optical satellite imagery. Several models are studied from the single-stage, two-stage, and multi-stage object detection families of techniques. For the detection of fracking well pads (50m - 250m), we find single-stage detectors provide superior prediction speed while also matching detection performance of their two and multi-stage counterparts. However, for detecting small cars, two-stage and multi-stage models …
In the emerging commercial space industry there is a drastic increase in access to low cost satellite imagery. The price for satellite images depends on the sensor quality and revisit rate. This work proposes to bridge the gap between image quality and the price by improving the image quality via super-resolution (SR). Recently, a number of deep SR techniques have been proposed to enhance satellite images. However, none of these methods utilize the region-level context information, giving equal importance to each region in the image. This, along with the fact that most state-of-the-art SR meth…
We explore the application of super-resolution techniques to satellite imagery, and the effects of these techniques on object detection algorithm performance. Specifically, we enhance satellite imagery beyond its native resolution, and test if we can identify various types of vehicles, planes, and boats with greater accuracy than native resolution. Using the Very Deep Super-Resolution (VDSR) framework and a custom Random Forest Super-Resolution (RFSR) framework we generate enhancement levels of 2x, 4x, and 8x over five distinct resolutions ranging from 30 cm to 4.8 meters. Using both native an…
Adaptive federated learning for ship detection across diverse satellite imagery sources
Luxembourg Institute of Science and Technology (LIST)（卢森堡科学技术研究所（LIST））；Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA)（计算机科学与随机系统研究所（IRISA））；Université Bretagne Sud (UBS)（南布列塔尼大学（UBS））
University of Southern California（南加州大学）；Institute for Creative Technologies（创意技术研究所）；Army Futures Command（陆军未来司令部）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；US Army’s Synthetic Training Environment（美国陆军合成训练环境）
In the globalized economic world, it has become important to understand the purpose behind infrastructural and construction initiatives occurring within developing regions of the earth. This is critical when the financing for such projects must be coming from external sources, as is occurring throughout major portions of the African continent. When it comes to imagery analysis to research these regions, ground and aerial coverage is either non-existent or not commonly acquired. However, imagery from a large number of commercial, private, and government satellites have produced enormous dataset…
Yield estimation is a powerful tool in vineyard management, as it allows growers to fine-tune practices to optimize yield and quality. However, yield estimation is currently performed using manual sampling, which is time-consuming and imprecise. This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards. Continuous data collection using a vehicle-mounted sensing kit combined with collection of ground truth yield data at harvest using a commercial yield monitor allowed for the generation of a large dataset of 23,581 yield points and…
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）
Singapore-ETH Center（新加坡-ETH中心）；ETH Zuerich（苏黎世联邦理工学院）；University of Melbourne（墨尔本大学）
Recently developed automatic dense image matching algorithms are now being implemented for DSM/DTM production, with their pixel-level surface generation capability offering the prospect of partially alleviating the need for manual and semi-automatic stereoscopic measurements. In this paper, five commercial/public software packages for 3D surface generation are evaluated, using 5cm GSD imagery recorded from a UAV. Generated surface models are assessed against point clouds generated from mobile LiDAR and manual stereoscopic measurements. The software packages considered are APS, MICMAC, SURE, Pi…
Camera-based electronic monitoring (EM) systems are increasingly being deployed onboard commercial fishing vessels to collect essential data for fisheries management and regulation. These systems generate large quantities of video data which must be reviewed on land by human experts. Computer vision can assist this process by automatically detecting and classifying fish species, however the lack of existing public data in this domain has hindered progress. To address this, we present the Fishnet Open Images Database, a large dataset of EM imagery for fish detection and fine-grained categorizat…
Southern Medical University（南方医科大学）；Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology（广州市医学辐射成像与检测技术重点实验室）；South China University of Technology（华南理工大学）
Energy-resolving computed tomography (ErCT) has the ability to acquire energy-dependent measurements simultaneously and quantitative material information with improved contrast-to-noise ratio. Meanwhile, ErCT imaging system is usually equipped with an advanced photon counting detector, which is expensive and technically complex. Therefore, clinical ErCT scanners are not yet commercially available, and they are in various stage of completion. This makes the researchers less accessible to the ErCT images. In this work, we investigate to produce ErCT images directly from existing energy-integrati…
False Claims, Credible Images: A Red-Teaming Benchmark for Commercial Image Generators
Xiangtan University（湘潭大学）；Jinan University（暨南大学）；Huazhong University of Science and Technology（华中科技大学）；City University of Hong Kong（香港城市大学）；Changsha University of Science and Technology（长沙理工大学）；Zhejiang University（浙江大学）；Chongqing University（重庆大学）
HairLRM: Strand-based Hair Modeling via Large Reconstruction Models
LIGHTSPEED Shenzhen China（LIGHTSPEED深圳中国）；State Key Lab of CAD and CG, Zhejiang University Hangzhou China（计算机辅助设计与图形学国家重点实验室，浙江大学杭州中国）；Hong Kong Baptist University Hong Kong China（香港 Baptist大学香港中国）；LIGHTSPEED Los Angeles CA USA (2026)（LIGHTSPEED洛杉矶CA美国（2026））
With the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process with manual annotations, (2) most datasets are only designed for a single specific task, (3) the modification or randomization of the 3D scene is difficult, and (4) the release of commercial 3D data may encounter copyright issue. This paper presents MINERVAS, a Massive INterior Envi…
In this paper, an autonomous method of satellite detection and tracking in images is implemented using optical flow. Optical flow is used to estimate the image velocities of detected objects in a series of space images. Given that most objects in an image will be stars, the overall image velocity from star motion is used to estimate the image's frame-to-frame motion. Objects seen to be moving with velocity profiles distinct from the overall image velocity are then classified as potential resident space objects. The detection algorithm is exercised using both simulated star images and ground-ba…
Pennsylvania State University（宾夕法尼亚州立大学）；U. S. Naval Research Laboratory（美国海军研究实验室）
Finding mines in Sonar imagery is a significant problem with a great deal of relevance for seafaring military and commercial endeavors. Unfortunately, the lack of enormous Sonar image data sets has prevented automatic target recognition (ATR) algorithms from some of the same advances seen in other computer vision fields. Namely, the boom in convolutional neural nets (CNNs) which have been able to achieve incredible results - even surpassing human actors - has not been an easily feasible route for many practitioners of Sonar ATR. We demonstrate the power of one avenue to incorporating CNNs into…
So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection
University of Liverpool, UK（利物浦大学）；Nanyang Technological University（南洋理工大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The University of Sheffield（谢菲尔德大学）
