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2024 IAF Symposium on Integrated Applications at the 75th International Astronautical Congress, IAC 2024

作者： Song, Jiayun Yao, Lifan Zhang, Fangfang Zhang, Xinye Qiu, Sichuang Bao, Yi Qingdao Research Institute Beihang University 393 Songling Road Laoshan District Qingdao Shandong266100 China

ISBN: (纸本)9798331312176

With the rapid development of artificial intelligence (AI) technology, its application in commercial remote sensing satellite data processing and analysis has brought profound changes to the field of natural resource monitoring. AI technology provides efficient methods for processing and analyzing large amounts of remote sensing data, greatly improving the accuracy and efficiency of natural resource monitoring. This article summarizes the research progress of domestic and foreign scholars on the application of AI-enabled commercial remote sensing satellites, explores the application scenarios of how AI can empower commercial remote sensing satellites in the field of natural resource monitoring, and focuses on analyzing the improvement of data processing efficiency and pattern recognition capabilities of AI technology. Contributions to enhancement and decision support system optimization. At the same time, through typical case studies such as land resources monitoring, forestry pest and disease monitoring, and forest fire prevention, the specific solutions and implementation effects of AI-empowered commercial remote sensing satellites in actual natural resource monitoring scenarios are described in detail. Finally, this article also discusses the challenges existing in current applications in data acquisition, algorithm development, interdisciplinary cooperation, and technology implementation, and looks forward to the future development trend of Al-enabled commercial remote sensing satellites in the field of natural resource monitoring,in order to promote the sustainable and innovative development of commercial remote sensing satellite applications in the field of natural resource monitoring. Copyright © 2024 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Deforestation

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Repurposing Diffusion-Based image Generators for Monocular Depth Estimation

Repurposing Diffusion-Based Image Generators for Monocular D...

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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Ke, Bingxin Obukhov, Anton Huang, Shengyu Metzger, Nando Daudt, Rodrigo Caye Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland

ISBN: (纸本)9798350353006

Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the growth in model capacity, from relatively modest CNNs to large Transformer architectures. Still, monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout, since their knowledge of the visual world is restricted by the data seen during training, and challenged by zero-shot generalization to new domains. This motivates us to explore whether the extensive priors captured in recent generative diffusion models can enable better, more generalizable depth estimation. We introduce Marigold, a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic training data. It delivers state-of-the-art performance across a wide range of datasets, including over 20% performance gains in specific cases. Project page: https://***.

关键词： ddim ddpm depth estimation diffusion generative LDM vision

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(Street) Lights Will Guide You: Georeferencing Nighttime Astronaut Photography of Earth

(Street) Lights Will Guide You: Georeferencing Nighttime Ast...

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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Stoken, Alex Ilhardt, Peter Lambert, Mark Fisher, Kenton NASA Johnson Space Ctr Jacobs Technol Washington DC 20546 USA NASA Johnson Space Ctr Washington DC 20546 USA

ISBN: (纸本)9798350365474

Astronaut photography from the International Space Station provides the highest spatial resolution nighttime Earth observations imagery publicly available, offering up to a 150x increase in resolution over other freely accessible satellite data sources. Yet, this imagery is underutilized in science applications because it lacks the geolocation meta-data required for downstream analysis. We present Night-Match, a fast and accurate method for localizing and georectifying nighttime astronaut photography. By combining street network data with daytime satellite imagery, we produce a reliable reference target for similarity detection via pairwise image matching. We curate and release the Astronaut imagery Matching Subset - Night (AIMS-Night), a collection of 363 images and ground truth localizations, and benchmark our method against this set to establish a robust localization pipeline. Our method correctly localizes 81.8% of AIMS-Night, and can be quickly deployed on the over 2 million nighttime astronaut photographs to produce a high quality analysis-ready data product.

关键词： Astronaut Photography image Matching Nighttime imagery remote sensing

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Roadmap for Measurement and Applications: Uncertainty Quantification and Visualization for Optimal Decision-Making in Hyperspectral Imaging-Based Precision Agriculture

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2025年第1期28卷 23-32页

作者： Faisal, Shah Ooi, Melanie Po-Leen Abeysekera, Sanush K. Kuang, Ye-Chow Fletcher, Dale Univ Waikato Hamilton New Zealand Univ Waikato Sch Engn Hamilton New Zealand Univ Waikato Fac Sci & Engn Hamilton New Zealand Waikato Robot Automat & Sensing Grp Hamilton New Zealand Univ Waikato Sch Comp & Math Sci Appl Machine Learning Grp Hamilton New Zealand

Precision agriculture is rapidly transforming the production workflow for crop monitoring and food quality control through the use of state-of-the-art instrumentation and measurement (I&M) methods that gather information about the crop rapidly and non-destructively. Specifically, the use of hyperspectral imaging (HSI) systems provides the capability to acquire not only spatial but also spectral details. It is no surprise therefore that HSI has become a notable instrument in many areas such as remote sensing and agriculture [1]. Yet despite the usefulness of the instrument, extracting relevant or useful information from HSI data is not an easy task, particularly in uncontrolled lighting conditions which affect the measurement of spectral responses of the object-under-test. In recent years, advancements in Machine Learning (ML) and Deep Learning (DL) have shown success in extracting useful features and performing complex pattern recognition across a range of problems. When used in combination with HSI, it has the potential to extract spectral responses related to plant phenotypes or chemical compounds, thus linking its spectral measurements to crop traits such as ripeness, onset of disease, and nutrient/water deprivation. Fig. 1 illustrates the four key computational steps involved in the use of HSI measurement systems: imaging, data processing, data analysis, and decision-making.

关键词： Precision agriculture Instruments Decision making Crop yield Quality control Production management Feature extraction Reliability Hyperspectral imaging Smart agriculture Food processing Food security

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Research on image recognition Technology of Overhead Transmission Line Damage

Research on Image Recognition Technology of Overhead Transmi...

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2024 International Conference on remote sensing, Mapping, and image processing, RSMIP 2024

作者： Zhu, Qianxin Fang, Ming Wang, Qianjin Yang, Ten Yin, Hongjiang Zhang, Yi Zhang, Bingzhen Wang, Yaoping Dang, Junpeng Wang, Tao Yunnan Power Grid Co. Ltd. Yuxi Power Supply Bureau Yuxi China

ISBN: (纸本)9781510680012

The transmission line conductors are an essential component of power system operation. Due to prolonged exposure to the external environment, these conductors are vulnerable to damage caused by various factors, leading to power system failures that have significant impacts on production and daily life. To monitor the condition of transmission conductors, assess the extent of damage, and ensure stable and safe grid operation, we propose an intelligent wire damage identification technology based on transmission wire damage images. Initially, we apply gray variance normalization and median filtering techniques to enhance the quality of the transmission wire image. Subsequently, local threshold segmentation is performed on the enhanced image to extract the wire area. Leveraging the characteristic that damaged areas exhibit higher gray values compared to other regions, we employ a gray value vertical and horizontal projection algorithm along with subsequent filtering processes for identifying surface damages on wires. This approach effectively transforms wire damage recognition into a data processing problem. Finally, through a series of experiments conducted in power maintenance operations, we validate its feasibility. © 2024 SPIE.

关键词： Damage detection

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SAR image edge detection: review and benchmark experiments

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INTERNATIONAL JOURNAL OF remote sensing 2022年第14期43卷 5372-5438页

作者： Meester, M. J. Baslamisli, A. S. OPT NET BV Kerkedijk 7A NL-1862 BD Bergen Netherlands Univ Amsterdam Comp Vis Lab Amsterdam Netherlands

Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art.

关键词： Synthetic aperture radar (SAR) remote-sensing image processing edge detection filtering speckle noise fusion benchmark

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A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2023年第1期202卷 87-113页

作者： Han, Wei Zhang, Xiaohan Wang, Yi Wang, Lizhe Huang, Xiaohui Li, Jun Wang, Sheng Chen, Weitao Li, Xianju Feng, Ruyi Fan, Runyu Zhang, Xinyu Wang, Yuewei China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Peoples R China

Due to limited resources and environmental pollution, monitoring the geological environment has become essential for many countries' sustainable development. As various high-resolution remote-sensing (RS) imaging platforms are continuously available, the remote sensing of the geological environment (GERS) provides a finegrain, all-weather, and low-cost method for identifying geological elements. Mainstream machine learning (ML) and deep learning (DL) methods can extract high-level high-dimensional semantic information and thus supply an efficient tool for high-precision classification and recognition in many fields. Therefore, the integration of advanced methods and multi-source RS images for GERS interpretation has achieved remarkable breakthroughs during the past decades. However, to the best of our knowledge, a systematic survey of the advances of GERS interpretation regarding ML and DL methods is still lacking. Through the collection of extensive published research in this area, this survey outlines and analyzes the challenges, progress, and promising directions of GERS interpretation. Specifically, the main challenges and difficulties in identifying GERS elements are first summarized in four aspects: sufficient element characteristics and variations, complex context disturbance, RS image quality and types, and other limitations in GERS interpretation. Second, we systematically introduce various RS imaging platforms and advanced ML and DL methods for GERS interpretation. Third, the research status and trends of several GERS applications, including their use for lithology, soil, water, rock glacier, and geological disaster, are ultimately collected and compared. Finally, potential opportunities for future research are discussed. After the systematic and comprehensive review, the conclusive findings suggest that longtime large-scale GERS interpretation and corresponding change pattern analysis will be a significant future direction to meet the needs of environment

关键词： Geological environment remote sensing Machine learning Deep learning Geological applications

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Heart Seeker - A Guide Robot with Warmth

Heart Seeker - A Guide Robot with Warmth

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Wang, Haiwen Fang, Zupeng Ying, Fangtian Faculty of Humanities and Arts Macau University of Science and Technology China School of Art and Design Wuhan Technology and Business University Wuhan China

ISBN: (纸本)9781643685069

In response to the problem that more than 200 million people around the world are in moderate or severe visual impairment or blindness, the field of guiding the blind has been deeply studied by researchers for a long time. This study aims to develop an exclusive intelligent mobility navigation trolley for the blind based on neural network deep learning algorithm and combined with remote sensing digital image processing to help the blind travel independently and improve their quality of *** system employs neural network deep learning and intelligent voice interaction, remote sensing mapping supplemented by realistic data probabilistic method and auxiliary probabilistic segmentation method for image processing to accurately detect obstacles, applies the algorithm to unmanned autonomous driving technology, and fuses search and decoding algorithms to integrate an environment-aware sensor in the trolley, and uses the algorithm to process voice commands and provide navigation *** the test, the blind person controls the navigation trolley through voice commands and remote sensing localization combined with image modeling recognition, and obtains accurate navigation information and precise obstacle avoidance. The blind person was able to obtain information about the surrounding environment and plan the optimal path to provide a personalized *** guide robot provides a convenient, safe and personalized travel and navigation experience for blind users. It provides a new innovative technical solution for blind people to travel and has a broad application prospect. © 2024 The Authors.

关键词： remote sensing

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Semi-automatic Labeling of Satellite images Using Texture Features and Hough Circle Transformation

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SN Computer Science 2024年第5期5卷 1-12页

作者： Sundari, L. K. Sowmya Mallikarjuna, M.K. Halakeri, Pooja Hebbar, Ramachandra School of CIT REVA University Bangalore India RRSC –South Bangalore India

In order to extract valuable ground information from images, supervised classification is an extensively adopted technique. However, it has a substantial downside in that choosing and labeling training samples requires a lot of time and resources. Fortunately, recent developments in automatic information indexing have produced encouraging this development makes it possible to develop an automated method for labelling training samples as opposed to relying just on human interpretation. In this research paper, we present a unique technique for the automated recognition and annotation of training data for high-resolution image classification which will identify tree and non-tree. The proposed methodology consists of methods like pre-processing to enhance the quality, identifying features where distinctive characteristics are extracted from the images, applying NDVI a fundamental vegetation index which quantifies the presence of healthy vegetation, applying a 7*7 mask, spatial filter to enhance specific features in the image, image subtraction to highlights discrepancies between images and Hough circle transformation a robust method for circular shape detection to identify circular patterns associated with tree canopies detecting tree region. The proposed sampling methodology has been assessed using a series of high-resolution remote sensing images. The results indicate that the proposed method can reliably generate a large number of samples and can achieve satisfactory results when used with various classifiers. This research makes a significant contribution to high-resolution image classification by automating the labeling of training data, with a particular emphasis on tree identification. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： NDVI pattern spatial filters remote sensing Texture analysis Very high-resolution images

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A New remote sensing Electro-Optical System on Low-light Target 20

A New Remote Sensing Electro-Optical System on Low-light Tar...

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Conference on Electro-Optical and Infrared Systems - Technology and Applications XX

作者： Ge Jingjing Beijing Inst Space Mech & Elect Beijing 100094 Peoples R China

ISBN: (数字)9781510667044

ISBN: (纸本)9781510667037

In view of the application of optical remote sensing disaster emergency rescue under the complex environment of low illumination at night, the optical remote sensing imaging technology under the condition of low illumination and low signal-to-noise ratio was studied. The compressed sensing technology of thin film diffraction grating primary mirror is used to realize large-aperture optical acquisition. The sensing ability of large dynamic range is increased by Geiger pattern imaging technology, and the dim and weak targets are identified by semantic sensing algorithm. The system realizes target recognition under the condition of extremely low image signal-to-noise ratio through the design of the new system's main mirror flattening and the aliasing compression and decoupling of spatial information and spectral information. The technology has completed space-based scheme design and desktop principle verification tests, and the spectral resolution reaches 5nm, realizing fast target search and recognition.

关键词： Low-light level imaging Computational remote sensing Diffraction grating primary mirror Emergency disaster reduction

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