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IEEE International Geoscience and remote sensing Symposium (IGARSS)

作者： Hong, Danfeng Chanussot, Jocelyn Zhu, Xiao Xiang German Aerosp Ctr Remote Sensing Technol Inst Wessling Germany Univ Grenoble Alpes LJK Grenoble INP INRIACNRS Grenoble France Tech Univ Munich Data Sci Earth Observat Munich Germany

ISBN: (纸本)9781665403696

With the ever-growing availability of different remote sensing (RS) products from both satellite and airborne platforms, simultaneous processing and interpretation of multimodal RS data have shown increasing significance in the RS field. Different resolutions, contexts, and sensors of multimodal RS data enable the identification and recognition of the materials lying on the earth's surface at a more accurate level by describing the same object from different points of the view. As a result, the topic on multimodal RS data fusion has gradually emerged as a hotspot research direction in recent years. This paper aims at presenting an overview of multimodal RS data fusion in several mainstream applications, which can be roughly categorized by 1) image pansharpening, 2) hyperspectral and multispectral image fusion, 3) multimodal feature learning, and (4) crossmodal feature learning. For each topic, we will briefly describe what is the to-be-addressed research problem related to multimodal RS data fusion and give the representative and state-of-the-art models from shallow to deep perspectives.

关键词： Classification crossmodal data fusion deep learning feature learning multimodal pansharpening remote sensing shallow models

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Self-Correcting Capsule Network on remote sensing Scene Classification

Self-Correcting Capsule Network on Remote Sensing Scene Clas...

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image processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Dongdonz Kan Yue Cao School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Management & Marketing-MSc (MSCMG) University College Cork Cork Ireland

Most of the current capsule network methods have good classification effects mainly on simple content datasets such as MNIST and CIFAR10. However, for remote sensing scene images with complex objects, these methods cannot well resolve the main objects in the images, which in turn affects the recognition of scene categories. To address the above problems, this paper proposes a self-correcting capsule network with the ability to enhance the main object information contained in the features. First, the original features of the image are encapsulated into capsules. Then, the part extraction layer goes to learn the part information of the main targets in the scene and corrects the information of the primary capsule features, so as to parse the parts of different objects in the image. Since the traditional dynamic routing algorithms cannot be applied well in remote sensing scenes, in order to get the category information effectively, a class activation routing is proposed to act into the part capsules by learning the class activation weights in the original features, and using the routing to learn the spatial relationship between different parts to combine different part capsules to calculate the capsules rich in category information. The method in this paper effectively improves the classification performance of the network.

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Detection of remote sensing Warp Tension during Weaving on Plain Twill and Satin Fabrics

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FIBRES & TEXTILES IN EASTERN EUROPE 2021年第1期29卷 35-39页

作者： Kaplan, Volkan Pamukkale Univ Fac Engn Dept Text Engn Kinikli Campus Denizli Turkey

Primp tensions were measured while a machine was operating on a woven cotton fabric with three different woven patterns. This study was carried out with image analysis methods using a high speed camera. Three weave pattern types: plain, twill and satin were woven on the same weaving machine, and thus it could be understood how weave pattern differences affect warp tension. Each of these three weaves was woven in three weft densities: 20, 28 and 45 wefts per cm. These fabrics were able to be made on a weaving machine with an automatic dobby. It was aimed to investigate warp tension differences for three basic weave patterns while keeping all machine settings constant. The weave settings of the dobbv were changed for plain, twill and satin weaves. Warp tension calculation was based on the warp elasticity theory. Warp elasticises were measured by image processing methods in MATLAB using a high-speed camera. It was aimed to improve upon the new method of warp extension measurement of fabric when the loom is in operation. It was observed that the warp tension in plain fabric was higher than for twill and satin under the same conditions.

关键词： weaving fuzzy logic warp tension image processing

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AI Augmentation to remote sensing imagery in Forestry Conservation and Restoration for Increased Responsive Capabilities

AI Augmentation to Remote Sensing Imagery in Forestry Conser...

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2022 IEEE Applied imagery pattern recognition Workshop, AIPR 2022

作者： Gandikota, Dhanuj Mount Gladkova, Taissa Tran, Kha-Ai Bapat, Sanika Richkus, Jenn Arnold, Dr. Jeffrey The MITRE Corporation Artificial Intelligence Research Engineer McleanVA United States The MITRE Corporation Mechanical and Human Factors Engineer BedfordMA United States The MITRE Corporation Geospatial Computing Engineer McleanVA United States The MITRE Corporation Artificial Intelligence Research Engineer BedfordMA United States The MITRE Corporation Climate Strategist McleanVA United States The MITRE Corporation Chief Climate and Environmental Scientist McleanVA United States

ISBN: (纸本)9781665477291

Responsive forestry management is critical to carbon management and climate change mitigation. The United Nation's Intergovernmental Panel on Climate Change (IPCC) Special Report stated that mitigation measures in forests from 2010-2019 have delivered approximately 80% of carbon mitigation from Land Use sectors. Advancements in remote sensing (RS) and Artificial Intelligence (AI) technologies hold significant potential to increase the necessary coverage and speed of forestry management;however, there is a delay in the current capability integration due to technical, cost, and human-factor constraints that prevent adoption and deployment. This research details the formation of the ART3MIS-AI (Augmented Real-Time 3D Mapping with Intelligent sensing AI) system framework, an AI-Augmentation framework design that minimizes these constraints. It is tailored for responsive deployment in forestry agencies and provides long-Term robustness and extensibility for all four functions of forestry management. Our research analyzes AI capabilities in forestry RS from 75 research papers and assesses the potential integration pathways of these capabilities into the post-processing infrastructures of RS within forestry management for augmented-data transformation and critical metric extraction. The ART3MIS-AI system framework optimizes these observed capabilities for additional high levels of interpretability and extensibility given the technical and sensing platform constraints of forestry agencies. We generated a set of objectives and guidelines to facilitate the responsive deployment and integration of AI-Augmentation frameworks within forestry agencies. The guidelines are based on established work in Human Machine Teaming, AI-Assurance, and prior research, in addition to consultation from our forestry professional partners in United States forestry agencies. The proposed ART3MIS-AI system framework is a detailed four-phase, automated and pipelined system implemented in Python and

关键词： Climate change

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基于光学遥感图像的土地利用空间格局识别

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勘察科学技术 2025年第1期 68-74页

作者：李彦粉北京威特空间科技有限公司北京市102600

土地利用空间在格局上的分界差异很小,当前土地利用空间格局识别过程中主要使用以电磁波为主的普通遥感图像,受困于采集的土地空间遥感特征尺度差异大,几何偏差突变区域多,导致土地利用空间格局识别效果差,该文提出了基于光学遥感图像... 详细信息

土地利用空间在格局上的分界差异很小,当前土地利用空间格局识别过程中主要使用以电磁波为主的普通遥感图像,受困于采集的土地空间遥感特征尺度差异大,几何偏差突变区域多,导致土地利用空间格局识别效果差,该文提出了基于光学遥感图像的土地利用空间格局识别方法。该方法利用无人机采集遥感图像,设计一种根据基准图像和校正图像特征对应关系的图像拼接技术,以克服当前大差异土地光学遥感图像几何校正的弊端;设计了一种暗通道和双边滤波相结合的方法,对校正后的图像进行增强处理,该方法在滤波过程中考虑了像素之间的空间距离和灰度相似性,双边滤波能够降低图像噪声并保持图像细节,同时避免了传统高斯滤波对边缘信息的模糊处理;通过融合多尺度与主方向因子的灰度共生矩阵纹理特征,完成土地利用空间格局的识别。实验结果表明,该方法对土地利用空间格局识别准确率高、效率高且效果好。

关键词：光学遥感图像土地利用空间格局识别几何校正图像增强

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Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters

Revisiting pre-trained remote sensing model benchmarks: resi...

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IEEE Computer Society Conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Isaac Corley Caleb Robinson Rahul Dodhia Juan M. Lavista Ferres Peyman Najafirad University of Texas at San Antonio San Antonio TX USA Microsoft AI for Good Research Lab Redmond WA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets containing remotely sensed imagery. A common benchmark case is to evaluate SSL pre-trained model embeddings on datasets of remotely sensed imagery with small patch sizes, e.g., 32 × 32 pixels, whereas standard SSL pre-training takes place with larger patch sizes, e.g., 224 × 224. Furthermore, pre-training methods tend to use different image normalization preprocessing steps depending on the dataset. In this paper, we show, across seven satellite and aerial imagery datasets of varying resolution, that by simply following the preprocessing steps used in pre-training (precisely, image sizing and normalization methods), one can achieve significant performance improvements when evaluating the extracted features on downstream tasks – an important detail overlooked in previous work in this space. We show that by following these steps, imageNet pre-training remains a competitive baseline for satellite imagery based transfer learning tasks – for example we find that these steps give +32.28 to overall accuracy on the So2Sat random split dataset and +11.16 on the EuroSAT dataset. Finally, we report comprehensive benchmark results with a variety of simple baseline methods for each of the seven datasets, forming an initial benchmark suite for remote sensing imagery. 1

关键词： Satellites image resolution Transfer learning Self-supervised learning Benchmark testing Feature extraction Satellite images

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remote sensing Object Detection Method Based on Three-Dimensional Weight-Guided Selection Kernel

Remote Sensing Object Detection Method Based on Three-Dimens...

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image processing and Media Computing (ICIPMC), International Conference on

作者： Min Gong Kun Yan Shenglin Peng Shuyi Qu School of Information Science and Technology Northwest University Xi’an China China Academy of Space Technology(Xi’an) Xi’an China Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality Xi’an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

In recent years, research on remote sensing object detection has primarily focused on oriented box representation and optimization, while overlooking the importance of studying feature extraction networks, leading to inaccurate identification of some targets. The conventional feature extraction networks employ fixed-size convolutional kernels, which fail to capture different levels of visual features for targets of varying sizes. In this paper, we propose a mechanism called the 3D-Weight Guide Selective Kernel (3D-WGSK) to jointly utilize spatial and channel information of input features to derive three-dimensional weights. These weights are then employed for convolutional kernel selection, enabling adaptive receptive field acquisition for capturing features of targets with different sizes. We devise a network architecture named the 3D-WGSK Net for feature extraction in remote sensing object detection. It possesses the capability of multi-scale information fusion and adaptive kernel adjustment, effectively extracting crucial features of targets with varying sizes. Experimental results on the publicly available DOTA-v1.5 dataset demonstrate the superior detection performance of our method.

关键词： Location awareness Visualization Target recognition Object detection Feature extraction Reliability engineering Kernel

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Efficient Object Detection and Classification of Ground Objects from Thermal Infrared remote sensing image Based on Deep Learning 4th

Efficient Object Detection and Classification of Ground Obje...

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4th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Wu, Falin Zhou, Guopeng He, Jiaqi Li, Haolun Liu, Yushuang Yang, Gongliu Beihang Univ Sch Instrumentat & Optoelect Engn SNARS Lab Beijing 100191 Peoples R China Beijing Syst Design Inst Electromech Engn Beijing 100854 Peoples R China

ISBN: (纸本)9783030880132;9783030880125

Wild searching and nature reserve monitoring are formidable tasks. In order to relieve the current pressure of generalmanpower observation, drone aerial surveillance using visible and thermal infrared (TIR) cameras is increasingly being adopted. Automatic data acquisition has become easier with advances in unmanned aerial vehicles (UAVs) and sensors like TIR cameras, which enables executives to search and detect ground objects at night. However, it's still a challenge to accurately and quickly process the large amount of TIR data generated from this. In response to the above problems, this paper designs an enhanced ground object detection network (UAV-TIR Retinanet) for theUAVthermal imaging system. The network uses the Retinanet as infrastructure, extracts shallowfeatures according to the characteristics of thermal infrared remote sensing images, introduces an attention mechanism and adaptive receptive field mechanism. The method achieves the best speed-accuracy trade-off on the dataset, reporting 74.47% AP at 23.48 FPS.

关键词： Ground object detection Thermal infrared remote sensing image Deep learning Attention mechanism Adaptive multiscale receptive field

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remote sensing Predicts Long-term Indicators of Governance, Stability, and Well-being

Remote Sensing Predicts Long-term Indicators of Governance, ...

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IEEE Applied imagery pattern recognition Workshop (AIPR)

作者： Irvine, John M. Angelini, Brigid Crystal, Michael MITRE Corp Burlington Rd Bedford MA 01730 USA Liberty Mutual Boston MA USA Draper Lab Cambridge MA USA

ISBN: (纸本)9781665424714

Understanding a region's socio-economic conditions can inform the development of policies in both the public and private sectors. Commercial satellite imagery provides a potential avenue for abstracting socio-economic context in a quick and relatively inexpensive manner. Satellite images contain infrastructural and agricultural information that, in a previous study focused on Afghanistan and Botswana, provided a useful for characterizing regional socio-economic information. Previous studies have compared survey responses to imagery feature, using supervised machine learning models. Building on previous work, this study explores long-term assessments of a country. As with previous studies, image features extracted from commercial imagery form the explanatory variables for our models. In this case, however, we seek to predict annual indicators of conditions for a country as assessed by the World Bank. The models relate imagery-derived features to indicators of Political Stability, Control of Corruption, Rule of Law, Government Effectiveness, Voice and Accountability, and Gross Domestic Product using data from multiple countries.

关键词： remote sensing imagery economic analysis governance social indicators

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Edge-Guided and Cross-Scale Feature Fusion Network for Efficient Multi-contrast MRI Super-Resolution 27th

Edge-Guided and Cross-Scale Feature Fusion Network for Effic...

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27th International Conference on pattern recognition, ICPR 2024

作者： Yang, Zhiyuan Zhang, Bo Zeng, Zhiqiang Yeo, Si Yong School of Electronic and Information Engineering Beihang University Beijing China College of Computing and Data Science Nanyang Technological University Singapore Singapore Beijing Institute of Remote Sensing Equipment Beijing China Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783031783975

In recent years, MRI super-resolution techniques have achieved great success, especially multi-contrast methods that extract texture information from reference images to guide the super-resolution reconstruction. However, current methods primarily focus on texture similarities at the same scale, neglecting cross-scale similarities that provide comprehensive information. Moreover, the misalignment between features of different scales impedes effective aggregation of information flow. To address the limitations, we propose a novel edge-guided and cross-scale feature fusion network, namely ECFNet. Specifically, we develop a pipeline consisting of the deformable convolution and the cross-attention transformer to align features of different scales. The cross-scale fusion strategy fully integrates the texture information from different scales, significantly enhancing the super-resolution. In addition, a novel structure information collaboration module is developed to guide the super-resolution reconstruction with implicit structure priors. The structure information enables the network to focus on high-frequency components of the image, resulting in sharper details. Extensive experiments on the IXI and BraTS2020 datasets demonstrate that our method achieves state-of-the-art performance compared to other multi-contrast MRI super-resolution methods, and our method is robust in terms of different super-resolution scales. Our code is available at https://***/zhiyuan-yang/Edge-Guided-Cross-Scale-MRI-Super-resolution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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