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Ground truth to fake geographies: machine vision and learning in visual practices

AI & SOCIETY 2021年第4期36卷 1253-1262页

作者： Gil-Fournier, Abelardo Parikka, Jussi FAMU Prague Czech Republic Univ Southampton Southampton Hants England

This article investigates the concept of the ground truth as both an epistemic and technical figure of knowledge that is central to discussions of machine vision and media techniques of visuality. While ground truth refers to a set of remote sensing practices, it has a longer history in operational photography, such as aerial reconnaissance. Building on a discussion of this history, this article argues that ground truth has shifted from a reference to the physical, geographical ground to the surface of the images echoing earlier points raised by philosopher Jean-Luc Nancy that there is a ground of the image that is central to the task of analysis beyond representational practices. Furthermore, building on the practices of pattern recognition, composite imaging, and different interpretational techniques, we discuss contemporary practices of machine learning that mobilizes geographical earth observation datasets for experimental purposes, including tests such as "fake geography" as well as artistic practices, to show how ground truth is operationalized in such contexts of AI and visual arts.

关键词： remote sensing Machine vision Machine learning Visual culture Operational image

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Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for remote sensing

arXiv

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arXiv 2025年

作者： Zi, Xing Jin, Kairui Tao, Xian Li, Jun Braytee, Ali Shah, Rajiv Ratn Prasad, Mukesh University of Technology Sydney Australia Chinese Academy of Sciences China Indraprastha Institute of Information Technology Delhi India

Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks. Despite their advances, challenges specific to remote sensing remain substantial. Firstly, The SAM without clear prompt constraints, often generates redundant masks, and making post-processing more complex. Secondly, the CLIP model, mainly designed for global feature alignment in foundational models, often overlooks local objects crucial to remote sensing. This oversight leads to inaccurate recognition or misplaced focus in multi-target remote sensing imagery. Thirdly, both models have not been pre-trained on multi-scale aerial views, increasing the likelihood of detection failures. To tackle these challenges, we introduce the innovative VTPSeg pipeline, utilizing the strengths of Grounding DINO, CLIP, and SAM for enhanced open-vocabulary image segmentation. The Grounding DINO+(GD+) module generates initial candidate bounding boxes, while the CLIP Filter++(CLIP++) module uses a combination of visual and textual prompts to refine and filter out irrelevant object bounding boxes, ensuring that only pertinent objects are considered. Subsequently, these refined bounding boxes serve as specific prompts for the FastSAM model, which executes precise segmentation. Our VTPSeg is validated by experimental and ablation study results on five popular remote sensing image segmentation datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： image segmentation

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Design of Cloud Platform for Efficient remote sensing Big Data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Yan Huang Weimeng Zhang Xi’an Traffic Engineering Institute Xi’an Shaanxi China Xi’an Technical School Xi’an Shaanxi China

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management.

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image processing based data reduction technique in WVSN for smart agriculture

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COMPUTING 2023年第12期105卷 2675-2698页

作者： Koteich, Jana Salim, Christian Mitton, Nathalie Inria Villeneuve Dascq France Junia Comp Sci & Math F-59000 Lille France

Nowadays, to improve animal well being in livestock farming application, a wire-less video sensor network (WVSN) can be deployed to early detect injury and moni-tor animals. They are composed of small embedded video and camera motes that capture video frames periodically and send them to a specific node called a sink. Sending all the captured images to the sink consumes a lot of energy on every sensor and may cause a bottleneck at the sink level. Energy consumption and bandwidth limitation are two important challenges in WVSNs because of the limited energy resources of the nodes and the medium scarcity. In this work, we introduce two mechanisms to reduce the overall number of frames sensed and sent to the sink. The first approach is applied on each sensor node, where the FRABID algorithm, a joint data reduction, and frame rate adaptation on sensing and transmission phases mechanism is introduced. This approach reduces the number of sensed frames based on a similarity method. The aim is to adapt the number of sensed frames based on the degree of difference between two consecutive sensed frames in each period. This adaptation technique maintains the accuracy of the video while capturing frames holding new information. This approach is validated through simulations using real data-sets from video sensors (Wang et al. in: 2014 IEEE conference on computer vision and pattern recognition workshops, pp 393-400, 2014). The results show that the amount of sensed data is reduced by more than 70% compared to a recent algorithm in Christian et al. (Multimed Tools Appl 79(3):1801-1819, 2020) while guaranteeing the detection of all the critical events at the sensor node level. The sec-ond approach exploits the Spatio-temporal correlation between neighboring nodes to reduce the number of captured frames. For that purpose, Synchronization with Frame Rate Adaptation SFRA algorithm is introduced where overlapping nodes capture frames in a synchronized fashion every N - 1 periods, wher

关键词： Smart agriculture Spatio-temporal correlation Data reduction Data prediction WSN

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Toward On-Board Panoptic Segmentation of Multispectral Satellite images

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2023年 61卷

作者： Fernando, Tharindu Fookes, Clinton Gammulle, Harshala Denman, Simon Sridharan, Sridha Queensland Univ Technol Signal Proc Artificial Intelligence & VisionTechno Brisbane Qld 4000 Australia

With tremendous advancements in low-power embedded computing devices and remote sensing instruments, the traditional satellite image processing pipeline which includes an expensive data transfer step prior to processing data on the ground is being replaced by on- board processing of captured data. This paradigm shift enables critical and time-sensitive intelligence to be acquired in a timely manner on- board the satellite itself. However, at present, the on- board processing of multispectral satellite images is limited to classification and segmentation tasks. Extending this processing to the next logical level, we take the first step toward on- board panoptic segmentation of multispectral satellite images and evaluate the applicability of state-of-the-art panoptic segmentation models to an on- board setting. Panoptic segmentation offers major economic and environmental insights, ranging from yield estimation from agricultural lands to intelligence for complex military applications. Nevertheless, the on- board intelligence extraction poses several challenges due to the loss of temporal observations and the need to generate predictions from a single sample. To address this challenge, we propose a multimodal teacher network with a cross modality attention-based fusion strategy to improve segmentation accuracy by exploiting data from multiple modes. We also propose an online knowledge distillation framework to transfer the knowledge learned by this multimodal teacher network to a unimodal student, which receives only a single frame input, and is more appropriate for an on- board environment. We benchmark our approach against existing state-of-the-art panoptic segmentation models using the PASTIS multispectral panoptic segmentation dataset considering an on- board processing setting. Our evaluations demonstrate a substantial 10.7%, 11.9%, and 10.6% increase in segmentation quality (SQ), recognition quality (RQ), and panoptic quality (PQ) metrics compared to the existing

关键词： image segmentation Satellites Computer architecture Benchmark testing Pipelines Knowledge engineering Task analysis Knowledge distillation multimodality fusion multispectral image processing on-board satellite image processing panoptic segmentation

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Unsupervised double weighted graphs via good neighbours for dimension reduction of hyperspectral image

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INTERNATIONAL JOURNAL OF remote sensing 2022年第15-16期43卷 6152-6175页

作者： Chou, Jiahui Zhao, Siyu Chen, Yingyi Jing, Ling China Agr Univ Coll Sci Beijing 100091 Peoples R China China Agr Univ Natl Innovat Ctr Digital Fishery Beijing Peoples R China China Agr Univ Yantai Inst Yantai Shandong Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China Minist Agr & Rural Affairs Key Lab Smart Farming Technol Aquat Anim & Livest Beijing Peoples R China

As the major research in pattern recognition, unsupervised dimension reduction is a challenging problem because of no label information. Most unsupervised dimension reduction methods usually construct similarity graph by k-nearest neighbour to preserve local structure in the low-dimensional subspace. However, k-nearest neighbour is calculated by Euclidean distance, which is sensitive to noise and outliers. And only considering local structure will reduce the classification accuracy. In this paper, a new unsupervised dimension reduction method called unsupervised double-weighted graphs via good neighbours (uDWG-GN) is proposed. First, uDWG-GN proposes a local structure Low-Rank Representation to learn similarity matrix and then uses the similarity matrix to find good neighbours of each sample. Second, according to good neighbours of the sample, uDWG-GN considers similar and dissimilar relationship between samples and constructs double-weighted graphs. Finally, based on L-2,L-1 norm, uDWG-GN finds the optimal projection matrix by maximizing the distance of dissimilar samples and minimizing the distance of similar samples. Experimental results on three hyperspectral images demonstrate the superiority and effectiveness of our method compared with other dimension reduction methods.

关键词： Unsupervised learning dimension reduction good neighbour feature extraction hyperspectral data

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ANALYSIS: IMPACT OF image MATCHING METHODS ON JITTER COMPENSATION 24

ANALYSIS: IMPACT OF IMAGE MATCHING METHODS ON JITTER COMPENS...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Ye, G. Pan, J. Wang, M. Zhu, Y. Jin, S. Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Inst Technol Sch Elect & Informat Engn Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China

The degradation of image quality caused by satellite jitter has drawn attention, and many researches have illustrated the importance of the jitter compensation. As the essential component of jitter compensation, image matching involves in the determination of jitter processing accuracy. Hence, the impact of imaging matching methods on jitter compensation is explored in this paper. At first, a framework based on imaging matching is built for jitter compensation. Two typical sub-pixel accuracy matching methods (i.e. correlation coefficient and least squares matching, as well as phase correlation matching) could then be served for the framework. The experiments are designed by using multispectral images of GF-1 satellite, and quantitative evaluations show that compared with correlation coefficient and least squares matching, phase matching method makes the accuracy of obtained jitter curve increase with the amplitude error decreasing by more than 0.012 pixel as well as phase error decreasing by more than 0.0119 rad, and makes the quality of restored images improved on both geometry and radiation. It indicates that phase matching method has better performance with respect to the framework of jitter compensation in this paper.

关键词： Satellite jitter Jitter compensation Multispectral images image matching GF-1 satellite

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A probabilistic collaborative dictionary learning-based approach for face recognition

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IET image processing 2021年第4期15卷 868-884页

作者： Lv, Shilin Liang, Jiuzhen Di, Lan Yunfei, Xia Hou, ZhenJie Changzhou Univ Sch Comp Sci & Artificial Intelligence Changzhou 213164 Jiangsu Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Univ North Carolina Charlotte Dept Math & Stat Charlotte NC USA

Although Sparse Representation based Classifier (SRC), a non-parametric model, can obtain an interesting result for pattern recognition , a reasonable interpretation has been lacked for its classification mechanism. What is more, the training samples are used as off-the-shelf dictionary directly in SRC, which can make the feature hidden in the training samples hard be extracted. At the same time, the complexity of the algorithm is increased because of too many atoms of the dictionary. The authors first explains in detail the classification mechanism of SRC from the view of probabilistic collaborative subspace and offer the process to improve the stability of the algorithm using the joint probability in the case of the multi-subspace. Then, the authors introduce the dictionary learning (DL) and Fisher criterion into the model to further enhance the discrimination of the coding coefficient. In order to ensure the convexity of the discrimination term and further enhance the discrimination, the authors add the L-21-norm term into the Fisher discrimination term and offer the proof for its convexity. Finally, the experimental result on a series of benchmark databases, such as AR, Extended Yale B, LFW3D-hassner, LFW3D-sdm and LFW3D-Dlib, show that PCDDL outperforms existing classical classification models.

关键词： probability image representation compressed sensing coding coefficient training samples linear discriminant analysis probabilistic collaborative dictionary learning joint probability Fisher discrimination term image recognition Computer vision and image processing techniques Data handling techniques norm term image classification classical classification models feature extraction off-the-shelf dictionary SRC probabilistic collaborative subspace Fisher criterion nonparametric model face recognition pattern recognition reasonable interpretation sparse representation classifier Other topics in statistics

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An Intelligent Registration Method of Heterogeneous remote sensing images Based on Style Transfer 22

An Intelligent Registration Method of Heterogeneous Remote S...

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and pattern recognition

作者： Haoyang Tang Xin Miao Jiakun Shi Zhifan Hua Dongfang Yang College of Automation Xi 'an University of Posts and Telecommunications China The graduate school Rocket Force Engineering University China

ISBN: (纸本)9781450396899

Intelligent registration of heterogeneous remote sensing images is a hot issue in the field of remote sensing and has important research and application values. Due to the significant differences in data sources, image texture, color space and other factors of heterogeneous remote sensing images, the traditional image registration algorithms are difficult to be directly applied to the intelligent registration of images from different sources. In this paper, a new method of intelligent registration of heterogenous remote sensing images based on style transfer is proposed. First, the content features of the baseline image and the style features of the image to be aligned are extracted by using the generative adversarial nets, and the remote sensing images from different sources are fused in terms of content and style to obtain the remote sensing image transfer results with the same style as the image to be aligned and keeping the content of the baseline image unchanged. Then, an intelligent image registration algorithm is used to match the migrated remote sensing images. Finally, a heterogenous remote sensing image dataset is constructed around the heterogenous remote sensing image registration task, and the intelligent registration method of this paper is experimentally validated based on this dataset. The experimental results show that the intelligent image registration method based on style transfer can effectively improve the accuracy of heterogenous remote sensing image registration compared with the direct image registration method.

关键词： Heterogenous remote sensing image data set Homography transformation N-point method Style transfer image registration

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Disaster Site Structure Analysis: Examining Effective remote sensing Techniques in Blue Tarpaulin Inspection

Disaster Site Structure Analysis: Examining Effective Remote...

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作者： Miles, Madeline Grace Jacksonville State University

学位级别：M.G.I.S., Master of Geographic Information Science

This thesis aimed to evaluate three methods of analyzing blue roofing tarpaulin (tarp) placed on homes in post natural disaster zones with remote sensing techniques by assessing the different methods- image segmentation, machine learning (ML), and supervised classification. One can determine which is the most efficient and accurate way of detecting blue tarps. The concept here was that using the most efficient and accurate way to locate blue tarps can aid federal, state, and local emergency management (EM) operations and homeowners. In the wake of a natural disaster such as a tornado, hurricane, thunderstorm, or similar weather events, roofs are the most likely to be damaged (Esri Events., 2019). Severe roof damage needs to be mitigated as fast as possible: which in the United States is often done at no cost by the Federal Emergency Management Agency (FEMA). This research aimed to find the most efficient and accurate way of detecting blue tarps with three different remote sensing practices. The first method, image segmentation, separates parts of a whole image into smaller areas or categories that correspond to distinct items or parts of objects. Each pixel in a remotely sensed image is then classified into categories set by the user. A successful segmentation will result when pixels in the same category have comparable multivariate, grayscale values and form a linked area, whereas nearby pixels in other categories have distinct values. Machine Learning, ML, a second method, is a technique that processes data depending on many layers for feature identification and pattern recognition. ArcGIS Pro mapping software processes data with ML classification methods to classify remote sensing imagery. Deep learning models may be used to recognize objects, classify images, and in this example, classify pixels. The resultant model definition file or deep learning software package is used to run the inference geoprocessing tools to extract particular item positions, categorize

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