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Multisensor Diffusion-Driven Optical Image Translation for Large-Scale Applications

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 1515-1536页

作者： Vinholi, Joao Gabriel Chini, Marco Amziane, Anis Machado, Renato Silva, Danilo Matgen, Patrick Luxembourg Inst Sci & Technol LIST L-4362 Esch Sur Alzette Luxembourg Aeronaut Inst Technol ITA Dept Telecommun BR-12228900 Sao Josedos Campos SP Brazil Fed Univ Santa Catarina UFSC Dept Elect & Elect Engn BR-88040900 Florianopolis SC Brazil

Comparing images captured by disparate sensors is a common challenge in remote sensing. This requires image translation-converting imagery from one sensor domain to another while preserving the original content. Denoising diffusion implicit models (DDIM) are potential state-of-the-art solutions for such domain translation due to their proven superiority in multiple image-to-image translation tasks in computer vision. However, these models struggle with reproducing radiometric features of large-scale multipatch imagery, resulting in inconsistencies across the full image. This renders downstream tasks like heterogeneous change detection impractical. To overcome these limitations, we propose a method that leverages denoising diffusion for effective multisensor optical image translation over large areas. Our approach super-resolves large-scale low spatial resolution images into high-resolution equivalents from disparate optical sensors, ensuring uniformity across hundreds of patches. Our contributions lie in new forward and reverse diffusion processes that address the challenges of large-scale image translation. Extensive experiments using paired Sentinel-II (10 m) and Planet Dove (3 m) images demonstrate that our approach provides precise domain adaptation, preserving image content while improving radiometric accuracy and feature representation. A thorough image quality assessment and comparisons with the standard DDIM framework and five other leading methods are presented. We reach a mean learned perceptual image patch similarity of 0.1884 and a Fr & eacute;chet Inception Distance of 45.64, expressively outperforming all compared methods, including DDIM, ShuffleMixer, and SwinIR. The usefulness of our approach is further demonstrated in two Heterogeneous change detection tasks.

关键词： Optical sensors Spatial resolution Noise reduction Optical imaging Diffusion models Training Radiometry Superresolution Remote sensing Image sensors change detection algorithms deep learning diffusion models image denoising image enhancement image quality image reconstruction remote sensing superresolution

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Object Covisibility Graph for change detection and 3D Object-Oriented Map Revision in Semi-Static Scenes

Object Covisibility Graph for Change Detection and 3D Object...

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IEEE/SICE International Symposium on System Integration

作者： Ziquan Wang Masahiko Mikawa Makoto Fujisawa Graduate School of Comprehensive Human Sciences University of Tsukuba Japan Institute of Library Information and Media Science University of Tsukuba Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Recently, Object-oriented SLAM(Object SLAM) has attracted extensive research due to its ability to perceive the environment at a 3d object level. Existing object SLAM methods mostly focus on constructing 3d object map for static objects or mitigating the impact of currently dynamic objects on localization and mapping. However, detection of semi-static objects whose position change while unobserved still pose a significant challenge, resulting in outdated maps, which could lead to localization and robot application failures. In this paper, we propose a method to compare current observation with the existing map, enabling the continuous detection and updating of outdated sections within the map caused by position-changing semi-static objects. First, we introduce Object Covisibility Graph(OCG) to maintain the historically observed co-visibility relationships between objects. Building on this, we design an algorithm that uses the OCG to determine whether the current camera is within the observable region of each object, and subsequently implement an object state updating algorithm to detect and update outdated sections continuously. We conduct experiments on our self-make dataset with changing objects and a dataset with only static objects. The experimental results show that our method updates the outdated parts of the map more effectively compared to previous studies.

关键词： Location awareness Three-dimensional displays Simultaneous localization and mapping change detection algorithms Buildings System integration Cameras Robots

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Improving Zero-Shot Object-Level change detection by Incorporating Visual Correspondence

Improving Zero-Shot Object-Level Change Detection by Incorpo...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Hung H. Nguyen Pooyan Rahmanzadehgervi Long Mai Anh Totti Nguyen Auburn University Adobe Research

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Detecting object-level changes between two images across possibly different views (Fig. 1) is a core task in many applications that involve visual inspection or camera surveillance. Existing change-detection approaches suffer from three major limitations: (1) lack of evaluation on image pairs that contain no changes, leading to unreported false positive rates; (2) lack of correspondences (i.e., localizing the regions before and after a change); and (3) poor zero-shot generalization across different domains. To address these issues, we introduce a novel method that lever-ages change correspondences (a) during training to improve change detection accuracy, and (b) at test time, to minimize false positives. That is, we harness the supervision labels of where an object is added or removed to supervise change detectors, improving their accuracy over previous work [25] by a large margin. Our work is also the first to predict correspondences between pairs of detected changes using estimated homography and the Hungarian algorithm. Our model demonstrates superior performance over existing methods, achieving state-of-the-art results in change detection and change correspondence accuracy across both in-distribution and zero-shot benchmarks.

关键词： Training Measurement Visualization Computer vision Accuracy change detection algorithms Surveillance Detectors Inspection Prediction algorithms

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Evaluation of clustering algorithms for unsupervised change detection in VHR remote sensing imagery

Evaluation of clustering algorithms for unsupervised change ...

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Urban Remote Sensing Joint Event

作者： Tobias Leichtle Christian Geiß Michael Wurm Tobia Lakes Hannes Taubenböck German Remote Sensing Data Center (DFD) German Aerospace Center (DLR) Wessling Germany Company for Remote Sensing and Environmental Research (SLU Munich Germany Deutsches Zentrum fur Luft und Raumfahrt Standort Bonn Bonn Nordrhein-Westfalen DE Geography Department Humboldt-Universität zu Berlin Berlin Germany

Remote sensing has proven to be an adequate tool for observation of changes to the Earth's surface. Especially modern space-borne sensors with very-high spatial resolution offer new capabilities for monitoring of dynamic urban environments. In this context, clustering is a well suited technique for unsupervised and thus highly automatic detection of changes. In this study, seven partitioning clustering algorithms from different methodological categories are evaluated regarding their suitability for unsupervised change detection. In addition, object-based feature sets of different characteristics are included in the analysis assessing their discriminative power for classification of changed against unchanged buildings. In general, the most important property of favorable algorithms is that they do not require additional arbitrary input parameters except the number of clusters. Best results were achieved based on the clustering algorithms k-means, partitioning around medoids, genetic k-means and self-organizing map clustering with accuracies in terms of κ statistics of 0.8 to 0.9 and beyond.

关键词： Clustering algorithms change detection algorithms Buildings Partitioning algorithms Classification algorithms Kernel Gray-scale

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Memory-Based algorithms for Abrupt change detection in Sensor Data Streams

Memory-Based Algorithms for Abrupt Change Detection in Senso...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Daniel Nikovski Ankur Jain Mitsubishi Electric Research Laboratories Inc. Cambridge USA

This paper describes two novel learning algorithms for abrupt change detection in multivariate sensor data streams that can be applied when no explicit models of data distributions before and after the change are available. One of the algorithms, MB -GT, uses average Euclidean distances between pairs of data sets as the decision variable, and the other, MB - CUSUM, is a direct extension of the CUSUM algorithm to the case when the unknown probability density functions are estimated by means of kernel density estimates. The algorithms operate on a sliding memory buffer of the most recent TV data readings, and consider all possible splits of that buffer into two contiguous windows before and after the change. Despite the apparent computational complexity of O(N 4 ) of this computation, our proposed algorithmic solutions exploit the structure present in their respective decision functions and exhibit computational complexity of only O(N 2 ) and memory requirement of O(N).

关键词： change detection algorithms Computational complexity Machine learning algorithms Sensor phenomena and characterization Computerized monitoring Space technology Electrical equipment industry Fuel processing industries Humans Probability distribution

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Sequential change detection based on universal compression algorithms

Sequential change detection based on universal compression a...

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IEEE International Symposium on Information Theory

作者： T. Jacob R. K. Bansal Department of Electrical Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9781424422562

The application of universal source coding algorithms to the problem of classification was initiated by Ziv and has been extended to other problems in statistics such as order estimation. In spite of the large literature, these studies have been limited to problems with fixed number of samples. In this paper we study the application of universal source coding to the problem of sequential hypothesis testing and sequential change detection. algorithms are proposed which are inspired by Waldpsilas Sequential Probability Ratio Test (SPRT) and Pagepsilas Cumulative Sum Test (CUSUM) for these problems respectively. Performance of the proposed algorithms are studied in the asymptotic regime to demonstrate their effectiveness.

关键词： change detection algorithms Testing Source coding Entropy Encoding Classification algorithms Probability

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change detection of enhanced, no-changed and reduced scattering in multi-temporal ERS-2 SARimages using the two-thresholds EM and MRF algorithms

Change detection of enhanced, no-changed and reduced scatter...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ya-Qiu Jin Key Laboratory of Wave Scattering and Remote Sensing Information Ministry of Education Fudan University Shanghai China

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Advanced algorithms for autonomous hyperspectral change detection

Advanced algorithms for autonomous hyperspectral change dete...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： A. Schaum A. Stocker Naval Research Laboratory Inc. Washington D.C. DC USA

Persistent ISR (intelligence surveillance and reconnaissance) has proven its value as a tactic for national defense. This activity can collect, in particular, information necessary for executing an important concept of operations: wide-area autonomous change detection over long time intervals. Here we describe the remarkable potential of hyperspectral remote sensing systems for enabling such missions, using either visible or thermal infrared wavelengths. First we describe blind change detection, in which no target knowledge is assumed. Targets that have moved can nevertheless be distinguished from naturally occurring background radiometric changes through the use of multivariate statistics informed by simple physics. detection relies on the ability of hyperspectral algorithms to predict certain conserved properties of background spectral patterns over long time intervals. We also describe a method of mitigating the most worrisome practical engineering difficulty in pixel-level change detection, image misregistration. This has led, in turn, to a method of estimating spectral signature evolution using multiple-scene statistics. Finally, we present a signature-based detection technique that fuses two discrimination mechanisms: use of some prior knowledge of target spectra, and the fact that a change has occurred.

关键词： change detection algorithms Hyperspectral sensors Hyperspectral imaging Statistics Surveillance Reconnaissance Remote sensing Radiometry Physics Prediction algorithms

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Search-based semi-supervised clustering algorithms for change detection in remotely sensed images

Search-based semi-supervised clustering algorithms for chang...

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IEEE India Conference (INDICON)

作者： Moumita Roy Susmita Ghosh Ashish Ghosh Department of Computer Science and Engineering Jadavpur University Kolkata India Center for Soft Computing Research Indian Statistical Institute Kolkata India

In real life change detection for remotely sensed images suffers due to the problem of inadequate labeled patterns. When a few labeled patterns can be collected by experts, semi-supervised (learning) clustering can be opted for change detection instead of the unsupervised approach to make full utilization of both labeled and unlabeled patterns. In the present work, a study has been carried out by applying some of the semi-supervised clustering techniques for changed detection. A comparative analysis between K-Means, COP-KMeans, Seeded-KMeans and Constrained-KMeans algorithms is being performed based on the results obtained using two multi-temporal remotely sensed images. It can be concluded from the experiments that the Constrained-KMeans is well suited for changed detection of remotely sensed images under semi-supervised framework.

关键词： Clustering algorithms Remote sensing change detection algorithms Partitioning algorithms Standards Training Satellites

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Combination of fuzzy clustering algorithms for change detection in remote sensing images

Combination of fuzzy clustering algorithms for change detect...

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International Conference on Emerging Applications of Information Technology (EAIT)

作者： Niladri Shekhar Mishra Susmita Ghosh Ashish Ghosh Department of Electronics and Communication Engineering Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Center for Soft Computing Research Indian Statistical Institute Kolkata India

Here we propose a methodology to combine the output of fuzzy clusterings to detect changes in remote sensing images. In this regard we select two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson Kessel clustering (GKC). For clustering purpose various image features are extracted using the neighborhood information of pixels from the difference image (DI). To assign a pixel-pattern to either of the two groups (for changed and unchanged regions of the DI) maximum of the two membership-values (given by FCM and by GKC for the same pattern for the same cluster) is considered. It has been observed experimentally that the changesare detected more efficiently using the proposed ensemble-based procedure. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. Results are compared with those of existing stand-alone fuzzy clustering based techniques, Markov random field (MRF) & neural network based algorithms and found to be superior.

关键词： Remote sensing Clustering algorithms Satellites Shape change detection algorithms Neural networks Visualization

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