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Domain Generalized Object Detection for remote sensing images

Domain Generalized Object Detection for Remote Sensing Image...

引用

IEEE signal processing and Communications Applications (SIU)

作者： Efkan Duraklı Erchan Aptoula Computer Engineering Department Gebze Technical University Kocaeli Turkey Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey

Building roof type detection from remotely sensed images is a crucial task for many remote sensing applications, including urban planning and disaster management. In recent years, deep learning-based object detection approaches have demonstrated outstanding performance in this field. However, most of these approaches assume that the training and testing data are sampled from the same distribution. When there are differences between the distributions of training and test data, known as domain shift, the performance significantly degrades. In this paper, we proposed a domain generalization method to address domain shift at the instance and image level for roof type detection from remote sensing images. Furthermore, we evaluated our proposed method with IEEE Data Fusion Contest 2023 dataset. The proposed approach is the first of its kind in terms of domain generalization for remote sensing object detection.

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Research and Simulation Analysis of Factors Affecting Optical Satellite Imaging Quality 12th

Research and Simulation Analysis of Factors Affecting Optica...

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12th International conference on signal and Information processing, Networking and Computers, ICSINC 2024

作者： Zuo, Bo Yin, Ke Deng, Lilan Institute of Remote Sensing Satellite CAST Beijing100094 China Economic and Technology Research Institute of State Grid Beijing Electric Power Company Beijing100055 China

ISBN: (纸本)9789819649662

With the continuous development of optical satellite imaging technology, improving satellite imaging quality has become an urgent issue. This paper focuses on optical remote sensing satellites, conducting research and analysis on the relationship between satellite imaging chains and the imaging quality of remote sensing images, utilizing a satellite image simulation platform, delves into the impact of various factors on imaging quality. The research results indicate that solar illumination angle and atmospheric visibility are critical factors affecting the brightness and contrast of satellite images. Additionally, the satellite’s attitude motion in the roll and pitch directions significantly influences the geometric distortion of the images. These research results provide valuable insights into the subsequent image processing processes, which can help develop optimization strategies and improve the quality of satellite imaging, thus advancing the development of related fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion analysis

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SAR Self-Enhanced by Electro-optical Network (SARSEEN) 31

SAR Self-Enhanced by Electro-optical Network (SARSEEN)

引用

conference on signal processing, Sensor/Information Fusion, and Target Recognition XXXI

作者： Tran, Kenneth Sakla, Wesam Krim, Hamid North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (数字)9781510651210

ISBN: (纸本)9781510651210;9781510651203

In this work, we investigate the relationship between paired synthetic aperture radar (SAR) and optical images. SAR sensors have the capabilities of penetrating clouds and capturing data at night, whereas optical sensors cannot. Due to this, we are particularly interested in the case where we have access to both modalities during training, but only the SAR during test time. Our goal is to improve the performance of SAR only models by learning a self-generated prior about optical imagery. To that end, we developed a framework that consists of a deep neural network that inputs a SAR image and predicts a Canny edge map of the optical image. The Canny edge map retains structural information about the optical image, while removing superfluous high frequency details that is hard to generate from the SAR image. For downstream tasks, we concatenate the predicted edge map to the original SAR image and use that as the input. Our experiments show that by using this predicted edge map as additional information, we can outperform the same model that is only given the SAR image.

关键词： S AR multimodal fusion remote sensing

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Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for remote sensing image Classification 29

Hierarchical Probabilistic Graphical Models and Deep Convolu...

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29th European signal processing conference (EUSIPCO)

作者： Pastorino, Martina Moser, Gabriele Serpico, Sebastiano B. Zerubia, Josiane Univ Genoa DITEN Dept Genoa Italy Univ Cote dAzur INRIA Sophia Antipolis France

ISBN: (纸本)9789082797060

The method presented in this paper for semantic segmentation of multiresolution remote sensing images involves convolutional neural networks (CNNs), in particular fully convolutional networks (FCNs), and hierarchical probabilistic graphical models (PGMs). These approaches are combined to overcome the limitations in classification accuracy of CNNs for small or non-exhaustive ground truth (GT) datasets. Hierarchical PGMs, e.g., hierarchical Markov random fields (MRFs), are structured output learning models that exploit information contained at different image scales. This perfectly matches the intrinsically multiscale behavior of the processes of a CNN (e.g., pooling layers). The framework consists of a hierarchical MRF on a quadtree and a planar Markov model on each layer, modeling the interactions among pixels and accounting for both the multiscale and the spatial-contextual information. The marginal posterior mode criterion is used for inference. The adopted FCN is the U-Net and the experimental validation is conducted on the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, with some modifications to approach the case of scarce GTs and to assess the classification accuracy of the proposed technique. The proposed framework attains a higher recall compared to the considered FCNs, progressively more relevant as the training set is further from the ideal case of exhaustive GTs.

关键词： remote sensing semantic segmentation multiresolution hierarchical PGM CNN

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Weak Energy Interference Suppression in InSAR image Using Semantic Segmentation Network

Weak Energy Interference Suppression in InSAR Image Using Se...

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IET International Radar conference 2023, IRC 2023

作者： Li, Jiawang Gong, Yanyun Tang, Chuheng Tao, Mingliang Su, Jia Northwestern Polytechnical University Xi'an710072 China Shanghai Institute of Satellite Engineering Shanghai201109 China

ISBN: (纸本)9781839539954

Spaceborne interferometric synthetic aperture radar (InSAR) satellite, serves multiple industries, including land management, earthquake analysis, mapping, environmental monitoring, disaster reduction, and forestry. However, the presence of radio frequency interference (RFI) makes it difficult to obtain high-quality digital elevation model (DEM) and to perform deformation monitoring. In this paper, a RFI suppression model based on Residual Attention UNet (RA-UNet) is proposed for the Lutan-1 ground processing system, which is capable of processing massive images quickly and efficiently. The model combines attention mechanism and residual block to quickly focus on the changes in the local interference region, and uses UNet to extract the interference features in the spectrum to distinguish the interference. Under different RFI conditions, a wide range of experimental results are provided in different scenarios of the real measured data of Lutan-1, including single-pass and repeat-pass modes. The experimental results demonstrate the effectiveness, efficiency and robustness of the proposed method in interference suppression. © The Institution of Engineering & Technology 2023.

关键词： Semantic Segmentation

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Stripe Noise Removal from remote sensing images 6

Stripe Noise Removal from Remote Sensing Images

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6th IEEE International conference on signal processing, Computing and Control, ISPCC 2021

作者： Satya, P.Mohana Jagadish, Samudrala Satyanarayana, V. Singh, Mahesh K Aditya Engineering College Department Of ECE Surampalem India New Delhi India

ISBN: (纸本)9781665425520

remote sensing images are in many domains used, including geographic, military, urban planning and environmental surveillance, but they are somewhat limiting their application due of additional stripe noise. Clear images from stripe pictures may be easily predicted in most existing stream noise reduction algorithms without considering the underlying characteristics of strip noise that cause the structure to be destroyed. Thus a new strategy was suggested in this study from the point of view of the image breakdown. The inherent qualities of strip noise and image properties are taken into consideration. The suggested methodology combines regularization, group regulation and television regularization in a framework for picture decomposition, into a (TV). The first two terms are used to execute stripe noise qualities through statistical analyses and regularization of the TV should evaluate the portions of the smooth structures of the stripe-free image. In addition, an effective alternating minimization methodology is proposed to solve the picture decomposition model. © 2021 IEEE.

关键词： remote sensing

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remote sensing Satellite image Analysis for Deforestation in Yavatmal District, Maharashtra, India 3

Remote Sensing Satellite Image Analysis for Deforestation in...

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3rd International conference on signal processing and Communication (ICPSC)

作者： Nanare, Indersheel Kaur H. S. Bhoyar, Dinesh B. Balamwar, Sanjay V. Yeshwantrao Chavan Coll Engn Dept Elect & Telecommun Engn Nagpur Maharashtra India Applicat Ctr Maharashtra Remote Sensing Nagpur Maharashtra India

ISBN: (纸本)9781665428644

Forests are the most important and essential natural resources used by mankind for various usage. Loss of these forests has emerged as a major environmental concern in recent years. But for the purpose of development, many forests are being cut resulting in deforestation. Due to this cutting of forests, deforestation rate is increasing day by day and the world is facing severe disasters. Therefore, this continuous clearing of forests has a major degraded impact on the soil quality, climate change, hydrological cycle and ecosystem etc. This paper suggests suitable remote sensing techniques which can be used for the analyses of planet scope 3m resolution satellite data of Yavatmal district of Maharashtra state. remote sensing techniques along with the GIS i.e. geographic information system are used to detect the changes in forest cover of Yavatmal district. For recognition of forest cover, layer stacking of bands 3 and 4 by using Erdas Imagine software is done. For forest classification, the normalized difference vegetation index (NDVI) is experimented. The NDVI values reveal the change of forest cover and various other categories resulting in increase and decrease of categories. It is concluded that out of total forest area (76740.32 hectare), decreased category shows 29.44% due to forest cutting, forest fires and leaf shedding etc.

关键词： remote sensing deforestation forest cover enhancement thresholding Normalized difference vegetation index(NDVI)

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An Unsupervised Domain Adversarial Wavelet Learning Method for registration of Optical and SAR Heterogeneous remote sensing images

An Unsupervised Domain Adversarial Wavelet Learning Method f...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Hongze Ye Shenfu Zhang Liangzhi Li Xiangchao Meng School of Information Science and Engineering Ningbo University Ningbo China College of Geological Engineering and Geomatics Chang’an University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Optical images and synthetic aperture radar (SAR) remote sensing images are highly complementary information for Earth observation and applications. Accurate registration of these two data types is essential for subsequent collaboration processing and analysis. However, traditional methods struggle to achieve effective registration due to nonlinear radiometric disparities and geometric distortions. Meanwhile, supervised deep learning approaches require costly manual annotations. To address these issues, this paper proposes an unsupervised registration method based on domain adversarial wavelet learning. We innovatively design an inter-domain adversarial mutual transfer (IAT) module to alleviate cross-modal differences while preserving structural features. Additionally, wavelet transform is employed at different scales for feature matching, to mitigate radiometric disparities and enhance feature recognizability. By incorporating an attention mechanism, the network structure is optimized through spatial alignment and image similarity. The proposed method outperforms other state-of-the-art methods in both quantitative and qualitative aspects of benchmark datasets.

关键词： Optical filters Wavelet transforms Wavelet domain Optical distortion Radiometry Optical imaging Optical sensors Nonlinear optics Synthetic aperture radar remote sensing

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Physically Plausible Spectral Reconstruction in remote sensing Using Multispectral image

Physically Plausible Spectral Reconstruction in Remote Sensi...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Wenbin Chen Xiyang Zhi Yuanxin Huang Zhipeng Wang Zheng Lu Wei Zhang Research Center for Space Optical Engineering Harbin Institute of Technology Harbin China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Hyperspectral imaging technology offers significant advantages in remote sensing, capturing richer and more detailed spectral information in narrower bands compared to multispectral images. This makes it highly effective for land cover classification, crop monitoring, and environmental monitoring. However, the high cost, low spatial resolution, and complexity of data acquisition limit its widespread application. This study proposes a novel and flexible framework for spectral reconstruction designed to overcome these challenges. Our framework incorporates radiometric calibration, atmospheric correction, classification, spectral unmixing, and thermal infrared super-resolution, all of which can be replaced with the latest high-performance algorithms. Additionally, the physically plausible framework considers actual optical transmission processes, ensuring accurate representation of both reflective and radiative properties. This adaptable approach facilitates cost-effective and scalable acquisition of hyperspectral data. Experimental results demonstrate the accuracy and effectiveness of the proposed method, with reconstructed images showing high consistency with measured data, both in spectral curve shapes and radiance levels, underscoring its significant application potential.

关键词： Accuracy Superresolution Land surface Radiometry Optical imaging Optical sensors Optical reflection Spatial resolution image reconstruction Hyperspectral imaging

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The Discrete System of Fibonacci Functions for the Construction of a Global Automated System for Monitoring Objects Littering the Environment Using remote sensing 27

The Discrete System of Fibonacci Functions for the Construct...

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conference on image and signal processing for remote sensing XXVII

作者： Kazaryan, M. Semenishchev, E. Voronin, V. North Ossetian State Med Acad Vladikavkaz Russia Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

The article explores the detection of unauthorized landfills based on remote sensing data. We have developed a mathematical approach that translates ordinary images to the 2-D manifold or 3-D manifolds in stereo images. At the same time, the posed task when developing algorithms is the transformation (convolution) of these manifolds into a one-dimensional sample. The developed method corresponds to the following two conditions: 1) preservation of the topological proximity of the elements of the original and expanded spaces, 2) preservation of correlations between the elements of the original and transformed spaces. The basic idea that underlies the mathematical support of the developed automated system is the concept of fractal sets. The concept of continuous orthogonal transformations, including the Fibonacci transform, is used as a mathematical basis for determining anomalous signal structures in the surrounding background. The problem of monitoring and decoding space images from the point of view of the synthesis of orthogonal systems with predetermined properties (speed of calculations, order of transformation, etc.) is presented. Examples of processing by the proposed algorithm are presented on the satellite images of the Moscow Region territories of the Russian Federation.

关键词： space image of waste disposal facility landfill chemical process waste degradation fractals discrete orthogonal transformations Fibonacci transformations

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