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Comparison between high and coarse spatial resolution surface soil moisture products over two Mediterranean sites in Tunisia and France 7

Comparison between high and coarse spatial resolution surfac...

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IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Ayari, Emna Baghdadi, Nicolas Rodriguez-Fernandez, Nemesio J. Albergel, Clement Stradiotti, Pietro Lili-Chabaane, Zohra Zribi, Mehrez UT3 Paul Sabatier CESBIO CNES CNRSINRAEIRD Toulouse France Univ Montpellier AgroParisTech CIRAD CNRSINRAETETIS Montpellier France ECSAT European Space Agcy Climate Off Harwell Campus Didcot OX11 0FD Oxon England Vienna Univ Technol Climate & Environm Remote Sensing Grp Dept Geodesy & Geoinformat Vienna Austria Spatial Anal & Modeling GREEN TEAM Agron Inst Tunisia LR17AGR01 InteGRatEd Management Nat Resources Rem Tunis Tunisia

ISBN: (纸本)9798350351491;9798350351484

The surface soil moisture (SSM) is an essential component in hydrologic modelling. Therefore, an accurate retrieval of SSM with high repetitiveness in time and space will improve the hydrologic performance. In the present paper, we compare between 1-km SSM product available every 6 days (HRSM) with operational SSM products namely Soil Moisture Active and Passive (SMAP) and ESA CCI combined (CCI) characterized with coarse spatial resolution and high revisit time up between 1 and 3 days over two Mediterranean sites in Tunisia and Occitanie in France. The analyses of SSM time series are performed as function of SSM product date in first step and monthly in a second step. The results revealed that HRSM time series, over the two sites, are in coherence with SMAP and CCI time series and marked by seasonality from one year to another (2017-2021). Over the Occitanie site, the comparison between HRSM monthly averages and the SMAP and CCI averages is characterized by an RMSE values varying between 5 and 9.8 vol.%, respectively. The RMSE values decreased, over Tunisia, and reached a maximum of 5 vol.%. The aforementioned findings underlined the potential of 1-km product to be included in future applications such as drought identification.

关键词： surface soil moisture microwave Sentinel-1 Sentinel-2 SMAP ESA CCI drought Tunisia France

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Slow Scan Television (SSTV) Method for Transmitting images Using HT and SDR as Receivers

Slow Scan Television (SSTV) Method for Transmitting Images U...

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2024 International conference on Aerospace Electronics and remote sensing Technology

作者： Maharani, Eliana Pratiwi, Amelia Chandra Zega, De Shepherd Guella Winisia Aprilliyani, Ria Ariteja, Dananjaya Dindin, Imanuel Republ Indonesia Def Univ Mil Elect Engn Bogor Indonesia Republ Indonesia Def Univ Mil Mech Engn Bogor Indonesia Republ Indonesia Def Univ Fac Def Sci & Technol Bogor Indonesia

ISBN: (纸本)9798331542023;9798331542016

In this paper, the performance of Slow Scan Television (SSTV) image transmission was investigated using two types of receivers: Handheld Transceiver (HT) and Software-Defined Radio (SDR). The study aimed to compare the effectiveness of these receivers based on image quality metrics, including Peak signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Root Mean Square Error (RMSE), at varying distances of 50 m, 100 m, and 200 m. Experimental results demonstrated that SDR consistently outperforms HT, particularly at 50 meters, where SDR achieved the highest PSNR (28.452), the lowest MSE (92.872) and the lowest RMSE (9.634), indicating superior image reception quality. These findings suggested that SDR offers enhanced capabilities for image transmission over short distances, making it a preferable choice in applications such as amateur radio and emergency communications. This research provided valuable insights into optimizing SSTV transmissions and highlighted SDR's adaptability for various communication scenarios.

关键词： SSTV SDR HT image processing communication

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SUPERVISING remote sensing CHANGE DETECTION MODELS WITH 3D SURFACE SEMANTICS 29

SUPERVISING REMOTE SENSING CHANGE DETECTION MODELS WITH 3D S...

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IEEE International conference on image processing (ICIP)

作者： Corley, Isaac Najafirad, Peyman Univ Texas San Antonio Secure Artificial Intelligence Lab Auton AILA San Antonio TX USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications. Recent advances in multimodal self-supervised pretraining have resulted in state-of-the-art methods which surpass vision models trained solely on optical imagery. In the remote sensing field, there is a wealth of overlapping 2D and 3D modalities which can be exploited to supervise representation learning in vision models. In this paper we propose Contrastive Surface-image Pretraining (CSIP) for joint learning using optical RGB and above ground level (AGL) map pairs. We then evaluate these pretrained models on several building segmentation and change detection datasets to show that our method does, in fact, extract features relevant to downstream applications where natural and artificial surface information is relevant.(1)

关键词： self-supervised learning contrastive learning remote sensing above ground level maps

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2024 IEEE International conference on image processing, ICIP 2024 - Proceedings

2024 IEEE International Conference on Image Processing, ICIP...

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31st IEEE International conference on image processing, ICIP 2024

ISBN: (纸本)9798350349399

The proceedings contain 593 papers. The topics discussed include: MDBFUSION: a visible and infrared image fusion framework capable for motion deblurring;prune channel and distill: discriminative knowledge distillation for semantic segmentation;imbalanced data robust online continual learning based on evolving class aware memory selection and built-in contrastive representation learning;privacy-preserving visual cues communication for hearing-impaired people using deep learning;transformer-based clipped contrastive quantization learning for unsupervised image retrieval;attention enhancement with parallel groups for remote sensing object detection;cross-domain few-shot in-context learning for enhancing traffic sign recognition;and recurrent 3-D multi-level visual transformer for joint classification of heterogeneous 2-D and 3-D radiographic data.

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Prompting DirectSAM for Semantic Contour Extraction in remote sensing images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Miao, Shiyu Chen, Delong Liu, Fan Zhang, Chuanyi Gu, Yanhui Guo, Shengjie Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University QLD Australia

ISBN: (纸本)9798350368741

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction-such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks. © 2025 IEEE.

关键词： remote sensing Semantic contour extraction Vision-language learning

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BOUNDARY-AWARE BIAS LOSS FOR TRANSFORMER-BASED AERIAL image SEGMENTATION MODEL 47

BOUNDARY-AWARE BIAS LOSS FOR TRANSFORMER-BASED AERIAL IMAGE ...

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47th IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Zhang, Yan Jiang, Xue Liu, Siqi Hu, Bo Gao, Xinbo Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China

ISBN: (纸本)9781665405409

Inspired by the tremendous success of the transformer-based model in natural language processing (NLP), many efforts introduce the transformer-based model into the image processing tasks. However, naive transformer models have to downsample the image resolution to satisfy computational restrictions, thus discarding the local information, which is catastrophic for high-performance remote sensing image segmentation. Hence, this paper proposes a novel trainable boundaryaware bias loss function to enhance transformer-based models of extracting local information. On the Challenging ISPRS Potsdam dataset, two representative transformer-based models achieve remarkable performance improvements, proving the effectiveness of the proposed method.

关键词： Aerial image Segmentation Loss Function Transformer Deep Learning remote sensing

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FREQUENCY-SPATIAL DOMAIN INFORMATION FUSION NETWORK FOR PAN-SHARPENING 31

FREQUENCY-SPATIAL DOMAIN INFORMATION FUSION NETWORK FOR PAN-...

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2024 International conference on image processing

作者： Zhao, Mengjiao Ma, Mengting Gao, Ao Zhang, Wei Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Zhejiang Univ Innovat Ctr Yangtze River Delta Jiaxing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Pan-sharpening aims to fuse panchromatic (PAN) images with low-resolution multi-spectral (LR-MS) images to generate high-resolution multi-spectral (HR-MS) images. Despite the impressive performance of existing learning-based methods, they are constrained by coarse fusion strategies in frequency or spatial domain. In this paper, we discover that PAN images can provide all the spatial textures required for HR-MS images, while spectral information must be provided jointly by PAN and LR-MS images. Inspired by this, we propose a noval frequency-spatial domain information fusion network for pan-sharpening, called FSDNet. Specifically, we design a Dual-Domain Information processing Module (DDPM) to construct FSDNet. It consists of a Frequency Domain Feature processing Block (FDB), a Spatial Domain Information processing Block (SDB), and an Information Fusion Block (IFB). The FDB in the frequency domain uses the Adaptive Amplitude Fusion Block (AAFB) and convolution layers to finely modulate amplitude and phase components, exploring global information. The SDB uses cascaded residual blocks to capture and enhance local information in the spatial domain. The IFB based on invertible neural networks (INNs) introduces Multi-Scale Self-Attention Block (MSAB), achieves effective information fusion and reduce information loss. Extensive experiments on the QuickBird and GaoFen-2 datasets demonstrate the effectiveness and superiority of our method.

关键词： Pan-sharpening remote sensing

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RJB-Net: Residual Deep Learning with Joint Bilateral Denoising Network for remote sensing image Fusion 3

RJB-Net: Residual Deep Learning with Joint Bilateral Denoisi...

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3rd IEEE Asian conference on Innovation in Technology, ASIANCON 2023

作者： Patel, Krupa Misra, Indranil Patel, Narendra Moorthi, S. Manthira Dhar, Debajyoti Birla Vishvakarma Mahavidyalaya Engineering College Dept. of Computer Science and Engineering Anand India Signal and Image Processing Group Ahmedabad India

ISBN: (纸本)9798350302288

remote sensing image fusion amalgamate information from panchromatic and multispectral remote sensing images to generate an optimum representative image. In this paper, we have proposed a novel method for image fusion known as Residual Deep Learning with Joint Bilateral Denoising Network (RJB-Net). The RJB-Net procedure is initiated with panchromatic image denoising using a joint bilateral filter to suppress noise and retain edge information. RJB-Net model is trained at image patches covering all major spectral classes. The denoised panchromatic image along with multispectral data is input to the residual deep learning network for inference and generates a fused image. The remote sensing datasets used for RJB-Net training and evaluation are Indian Cartosat-1 panchromatic and Resourcesat-2 multispectral imagery. The experiment section compares the RJB-Net denoising output with prominent spatial domain image filtering methods both qualitatively and quantitatively. It is found that RJB-Net fused images inject the spatial information from panchromatic images in a controlled manner while retaining the spectra characteristics of multispectral data. The merging performance of RJB-Net is compared with state-of-the-art remote sensing image fusion techniques that range from classical to recent deep learning model. The proposed method RJB-Net generates superior results among all fusion methods at different feature targets and compared the performance both visually as well as with image quality metrics. © 2023 IEEE.

关键词： remote sensing

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Spatial information extraction of oil well sites based on medium-resolution satellite imagery 29

Spatial information extraction of oil well sites based on me...

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conference on image and signal processing for remote sensing XXix

作者： Wu, Hao Dong, Hongli Wang, Zhibao Bai, Lu Huo, Fengcai Tao, Jinhua Chen, Liangfu Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intellige Daqing Peoples R China Northeast Petr Univ Sch Comp & Informat Technol Daqing Peoples R China Northeast Petr Univ Bohai Rim Energy Res Inst Qinhuangdao Hebei Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781510666955;9781510666962

The oil extraction process has cumulative detrimental impacts on the environment. However, in the process of oil mining, a large number of petroleum-based pollutants cause severe effect to soil and groundwater, which poses a serious risk to the ecological environment and human health. Understanding the distribution of oil well sites, is of vital importance to sustainable mining development. Efficient mapping these sites require automated identification and extraction of the oil well sites from satellite images. With the development of remote sensing satellite technology and the wide application of deep learning-based algorithms, it has become possible to automatically extract oil well sites from remote sensing images. However, there is lack of usage of Sentinel-2 satellite data to explore the efficacy in oil well sites detection. Therefore, we conducted this work to explore the feasibility of detecting the oil well sites with semantic segmentation from Sentinel-2 imagery. In this work, we established the Northeast Petroleum University Oil Well Sites Version 2.0 (NEPU-OWS V2.0) with spatial coverage spanning the Austin region of United States. We then validate the usability and effectiveness of the dataset using semantic segmentation models based on DANet and Swin-Unet, which are more capable of recognising small targets. Our experimental results show that both models have great potential for remote sensing detection in the medium-sized oil well sites and the Swin-Unet model achieved a better performance for the detection of oil well sites with a MIoU of 77.53%.

关键词： Oil well sites Sentinel 2 semantic segmentation DANet Swin-Unet

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A class-driven hierarchical ResNet for classification of multispectral remote sensing images 29

A class-driven hierarchical ResNet for classification of mul...

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conference on image and signal processing for remote sensing XXix

作者： Weikmann, Giulio Perantoni, Gianmarco Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci Via Sommarive 9 I-38123 Trento Italy

ISBN: (纸本)9781510666955;9781510666962

This work presents a multitemporal class-driven hierarchical Residual Neural Network (ResNet) designed for modelling the classification of Time Series (TS) of multispectral images at different semantical class levels. The architecture consists of a modification of the ResNet where we introduce additional branches to perform the classification at the different hierarchy levels and leverage on hierarchy-penalty maps to discourage incoherent hierarchical transitions within the classification. In this way, we improve the discrimination capabilities of classes at different levels of semantic details and train a modular architecture that can be used as a backbone network for introducing new specific classes and additional tasks considering limited training samples available. We exploit the class-hierarchy labels to train efficiently the different layers of the architecture, allowing the first layers to train faster on the first levels of the hierarchy modeling general classes (i.e., the macro-classes) and the intermediate classes, while using the last ones to discriminate more specific classes (i.e., the micro-classes). In this way, the targets are constrained in following the hierarchy defined, improving the classification of classes at the most detailed level. The proposed modular network has intrinsic adaptation capability that can be obtained through fine tuning. The experimental results, obtained on two tiles of the Amazonian Forest on 12 monthly composites of Sentinel 2 images acquired during 2019, demonstrate the effectiveness of the hierarchical approach in both generalizing over different hierarchical levels and learning discriminant features for an accurate classification at the micro-class level on a new target area, with a better representation of the minoritarian classes.

关键词： land cover classification deep learning hierarchical classification Earth observation remote sensing

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