Article
Environmental Sciences
Ruonan Zhao, Zhabko Andrey Viktorovich, Junfeng Li, Chuang Chen, Meinan Zheng
Summary: This paper presents a strategy for extracting three-dimensional mining deformation using single-geometry SAR data. The methodology includes modeling the relationship between horizontal displacement and subsidence gradient and proposing a solution strategy to improve stability. The proposed method allows for the reconstruction of 3D displacements in mining areas using various types of SAR data. The effectiveness of the method is validated through simulation and in-site data, showcasing its applicability in mining deformation monitoring.
Article
Geography, Physical
Fengming Hu, Feng Wang, Yexian Ren, Feng Xu, Xiaolan Qiu, Chibiao Ding, Yaqiu Jin
Summary: This paper investigates the error sources of the array-InSAR interferograms and proposes a hybrid 3D phase unwrapping approach for 3D reconstruction. A hypothesis test is developed to identify the phase ambiguity and three indicators are proposed to identify reliable arcs. The L-1 norm approach is used to detect unwrapping errors and a two-tie network strategy is employed for global optimization. Experimental results demonstrate that the proposed algorithm can eliminate errors and provide a viable solution for rapid 3D SAR imaging.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Hossein Aghababaei, Giampaolo Ferraioli, Sergio Vitale, Roghayeh Zamani, Gilda Schirinzi, Vito Pascazio
Summary: A model-free despeckling framework is proposed in this article, which utilizes similarity distribution and importance to effectively denoise various SAR images while preserving structures and textures.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Francescopaolo Sica, Sofie Bretzke, Andrea Pulella, Jose-Luis Bueso-Bello, Michele Martone, Pau Prats-Iraola, Maria-Jose Gonzalez-Bonilla, Michael Schmitt, Paola Rizzoli
Summary: Decorrelation phenomena are always present in synthetic aperture radar interferometry, and can provide valuable information about imaged targets. This letter investigates InSAR decorrelation effects at the X-band using data from the TanDEM-X and PAZ spaceborne missions, showcasing the potential of combining bistatic and repeat-pass InSAR acquisitions.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Geochemistry & Geophysics
Yuanhao Li, Yiran Zhang, Zhiyang Chen, Tingting Fu, Xingzhe Zhao, Cheng Hu, Andrea Virgilio Monti-Guarnieri
Summary: This article proposes a spatial-temporal joint particle filter-based method for DEM generation by distributed GEO SAR. The method is validated under several atmospheric conditions and obtains high-accuracy and high-resolution DEMs. It also shows good robustness in different conditions.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Fengming Hu, Fengli Xue, Feng Xu
Summary: This article introduces a tri-beam SAR system capable of detecting 3-D deformation and deriving optimal parameters from varying squint and incident angles. The study shows that tri-beam SAR can measure 3-D deformation and reconstruct 3-D surface model without the need for ground control points.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Richard Czikhardt, Hans van der Marel, Juraj Papco
Summary: This paper introduces a FOSS Python toolbox for analyzing SAR time series of artificial radar reflectors, which can be used to estimate clutter level, Radar Cross Section, Signal-to-Clutter Ratio, and InSAR displacement time series. The toolbox is applied to analyze a network of 23 corner reflectors for landslide monitoring in Slovakia.
Article
Geochemistry & Geophysics
Wei Xu, Weida Xing, Chonghua Fang, Pingping Huang, Weixian Tan
Summary: The article proposes a modified two-step notch filtering approach combined with linear prediction to improve the SAR image quality. This method effectively mitigates narrowband RFI energy while recovering missing range spectral components.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Geochemistry & Geophysics
Xiang Ding, Jian Kang, Zhe Zhang, Yan Huang, Jialin Liu, Naoto Yokoya
Summary: In this article, a novel convolutional sparse coding method called CoComCSC is proposed for interferometric phase restoration in SAR images. CoComCSC effectively reduces noise and preserves phase details, demonstrating superior performance compared to state-of-the-art methods. The study also indicates CoComCSC's potential for generating high-resolution DEM products.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Pasquale Imperatore, Eugenio Sansosti
Summary: This paper investigates the parallel processing of SAR image geometric coregistration using shared-memory architectures, proposing an efficient and scalable processor with thread-level parallelism. Experimental results demonstrate the potential operational use of the developed parallel processor in large-scale interferometric SAR data processing.
Article
Geochemistry & Geophysics
Hanwen Yu, Ning Cao, Yang Lan, Mengdao Xing
Summary: This article proposes a multisystem interferometric data fusion framework, TSDFF, which combines data from different SAR systems to enhance SAR performance. TSDFF allows datasets from different SAR sensors to help each other, thus expanding the application scope of each sensor.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Review
Environmental Sciences
Kazuo Ouchi, Takero Yoshida
Summary: In 1978, the SEASAT satellite launched the first civilian synthetic aperture radar (SAR) for monitoring oceans and studying land applications. Despite its short operational time, SEASAT-SAR provided valuable information on land and sea, paving the way for future spaceborne SAR programs and new technologies such as InSAR and PolSAR. This article reviews the imaging processes and analyses of oceanic data using SAR, InSAR, PolSAR, and AI, covering various phenomena including ocean waves, oil slicks, ship detection, and sea ice.
Article
Engineering, Electrical & Electronic
Hao Feng, Lu Zhang, Jie Dong, Sihui Li, Qixuan Zhao, Jiayin Luo, Mingsheng Liao
Summary: This study maps and analyzes a flood event in the Taiyuan basin in north China in early October 2021 using multitemporal synthetic aperture radar (SAR) images. The flood event process is characterized using a spatiotemporal filter for despeckling the SAR images and classic change indicators. The joint analysis of SAR change detection results and interferometric SAR measurements suggests that land subsidence may contribute to the flood event.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Manfred Zink, Alberto Moreira, Irena Hajnsek, Paola Rizzoli, Markus Bachmann, Ralph Kahle, Thomas Fritz, Martin Huber, Gerhard Krieger, Marie Lachaise, Michele Martone, Edith Maurer, Birgit Wessel
Summary: The TanDEM-X mission, launched in 2010, aimed to provide a global Digital Elevation Model with unprecedented accuracy using a formation flying radar system. In addition to DEM data, TanDEM-X also has unique capabilities that support various scientific experiments. Despite the completion of most mission objectives, the mission continues to focus on monitoring ongoing changes in Earth's topography.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Wenfei Mao, Xiaowen Wang, Guoxiang Liu, Rui Zhang, Yueling Shi, Saied Pirasteh
Summary: The study introduces a new method, azimuth split-spectrum interferometry (AziSSI), to correct ionospheric errors in MAI measurements. By utilizing two subband MAI interferograms with different centroid frequencies, this approach successfully recovers coseismic ground displacements induced by the Wenchuan earthquake and overcomes azimuth stripes for the Chile case's MAI measurements.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geography, Physical
Xin-Yi Tong, Gui-Song Xia, Xiao Xiang Zhu
Summary: High-resolution satellite images are valuable for land cover classification, but their application in detailed mapping at large scale is limited. To address this, we present a large-scale land cover dataset called Five-Billion-Pixels, with over 5 billion labeled pixels from 150 high-resolution Gaofen-2 satellite images. We also propose a deep-learning-based unsupervised domain adaptation approach for large-scale land cover mapping. Experimental results show promising performance even with entirely unlabeled images.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Article
Geography, Physical
Wei Huang, Yilei Shi, Zhitong Xiong, Qi Wang, Xiao Xiang Zhu
Summary: RS image scene classification has gained attention for its applications. Conventional supervised approaches require labeled data, but with more RS images available, utilizing unlabeled data becomes urgent. This paper proposes a SSDA method called BSCA for RS cross-domain scene classification, using unsupervised and supervised alignment strategies to reduce domain shift.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Kalifou Rene Traore, Andres Camero, Xiao Xiang Zhu
Summary: This study proposes a data-driven technique to initialize a population-based neural architecture search algorithm. The technique involves calibrated clustering analysis and extracting centroids for initialization. Experimental results show that compared to random and Latin hypercube sampling, this technique significantly improves performance in certain search scenarios and can be applied to improve search on other datasets.
ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Rodrigo Capobianco Guido, Tulay Adali, Emil Bjoernson, Laure Blanc-Feraud, Ulisses Braga-Neto, Behnaz Ghoraani, Christian Jutten, Alle-Jan Van der Veen, Hong Vicky Zhao, Xiaoxing Zhu
Summary: The IEEE Signal Processing Society has provided 75 years of service to the signal processing community, making significant contributions to technological advancement.
IEEE SIGNAL PROCESSING MAGAZINE
(2023)
Article
Geography, Physical
Jianhua Guo, Qingsong Xu, Yue Zeng, Zhiheng Liu, Xiao Xiang Zhu
Summary: Urban tree canopy maps are crucial for providing urban ecosystem services. This study developed a semi-supervised deep learning method to robustly segment urban trees from high-resolution remote sensing images in order to better serve Brazil's urban ecosystem. The results showed that the urban tree canopy coverage in Brazil ranges from 5% to 35%, with an average coverage of approximately 18.68%. These canopy maps quantified the nationwide urban tree canopy inequality problem in Brazil. It is expected that these maps will encourage research on Brazilian urban ecosystem services, support urban development, and improve inhabitants' quality of life to achieve the Agenda for Sustainable Development goals.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Jakob Gawlikowski, Sudipan Saha, Julia Niebling, Xiao Xiang Zhu
Summary: This paper proposes a method to incorporate source-wise out-of-distribution (OOD) detection into the fusion process of SAR and optical satellite data, aiming to improve the robustness to different types of OOD data and maintain the classification performance. The method adjusts the weights of extracted information based on the in-distribution probabilities, and shows significant improvement in handling individual data source failures or cloud coverage.
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
(2023)
Article
Remote Sensing
Xiangyu Zhao, Jingliang Hu, Lichao Mou, Zhitong Xiong, Xiao Xiang Zhu
Summary: This paper presents a deep transfer model with multiple sub-networks optimized by supervised loss and unsupervised loss. The model improves overall accuracy and average accuracy in climate zone classification. The proposed deep transfer network demonstrates outstanding performance.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Geochemistry & Geophysics
Fahong Zhang, Yilei Shi, Zhitong Xiong, Wei Huang, Xiao Xiang Zhu
Summary: This article proposes a self-training-based unsupervised domain adaptation method to tackle the domain shift problem in semantic segmentation. By exploiting feature-level relation among neighboring pixels, the method can regularize the prediction of the adapted model and outperform other UDA methods in public benchmarks.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Qian Song, Conrad M. M. Albrecht, Zhitong Xiong, Xiao Xiang Zhu
Summary: We propose a tree-level biomass estimation model using LiDAR data. Our model correlates tree height with biomass and demonstrates the Gaussian process regression model as a viable alternative to traditional biomass-height-diameter models. The model is validated with a dataset of 8342 samples covering seven global biomes. The study confirms a low relative error of below 1% for our model at stand-level (or plot-level).
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Remote Sensing
Qingyu Li, Lichao Mou, Yuansheng Hua, Yilei Shi, Sining Chen, Yao Sun, Xiao Xiang Zhu
Summary: Three-dimensional (3D) building structures play a vital role in understanding urban dynamics. Monocular remote sensing imagery is a cost-effective data source for large-scale building height retrieval. However, existing methods fail to consider the information of neighboring pixels belonging to the same building. Therefore, this study proposes a novel representation called 3D centripetal shifts, which incorporates both planar and vertical structures of buildings, and presents a robust solution named 3DCentripetalNet for building height retrieval.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Engineering, Electrical & Electronic
Lukas Kondmann, Sudipan Saha, Xiao Xiang Zhu
Summary: In this article, we explore the combination of unsupervised and supervised methods in a semisupervised framework to improve change detection performance. By using the unsupervised SiROC model to generate pseudolabels and selecting the most confident ones for pretraining different student models, we achieve robust results across various scenarios. The results show that pseudo-label pretraining produces significant performance gains, even when more labeled data is available. Moreover, the confidence selection of SiROC is effective and the performance gains are generalizable to unseen scenes.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Yaxiong Chen, Jinghao Huang, Lichao Mou, Pu Jin, Shengwu Xiong, Xiao Xiang Zhu
Summary: This article proposes a novel deep saliency smoothing hashing (DSSH) algorithm to learn effective hash codes for drone image retrieval by leveraging saliency capture mechanism, distribution smoothing term, global features, and local fine-grained features. Extensive experiments demonstrate that the DSSH algorithm can further improve retrieval performance compared with other deep hashing algorithms.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Qingsong Xu, Yilei Shi, Xin Yuan, Xiao Xiang Zhu
Summary: In this paper, a practical universal domain adaptation (UniDA) approach is proposed for remote sensing image scene classification, which requires no prior knowledge on the label sets. The proposed UniDA method distinguishes the shared and private label sets in each domain to promote adaptation and successfully recognize unknown samples. Empirical results demonstrate the effectiveness and practicality of the proposed model for remote sensing image scene classification, regardless of the availability of source data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Remote Sensing
Qingyu Li, Sebastian Krapf, Yilei Shi, Xiao Xiang Zhu
Summary: Promoting solar technology can provide affordable, reliable, and modern energy for all people while reducing energy-related emissions and pollutants, contributing to sustainable development goals. Aerial imagery offers a cost-effective approach for large-scale rooftop solar potential analysis compared to other data sources.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Remote Sensing
Konrad Heidler, Lichao Mou, Di Hu, Pu Jin, Guangyao Li, Chuang Gan, Ji-Rong Wen, Xiao Xiang Zhu
Summary: Many deep learning approaches rely on pretrained backbone networks from large datasets, but the lack of such datasets in remote sensing is a challenge. We propose a label-free, self-supervised approach using imagery and audio correspondence to pretrain deep neural networks in remote sensing. We introduce the SoundingEarth dataset containing aerial imagery and crowd-sourced audio samples, and use it to pretrain ResNet models. Our approach outperforms existing pretraining strategies for remote sensing imagery. The dataset, code, and pretrained model weights are available at https://github.com/khdlr/SoundingEarth.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
