Article
Environmental Sciences
Faguang Chang, Dexin Li, Zhen Dong, Yang Huang, Zhihua He, Xing Chen
Summary: This paper focuses on the analysis and solution of elevation space-variant error in geosynchronous synthetic aperture radar (GEO SAR), proposing an imaging algorithm to improve the focusing depth. Simulation results with dot-matrix targets and distributed targets validate the effectiveness of the method.
Article
Remote Sensing
Holger Heisig, Jean-Luc Simmen
Summary: The study presents a highly automated photogrammetric workflow for orientation of archival aerial imagery, which has been successfully applied to process a complete coverage of AAI over Switzerland with satisfying accuracies. The proposed workflow is at least five times more efficient in terms of human working time compared to classical workflows, while requiring very moderate computational resources.
PFG-JOURNAL OF PHOTOGRAMMETRY REMOTE SENSING AND GEOINFORMATION SCIENCE
(2021)
Article
Environmental Sciences
Faguang Chang, Dexin Li, Zhen Dong, Yang Huang, Zhihua He
Summary: This paper focuses on compensating for elevation space-variant errors in geosynchronous synthetic aperture radar (GEO SAR) and utilizing the autofocus method for elevation inversion in complex scenes to improve imaging accuracy. The proposed block map-drift phase gradient autofocus (block-MD-PGA) algorithm effectively compensates for elevation spatial variant errors and demonstrates excellent results in simulations and accuracy in elevation inversion.
Article
Geochemistry & Geophysics
Jindong Yu, Ze Yu, Chunsheng Li
Summary: In this article, a novel imaging algorithm is proposed for the geosynchronous synthetic aperture radar (GEO SAR) system to overcome the difficulty of focusing on maneuvering ships. The algorithm utilizes a spatial filter constructed by convolutional neural network (CNN) to extract time-frequency features, and combines it with local generalized Radon-Fourier transform (GRFT) and phase gradient autofocus (PGA) to improve extraction accuracy. Validation results based on simulations, airborne experiments, and wave pool experiments demonstrate the excellent imaging performance of the proposed algorithm.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Han Nie, Zhitao Fu, Bo-Hui Tang, Ziqian Li, Sijing Chen, Leiguang Wang
Summary: A dual-generator translation network is proposed to fuse structure and texture features of SAR and optical images, with the introduction of frequency-domain and spatial-domain loss functions to reduce differences between pseudo-optical and real optical images. Extensive experiments show that the method achieves state-of-the-art performance in terms of matching accuracy and keypoints repeatability.
Article
Geochemistry & Geophysics
Yuanxin Ye, Chao Yang, Jiacheng Zhang, Jianwei Fan, Ruitao Feng, Yao Qin
Summary: This study proposes a robust matching method by using a multiscale masked structure feature representation. By extracting pixelwise gradient structure features on multiple scales of images and constructing a mask based on large contours, the proposed method significantly improves the matching performance.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Liang Zhou, Yuanxin Ye, Tengfeng Tang, Ke Nan, Yao Qin
Summary: This study employs deep learning techniques to enhance image structure features for improved matching between SAR and optical images, showing advantages over other methods in experimental results.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Han Zhang, Lin Lei, Weiping Ni, Tao Tang, Junzheng Wu, Deliang Xiang, Gangyao Kuang
Summary: This study proposes the use of a Siamese convolutional neural network (CNN) architecture to address the challenges in optical and SAR image matching. By learning pixelwise deep dense features, it balances the learning of high-level semantic information and low-level fine-grained information, resulting in more accurate and precise image matching.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Yang Liu, Hua Qi, Shiyong Peng
Summary: A optical and SAR image-matching method based on phase structure convolutional features is proposed in this study. By using the log-Gabor filter (LGF) to extract the multiorientation phase structure information (MoPSI) and the mutual correlation layer to generate the image pair similarity map, a multiscale fusion SiamUNet-7 (MSF SiamUNet-7) network is constructed to fully fuse the local texture information and the global structure information. Experimental results show that the proposed method achieves high-precision matching.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Ma Xiaorui, Zheng Changwen, Liang Yi
Summary: A new method for matching SAR remote sensing images from coarse to fine based on salient contour features has been proposed, utilizing improved FCM clustering image segmentation and LBP operator to achieve high accuracy and strong robustness.
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY
(2021)
Article
Environmental Sciences
Eva Huintjes, Tobias Sauter, Benjamin Schroeter, Fabien Maussion, Wei Yang, Jan Kropacek, Manfred Buchroithner, Dieter Scherer, Shichang Kang, Christoph Schneider
ARCTIC ANTARCTIC AND ALPINE RESEARCH
(2015)
Article
Geography, Physical
Vicky L. Haider, Jan Kropacek, Istvan Dunkl, Bianca Wagner, Hilmar von Eynatten
EARTH SURFACE PROCESSES AND LANDFORMS
(2015)
Article
Engineering, Geological
Zuzana Varilova, Jan Kropacek, Jiri Zvelebil, Martin St'astny, Vit Vilimek
Article
Meteorology & Atmospheric Sciences
Niklas Neckel, Jan Kropacek, Benjamin Schroeter, Dieter Scherer
Article
Environmental Sciences
Eija Parmes, Yrjo Rauste, Matthieu Molinier, Kaj Andersson, Lauri Seitsonen
Article
Environmental Sciences
Oleg Antropov, Yrjo Rauste, Tuomas Hame, Jaan Praks
Article
Remote Sensing
Pedro Rodriguez-Veiga, Shaun Quegan, Joao Carreiras, Henrik J. Persson, Johan E. S. Fransson, Agata Hoscilo, Dariusz Ziolkowski, Krzysztof Sterenczak, Sandra Lohberger, Matthias Staengel, Anna Berninger, Florian Siegert, Valerio Avitabile, Martin Herold, Stephane Mermoz, Alexandre Bouvet, Thuy Le Toan, Nuno Carvalhais, Maurizio Santoro, Oliver Cartus, Yrjo Rauste, Renaud Mathieu, Gregory P. Asner, Christian Thiel, Carsten Pathe, Chris Schmullius, Frank Martin Seifert, Kevin Tansey, Heiko Balzter
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2019)
Article
Remote Sensing
Mohammad Imangholiloo, Jussi Rasinmaki, Yrjo Rauste, Markus Holopainen
CANADIAN JOURNAL OF REMOTE SENSING
(2019)
Article
Environmental Sciences
Oleg Antropov, Yrjo Rauste, Jaan Praks, Frank Martin Seifert, Tuomas Hame
Summary: This study used dense time series of RADARSAT-2 data to detect and map selective logging operations in the tropical forest area of northern Republic of the Congo. By combining multitemporal change detection with spatial texture analysis, the research successfully differentiated disturbed forests from intact ones.
Article
Remote Sensing
Jukka Miettinen, Simon Carlier, Lauri Hame, Annikki Makela, Francesco Minunno, Juho Penttila, Jan Pisl, Jussi Rasinmaki, Yrjo Rauste, Lauri Seitsonen, Xianglin Tian, Tuomas Hame
Summary: Forest biomass and carbon monitoring are crucial for climate change mitigation. A cloud-based approach utilizing Sentinel-2 imagery and ecosystem modeling has been demonstrated to produce large area forest volume and primary production estimates accurately. This lays the foundation for further development of an operational large area forest monitoring system.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Engineering, Electrical & Electronic
Sanja Scepanovic, Oleg Antropov, Pekka Laurila, Yrjo Rauste, Vladimir Ignatenko, Jaan Praks
Summary: The study focuses on mapping fundamental LC classes using satellite imaging radar data and evaluates seven state-of-the-art semantic segmentation models. Results show solid performance of the models, with FC-DenseNet and SegNet identified as the top two models.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Shaojia Ge, Erkki Tomppo, Yrjo Rauste, Weimin Su, Hong Gu, Jaan Praks, Oleg Antropov
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Teemu Mutanen, Laura Sirro, Yrjo Rauste
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
(2016)
Proceedings Paper
Engineering, Electrical & Electronic
T. Hame, T. Mutanen, Y. Rauste, O. Antropov, M. Molinier, S. Quegan, E. Kantzas, A. Makela, F. Minunno, J. A. Benediktsson, N. Falco, K. Arnason, R. Storvold, J. Haarpaintner, V. Elsakov, J. Rasinmaki
2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
(2015)
Article
Geosciences, Multidisciplinary
J. Kropacek, N. Neckel, B. Tyrna, N. Holzer, A. Hovden, N. Gourmelen, C. Schneider, M. Buchroithner, V. Hochschild
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
(2015)
