49 research outputs found

    INTEGRATED SHORELINE EXTRACTION APPROACH WITH USE OF RASAT MS AND SENTINEL-1A SAR IMAGES

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    Automatically extracted Antarctic coastline using remotely-sensed data: an update

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    The temporal and spatial variability of the Antarctic coastline is a clear indicator of change in extent and mass balance of ice sheets and shelves. In this study, the Canny edge detector was utilized to automatically extract high-resolution information of the Antarctic coastline for 2005, 2010, and 2017, based on optical and microwave satellite data. In order to improve the accuracy of the extracted coastlines, we developed the Canny algorithm by automatically calculating the local low and high thresholds via the intensity histogram of each image to derive thresholds to distinguish ice sheet from water. A visual comparison between extracted coastlines and mosaics from remote sensing images shows good agreement. In addition, comparing manually extracted coastline, based on prior knowledge, the accuracy of planimetric position of automated extraction is better than two pixels of Landsat images (30 m resolution). Our study shows that the percentage of deviation (7 km2 (2005) to 1.3537 Γ— 107 km2 (2010) and 1.3657 Γ— 107 km2 (2017). We have found that the decline of the Antarctic area between 2005 and 2010 is related to the breakup of some individual ice shelves, mainly in the Antarctic Peninsula and off East Antarctica. We present a detailed analysis of the temporal and spatial change of coastline and area change for the six ice shelves that exhibited the largest change in the last decade. The largest area change (a loss of 4836 km2) occurred at the Wilkins Ice Shelf between 2005 and 2010

    On the use of multipolarization satellite SAR data for coastline extraction in harsh coastal environments: the case of Solway Firth

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    This study deals with coastline extraction using multipolarization spaceborne synthetic aperture radar (SAR) imagery acquired over coastal intertidal areas. The latter are very challenging environments where mud flats lead to a large variability of normalized radar cross section, which may trigger a significant number of false edges during the extraction process. The performance of SAR-based coastline extraction methods that rely on a joint combination of multipolarization information (either single- or dual-polarization metrics) and speckle filtering (either local and nonlocal approaches) are analyzed using global positioning system (GPS) samples and colocated SAR imagery collected under different incidence angles. Our test site is an intertidal zone with a wetland (i.e., salt marsh) in the Solway Firth, south-west along the Scottish-English border. Experimental results, obtained processing a pair of RadarSAT-2 full-polarimetric and a pair of Sentinel-1 dual-polarimetric SAR imagery augmented by colocated GPS samples, show that: first, the multipolarization information outperforms the single-polarization counterpart in terms of extraction accuracy; second, among the single-polarization channels, the cross-polarized one performs best; third, both single- and dual-polarization methods perform better when nonlocal speckle filtering is applied; fourth, the joint combination of nonlocal speckle filter and dual-polarization information provides the best accuracy; and finally, the incidence angle plays a role in the extraction accuracy with larger incidence angles resulting in the best performance when dual-polarization metric is used

    SAR μ˜μƒμ„ ν™œμš©ν•œ ν™μˆ˜ λͺ¨λ‹ˆν„°λ§μ„ μœ„ν•œ 경계 κ°•ν™” μ λŒ€μ  ν•™μŠ΅ λ”₯λŸ¬λ‹ λͺ¨λΈ

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    ν•™μœ„λ…Όλ¬Έ(석사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : μžμ—°κ³Όν•™λŒ€ν•™ μ§€κ΅¬ν™˜κ²½κ³Όν•™λΆ€, 2023. 8. 김덕진.The necessity of real-time flood monitoring has been increasing as the frequency and intensity of water-related disasters increase. Synthetic Aperture Radar(SAR) could be particularly useful for a inundation mapping because it is able to penetrate clouds and provide images even during periods of darkness. Therefore, water segmentation using SAR has been actively researched, especially the advent of Convolutional Neural Networks(CNN) contributing to high overall accuracy. However, CNN is vulnerable to detecting precise boundaries and narrow rivers, which pose challenges for practical applications. In this study, we propose a boundary-driven adversarial learning approach of deep neural networks to detect waterbodies with precise borders and small rivers. We adopt the adversarial learning of Generative Adversarial Networks (GAN) to make a generator focus on pixels that could be easily ignored. A discriminator evaluates and distinguishes the segmented images with the ground truth labels by consulting the SAR images and the boundary distance map. The boundary distance map is designed to highlight the small area like the boundaries and streams and suppressing false positive errors.Moreover, we propose a hybrid loss function that guides the network to concentrate on both the overall and the fine details by fusing Binary Cross Entropy loss, Hausdorff distance loss and adversarial loss. Through adversarial training with the hybrid loss, the water segmentation model using SAR can precisely detect waterbodies. We demonstrate the effectiveness of the model using three evaluation metrics: F1-score, Boundary IoU, and Matthews Correlation Coefficient, and we also apply additional qualitative assessment. Our empirical evidence indicates that the proposed model outperforms other segmentation models like U-Net and DeepLabv3+, especially in terms of precise boundaries and narrow rivers. To assess the practical monitoring use, we demonstrate that the proposed model maintains precision with the large scene SAR images. Not only does it detect precise boundaries and narrow objects,but it also reduces false positive errors in large scene SAR images. The visual inspection further demonstrates that our model can detect narrow rivers and small reservoirs that are missed by other models, showcasing the potential of boundary-driven adversarial learning of deep neural networks in practical monitoring use.κΈ°ν›„λ³€ν™”κ°€ κ°€μ†ν™”λ‘œ 인해 μˆ˜μž¬ν•΄μ˜ λΉˆλ„μ™€ 강도 예츑이 μ–΄λ €μ›Œμ§μ— 따라 μ‹€μ‹œκ°„ ν™μˆ˜ λͺ¨λ‹ˆν„°λ§μ— λŒ€ν•œ μˆ˜μš”κ°€ μ¦κ°€ν•˜κ³  μžˆλ‹€. ν•©μ„±κ°œκ΅¬λ ˆμ΄λ‹€λŠ” 광원과 날씨에 λ¬΄κ΄€ν•˜κ²Œ μ§€μ†μ μœΌλ‘œ 촬영이 κ°€λŠ₯ν•œ λ ˆμ΄λ‹€λ‘œ, μˆ˜μž¬ν•΄κ°€ λ°œμƒν•˜μ˜€μ„ λ•Œμ—λ„ μ˜μƒμ„ μ œκ³΅ν•  수 μžˆλ‹€. 이에 ν•©μ„±κ°œκ΅¬λ ˆμ΄λ‹€λ₯Ό ν™œμš©ν•œ 수체 탐지 μ•Œκ³ λ¦¬μ¦˜ 개발이 ν™œλ°œνžˆ μ—°κ΅¬λ˜μ–΄ μ™”λ‹€. 특히 λ”₯λŸ¬λ‹μ˜ λ°œλ‹¬λ‘œ CNN을 ν™œμš©ν•œ 수체 탐지 μ•Œκ³ λ¦¬μ¦˜μ΄ 연ꡬ됨에 따라, 높은 μ •ν™•λ„λ‘œ 수체 탐지가 κΈ°λŠ₯ν•΄μ‘Œλ‹€. ν•˜μ§€λ§Œ, CNN 기반 수체 탐지 λͺ¨λΈμ€ ν›ˆλ ¨ μ‹œ 높은 μ •λŸ‰μ  μ •ν™•μ„± μ§€ν‘œλ₯Ό λ‹¬μ„±ν•˜μ—¬λ„ μΆ”λ‘  ν›„ 정성적 평가 μ‹œ 경계와 μ†Œν•˜μ²œμ— λŒ€ν•œ 정확성이 떨어진닀. ν™μˆ˜ λͺ¨λ‹ˆν„°λ§μ—μ„œ 특히 μ€‘μš”ν•œ 정보인 경계와 쒁은 ν•˜μ²œμ— λŒ€ν•΄μ„œ νƒμ§€μ˜ 정확성이 떨어짐에 따라 μ‹€μƒν™œ 적용이 μ–΄λ ΅λ‹€. 이에 μš°λ¦¬λŠ” 경계λ₯Ό κ°•ν™”ν•œ μ λŒ€μ  ν•™μŠ΅ 기반의 수체 탐지 λͺ¨λΈμ„ κ°œλ°œν•˜μ—¬ μ‰½κ²Œ νƒμ§€λ˜μ§€ μ•Šμ•˜λ˜ λΆ€λΆ„κΉŒμ§€ νƒμ§€ν•˜κ³ μž ν•œλ‹€. μ λŒ€μ  ν•™μŠ΅μ€ 생성적 μ λŒ€ 신경망(GAN)의 두 개의 λͺ¨λΈμΈ μƒμ„±μžμ™€ νŒλ³„μžκ°€ μ„œλ‘œ κ΄€μ—¬ν•˜λ©° 더 높은 정확도λ₯Ό 달성할 수 μžˆλ„λ‘ ν•™μŠ΅ν•˜λŠ” 과정을 μ˜λ―Έν•œλ‹€. νŒλ³„μžλŠ” μƒμ„±μžμ˜ μΆ”λ‘  결과와 μ‹€μ œ 라벨 데이터λ₯Ό κ΅¬λΆ„ν•˜κΈ° μœ„ν•΄ ν•™μŠ΅ν•˜λŠ” 반면, μƒμ„±μžλŠ” νŒλ³„μžλ₯Ό 속이기 μœ„ν•΄ 더 μ‹€μ œ 데이터 같은 κ°€μ§œ 데이터λ₯Ό μƒμ„±ν•˜κ³ μž λ…Έλ ₯ν•œλ‹€. μ΄λŸ¬ν•œ μ λŒ€μ  ν•™μŠ΅ κ°œλ…μ„ 수체 탐지 λͺ¨λΈμ— 처음으둜 λ„μž…ν•˜μ—¬, μƒμ„±μžλŠ” μ‹€μ œ 라벨 데이터와 μœ μ‚¬ν•˜κ²Œ 수체 경계와 μ†Œν•˜μ²œκΉŒμ§€ νƒμ§€ν•˜κ³ μž ν•™μŠ΅ν•œλ‹€. 반면 νŒλ³„μžλŠ” 경계 거리 λ³€ν™˜ 맡과 ν•©μ„±κ°œκ΅¬λ ˆμ΄λ‹€ μ˜μƒμ„ 기반으둜 라벨데이터와 수체 탐지 κ²°κ³Όλ₯Ό κ΅¬λΆ„ν•œλ‹€. μ΄λ•Œ 경계 거리 λ³€ν™˜ 맡은 μž‘μ€ ν•˜μ²œκ³Ό 경계에 κ°€μ€‘μΉ˜λ₯Ό μ€€ μ΄λ―Έμ§€λ‘œ, νŒλ³„μžλ‘œ ν•˜μ—¬κΈˆ νŒλ³„μ‹œ μž‘μ€ μ˜μ—­κΉŒμ§€ κ³ λ €ν•  수 μžˆλ„λ‘ κ°•μ‘°ν•˜λŠ” λ™μ‹œμ— μ˜€νƒμ§€μ— λŒ€ν•΄ μ–΅μ œν•  수 μžˆλŠ” 역할을 μœ„ν•΄ μ œμ•ˆν•˜μ˜€λ‹€. 경계가 κ°•μ‘°λœ λ°©ν–₯으둜 μ λŒ€μ  ν•™μŠ΅ 과정이 진행될 수 μžˆλ„λ‘, Binary Cross Entropy 손싀 ν•¨μˆ˜, Hausdorff distance 기반 손싀 ν•¨μˆ˜ 그리고 μ λŒ€μ  손싀 ν•¨μˆ˜λ₯Ό μœ΅ν•©ν•œ ν•˜μ΄λΈŒλ¦¬λ“œ 손싀 ν•¨μˆ˜λ₯Ό μƒˆλ‘­κ²Œ κ΅¬μ„±ν•˜μ˜€λ‹€. μ œμ•ˆ λͺ¨λΈμ΄ 경계와 μ†Œν•˜μ²œμ„ μ •ν™•νžˆ νƒμ§€ν•˜λŠ”μ§€ νŒλ‹¨ν•˜κΈ° μœ„ν•΄, μ •λŸ‰μ  μ§€ν‘œλ‘œ F1-score, Boundary IoU, Matthews Correlation Coefficientλ₯Ό μ‚¬μš©ν•˜μ˜€μœΌλ©°, μœ‘μ•ˆ νŒλ…μ„ 톡해 정성적 평가도 μ§„ν–‰ν•˜μ˜€λ‹€. 이λ₯Ό 톡해 μ œμ•ˆν•œ λͺ¨λΈμ΄ 경계 및 μ†Œν•˜μ²œκΉŒμ§€ μ •ν™•ν•˜κ²Œ 탐지해냄을 증λͺ…ν•˜μ˜€λ‹€. μ‹€μ œ ν™μˆ˜ 탐지에 μ‚¬μš©ν•˜κΈ° μœ„ν•΄μ„  패치 λ‹¨μœ„ 이미지가 μ•„λ‹Œ 전체 SAR μ˜μƒμ—μ„œλ„ 높은 정확도λ₯Ό μœ μ§€ν•˜λŠ”μ§€ 확인이 ν•„μš”ν•˜λ‹€. 이λ₯Ό μœ„ν•΄ 패치 λ‹¨μœ„λ‘œ ν•™μŠ΅λœ λͺ¨λΈμ΄ 전체 SAR μ˜μƒμ„ 탐지할 수 μžˆλ„λ‘ μΆ”κ°€ μ½”λ“œλ₯Ό κ°œλ°œν•˜μ—¬, ν•™μŠ΅μžλ£Œμ— μ „ν˜€ μ‚¬μš©λ˜μ§€ μ•Šμ€ ν•œλ°˜λ„λ₯Ό μ΄¬μ˜ν•œ 6개의 SAR μ˜μƒμ„ ν™œμš©ν•˜μ—¬ 탐지 κ²°κ³Όλ₯Ό λΉ„κ΅ν•˜μ˜€λ‹€. 평가 κ²°κ³Ό μ œμ•ˆν•œ 경계 κ°•ν™” μ λŒ€μ  수체 탐지 λͺ¨λΈμ΄ κΈ°μ‘΄ λͺ¨λΈ λŒ€λΉ„ 경계와 μœ„μ–‘μ„± 였λ₯˜μ— λŒ€ν•΄ μ˜¬λ°”λ₯΄κ²Œ νƒμ§€ν•˜λŠ” 것을 증λͺ…ν•˜μ˜€λ‹€. λ˜ν•œ λ‹€μ–‘ν•œ μŠ€μΌ€μΌμ˜ μˆ˜μ²΄μ— λŒ€ν•΄μ„œλ„ κΎΈμ€€νžˆ 높은 정확성을 μœ μ§€ν•˜μ—¬ μ‹€μ œ ν™μˆ˜νƒμ§€λ₯Ό μœ„ν•œ 기반 λͺ¨λΈλ‘œμ˜ κ°€λŠ₯성을 λ³΄μ—¬μ£Όμ—ˆλ‹€.1 Introduction 1 1.1 Research Background 1 1.2 Purpose of Research 7 2 Data Acquisition 11 3 Boundary-driven Adversarial Learning of Deep Neural Networks 21 3.1 Generator architecture 23 3.2 Discriminator architecture 25 3.3 Hybrid Loss 30 4 Experiments 33 4.1 Experiment Settings 33 4.2 Evaluation Metrics 36 4.3 Comparison to other segmentation models 38 4.4 Ablation Studies 43 5 Discussion 51 6 Conclusion 57 Bibliography 59 초 둝 64석

    A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging.

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    As a cutting-edge technique for denoising and feature extraction, singular spectrum analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in situ feature extraction in HSI, conventional pixel-based 1-D SSA fails to produce satisfactory results, while the band-image-based 2D-SSA is also infeasible especially for the popularly used line-scan mode. To tackle these challenges, in this article, a novel 1.5D-SSA approach is proposed for in situ spectral-spatial feature extraction in HSI, where pixels from a small window are used as spatial information. For each sequentially acquired pixel, similar pixels are located from a window centered at the pixel to form an extended trajectory matrix for feature extraction. Classification results on two well-known benchmark HSI datasets and an actual urban scene dataset have demonstrated that the proposed 1.5D-SSA achieves the superior performance compared with several state-of-the-art spectral and spatial methods. In addition, the near real-time implementation in aligning to the HSI acquisition process can meet the requirement of online image analysis for more efficient feature extraction than the conventional offline workflow

    Radar based positioning for unmanned surface vehicle under GPS denial environment

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    Remote Sensing of the Oceans

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    This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

    Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques

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    The grounding line marks the transition between ice grounded at the bedrock and the floating ice shelf. Its location is required for estimating ice sheet mass balance, modelling of ice sheet dynamics and glaciers and for evaluating ice shelf stability, which merits its long-term monitoring. The line migrates both due to short term influences such as ocean tides and atmospheric pressure, and long-term effects such as changes of ice thickness, slope of bedrock and variations in sea level. Of the numerous in-situ and remote sensing methods currently in use to map the grounding line, Differential Interferometric Synthetic Aperture Radar (DInSAR) is, by far, the most accurate technique which produces spatially dense delineations. Tidal deformation at the ice sheet-ice shelf boundary is visible as a dense fringe belt in DInSAR interferograms and its landward limit is taken as a good approximation of the grounding line location (GLL). The GLL is usually manually digitized on the interferograms by human operators. This is both time consuming and introduces inconsistencies due to subjective interpretation especially in low coherence interferograms. On a large scale and with increasing data availability a key challenge is the automation of the delineation procedure. So far, a limited amount of studies were published regarding the delineation processes of typical features on the ice sheets using deep neural networks (DNNs). The objectives of this thesis were to further explore the feasibility of using machine learning for mapping the interferometric grounding line, as well as exploring the contributions of complementary features such as coherence estimated from phase, Digital Elevation Model, ice velocity, tidal displacement and atmospheric pressure, in addition to DInSAR interferograms. A dataset composed of manually delineated GLLs generated within ESA’s Antarctic Ice Sheet Climate Change Initiative project and corresponding DInSAR interferograms from ERS-1/2, Sentinel-1 and TerraSAR-X missions over Antarctica together with the above mentioned features was compiled and used for training two DNNs: Holistically-Nested Edge Detection (HED) andUNet. The developed processing chain handles creation of the training feature stack, training the DNNs and performing post processing functions on the resulting predictions. HED outperformed UNet and was able to achieve a median deviation (from manual delineations) of 209.23 m with a median absolute deviation of 152.91 m. Analysis of the individual feature contributions revealed that only the phase and derived features (real and imaginary interferogram components and coherence estimates) substantially influence the predicted GLLs. This finding is advantageous in terms of saving time, computational effort and memory in creating and storing the above mentioned feature stack. Although the delineations generated from HED do not perfectly follow the true GLL in all locations, the gains in efficiency and consistency are considerable, compared to the time and effort spent for manual digitizations. This study shows the potential of DNNs for automating the interferometric GLL delineation process

    Mapping the grounding line of Antarctica in SAR interferograms with machine learning techniques

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    The grounding line marks the transition between ice grounded at the bedrock and the floating ice shelf. Its location is required for estimating ice sheet mass balance, modelling of ice sheet dynamics and glaciers and for evaluating ice shelf stability, which merits its long-term monitoring. The line migrates both due to short term influences such as ocean tides and atmospheric pressure, and long-term effects such as changes of ice thickness, slope of bedrock and variations in sea level. Of the numerous in-situ and remote sensing methods currently in use to map the grounding line, Differential Interferometric Synthetic Aperture Radar (DInSAR) is, by far, the most accurate technique which produces spatially dense delineations. Tidal deformation at the ice sheet-ice shelf boundary is visible as a dense fringe belt in DInSAR interferograms and its landward limit is taken as a good approximation of the grounding line location (GLL). The GLL is usually manually digitized on the interferograms by human operators. This is both time consuming and introduces inconsistencies due to subjective interpretation especially in low coherence interferograms. On a large scale and with increasing data availability a key challenge is the automation of the delineation procedure. So far, a limited amount of studies were published regarding the delineation processes of typical features on the ice sheets using deep neural networks (DNNs). The objectives of this thesis were to further explore the feasibility of using machine learning for mapping the interferometric grounding line, as well as exploring the contributions of complementary features such as coherence estimated from phase, Digital Elevation Model, ice velocity, tidal displacement and atmospheric pressure, in addition to DInSAR interferograms. A dataset composed of manually delineated GLLs generated within ESA’s Antarctic Ice Sheet Climate Change Initiative project and corresponding DInSAR interferograms from ERS-1/2, Sentinel-1 and TerraSAR-X missions over Antarctica together with the above mentioned features was compiled and used for training two DNNs: Holistically-Nested Edge Detection (HED) andUNet. The developed processing chain handles creation of the training feature stack, training the DNNs and performing post processing functions on the resulting predictions. HED outperformed UNet and was able to achieve a median deviation (from manual delineations) of 209.23 m with a median absolute deviation of 152.91 m. Analysis of the individual feature contributions revealed that only the phase and derived features (real and imaginary interferogram components and coherence estimates) substantially influence the predicted GLLs. This finding is advantageous in terms of saving time, computational effort and memory in creating and storing the above mentioned feature stack. Although the delineations generated from HED do not perfectly follow the true GLL in all locations, the gains in efficiency and consistency are considerable, compared to the time and effort spent for manual digitizations. This study shows the potential of DNNs for automating the interferometric GLL delineation process
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