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

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 × 107 km2 (2010) and 1.3657 × 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

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 영상을 활용한 홍수 모니터링을 위한 경계 강화 적대적 학습 딥러닝 모델

학위논문(석사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 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.기후변화가 가속화로 인해 수재해의 빈도와 강도 예측이 어려워짐에 따라 실시간 홍수 모니터링에 대한 수요가 증가하고 있다. 합성개구레이다는 광원과 날씨에 무관하게 지속적으로 촬영이 가능한 레이다로, 수재해가 발생하였을 때에도 영상을 제공할 수 있다. 이에 합성개구레이다를 활용한 수체 탐지 알고리즘 개발이 활발히 연구되어 왔다. 특히 딥러닝의 발달로 CNN을 활용한 수체 탐지 알고리즘이 연구됨에 따라, 높은 정확도로 수체 탐지가 기능해졌다. 하지만, CNN 기반 수체 탐지 모델은 훈련 시 높은 정량적 정확성 지표를 달성하여도 추론 후 정성적 평가 시 경계와 소하천에 대한 정확성이 떨어진다. 홍수 모니터링에서 특히 중요한 정보인 경계와 좁은 하천에 대해서 탐지의 정확성이 떨어짐에 따라 실생활 적용이 어렵다. 이에 우리는 경계를 강화한 적대적 학습 기반의 수체 탐지 모델을 개발하여 쉽게 탐지되지 않았던 부분까지 탐지하고자 한다. 적대적 학습은 생성적 적대 신경망(GAN)의 두 개의 모델인 생성자와 판별자가 서로 관여하며 더 높은 정확도를 달성할 수 있도록 학습하는 과정을 의미한다. 판별자는 생성자의 추론 결과와 실제 라벨 데이터를 구분하기 위해 학습하는 반면, 생성자는 판별자를 속이기 위해 더 실제 데이터 같은 가짜 데이터를 생성하고자 노력한다. 이러한 적대적 학습 개념을 수체 탐지 모델에 처음으로 도입하여, 생성자는 실제 라벨 데이터와 유사하게 수체 경계와 소하천까지 탐지하고자 학습한다. 반면 판별자는 경계 거리 변환 맵과 합성개구레이다 영상을 기반으로 라벨데이터와 수체 탐지 결과를 구분한다. 이때 경계 거리 변환 맵은 작은 하천과 경계에 가중치를 준 이미지로, 판별자로 하여금 판별시 작은 영역까지 고려할 수 있도록 강조하는 동시에 오탐지에 대해 억제할 수 있는 역할을 위해 제안하였다. 경계가 강조된 방향으로 적대적 학습 과정이 진행될 수 있도록, Binary Cross Entropy 손실 함수, Hausdorff distance 기반 손실 함수 그리고 적대적 손실 함수를 융합한 하이브리드 손실 함수를 새롭게 구성하였다. 제안 모델이 경계와 소하천을 정확히 탐지하는지 판단하기 위해, 정량적 지표로 F1-score, Boundary IoU, Matthews Correlation Coefficient를 사용하였으며, 육안 판독을 통해 정성적 평가도 진행하였다. 이를 통해 제안한 모델이 경계 및 소하천까지 정확하게 탐지해냄을 증명하였다. 실제 홍수 탐지에 사용하기 위해선 패치 단위 이미지가 아닌 전체 SAR 영상에서도 높은 정확도를 유지하는지 확인이 필요하다. 이를 위해 패치 단위로 학습된 모델이 전체 SAR 영상을 탐지할 수 있도록 추가 코드를 개발하여, 학습자료에 전혀 사용되지 않은 한반도를 촬영한 6개의 SAR 영상을 활용하여 탐지 결과를 비교하였다. 평가 결과 제안한 경계 강화 적대적 수체 탐지 모델이 기존 모델 대비 경계와 위양성 오류에 대해 올바르게 탐지하는 것을 증명하였다. 또한 다양한 스케일의 수체에 대해서도 꾸준히 높은 정확성을 유지하여 실제 홍수탐지를 위한 기반 모델로의 가능성을 보여주었다.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 록 64석

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

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

Remote Sensing of the Oceans

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

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

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