scholarly journals Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning

2019 ◽  
Vol 11 (21) ◽  
pp. 2529 ◽  
Author(s):  
Celia A. Baumhoer ◽  
Andreas J. Dietz ◽  
C. Kneisel ◽  
C. Kuenzer
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Ice Shelf ◽  
Low Contrast ◽  
Remote Sensing Imagery ◽  
Front Position ◽  
Elevation Model ◽  
The Antarctic

Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr.

scholarly journals Fast recession of the northern Larsen Ice Shelf monitored by space images

1993 ◽  
Vol 17 ◽  
pp. 317-321 ◽  
Author(s):  
Pedro Skvarca
Keyword(s):  
Remote Sensing ◽  
Antarctic Peninsula ◽  
Climatic Warming ◽  
Ice Shelf ◽  
Space Images ◽  
Front Position ◽  
Satellite Photograph ◽  
The Antarctic ◽  
Larsen Ice Shelf

The rapid retreat and disintegration of the Larsen Ice Shelf sector extending north of Seal Nunataks (65° S), documented from the mid 1970s onwards by remote sensing, is presented and related to the Antarctic Peninsula climatic warming recorded over several past decades. A 1975 KOSMOS satellite photograph and a series of LANDSAT MSS and TM images taken in 1978, 1979, 1986, 1988 and 1989 were used to monitor the retreat of the ice shelf between Seal Nunataks and Prince Gustav Channel. The ice shelf has decreased by more than 30% during the period 1975–89 within the Christensen Island to Cape Longing region. Measurements of the ice front position carried out in the field during late 1991 indicate that the recession between Lindenberg Island and Sobral Peninsula is still continuing, in some places at a rate of up to 2.5 km a−1.

scholarly journals DEANet: Dual Encoder with Attention Network for Semantic Segmentation of Remote Sensing Imagery

2021 ◽  
Vol 13 (19) ◽  
pp. 3900
Author(s):  
Haoran Wei ◽  
Xiangyang Xu ◽  
Ni Ou ◽  
Xinru Zhang ◽  
Yaping Dai
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Feature Maps ◽  
Attention Network ◽  
Remote Sensing Imagery ◽  
Training Strategy ◽  
Sensing Technology ◽  
Deep Learning Network ◽  
Public Datasets

Remote sensing has now been widely used in various fields, and the research on the automatic land-cover segmentation methods of remote sensing imagery is significant to the development of remote sensing technology. Deep learning methods, which are developing rapidly in the field of semantic segmentation, have been widely applied to remote sensing imagery segmentation. In this work, a novel deep learning network—Dual Encoder with Attention Network (DEANet) is proposed. In this network, a dual-branch encoder structure, whose first branch is used to generate a rough guidance feature map as area attention to help re-encode feature maps in the next branch, is proposed to improve the encoding ability of the network, and an improved pyramid partial decoder (PPD) based on the parallel partial decoder is put forward to make fuller use of the features form the encoder along with the receptive filed block (RFB). In addition, an edge attention module using the transfer learning method is introduced to explicitly advance the segmentation performance in edge areas. Except for structure, a loss function composed with the weighted Cross Entropy (CE) loss and weighted Union subtract Intersection (UsI) loss is designed for training, where UsI loss represents a new region-based aware loss which replaces the IoU loss to adapt to multi-classification tasks. Furthermore, a detailed training strategy for the network is introduced as well. Extensive experiments on three public datasets verify the effectiveness of each proposed module in our framework and demonstrate that our method achieves more excellent performance over some state-of-the-art methods.

scholarly journals Fast recession of the northern Larsen Ice Shelf monitored by space images

1993 ◽  
Vol 17 ◽  
pp. 317-321 ◽  
Author(s):  
Pedro Skvarca
Keyword(s):  
Remote Sensing ◽  
Antarctic Peninsula ◽  
Climatic Warming ◽  
Ice Shelf ◽  
Space Images ◽  
Front Position ◽  
Satellite Photograph ◽  
The Antarctic ◽  
Larsen Ice Shelf

The rapid retreat and disintegration of the Larsen Ice Shelf sector extending north of Seal Nunataks (65° S), documented from the mid 1970s onwards by remote sensing, is presented and related to the Antarctic Peninsula climatic warming recorded over several past decades. A 1975 KOSMOS satellite photograph and a series of LANDSAT MSS and TM images taken in 1978, 1979, 1986, 1988 and 1989 were used to monitor the retreat of the ice shelf between Seal Nunataks and Prince Gustav Channel. The ice shelf has decreased by more than 30% during the period 1975–89 within the Christensen Island to Cape Longing region. Measurements of the ice front position carried out in the field during late 1991 indicate that the recession between Lindenberg Island and Sobral Peninsula is still continuing, in some places at a rate of up to 2.5 km a−1.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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