In-situ Sea Ice Detection using DeepLabv3 Semantic Segmentation

Balasooriya, Narmada; Dowden, Benjamin; Chen, Jesse; Silva, Oscar De; Huang, Weimin

doi:10.23919/oceans44145.2021.9705801

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…SegNet and PSPNet architectures were used to establish detailed baseline experiments for the datasets. In [131], an automated SIE algorithm was integrated into a mobile device. In [132], considering the impact of raindrops on the segmentation results of captured images, raindrop removal techniques were developed to improve the classification performance.…”

Section: Supervised Learningmentioning

confidence: 99%

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Huang,

Yu,

et al. 2024

Remote Sensing

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Deep learning, which is a dominating technique in artificial intelligence, has completely changed image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications and future trends. Our review focuses on research published from 2016 to the present, with a specific focus on deep-learning-based approaches in the last five years. We divided all related algorithms into three categories, including the conventional image classification approach, the machine learning-based approach and deep-learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in four aspects including climate research, navigation, geographic information systems (GIS) production and others. This paper also provides insightful observations and inspiring future research directions.

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Section: Supervised Learningmentioning

confidence: 99%

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Huang,

Yu,

et al. 2024

Remote Sensing

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“…The fully convolutional neural network proposed by Long [27] achieves segmentation of images of any size by replacing the full connection layer of CNN with the convolution layer, which has already been developed and applied in SAR image segmentation [28,29]. Semantic segmentation attracts a significant amount of attention in the development of deep learning for achieving pixel-wise segmentation [30][31][32][33][34][35][36][37][38][39]. ENet [30], proposed by Paszke et al, has been improved and applied in remote sensing image semantic segmentation [31].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, an atrous-spatial-pyramid-pooling module is applied to probe convolutional features at multiple scales, which further boosts performance. DeepLabV3 is applied in in situ sea-ice detection [35]. OSDES_Net, using group convolutions, is proposed for oil spill detection in SAR images [36].…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Synthetic Aperture Radar Image Semantic Segmentation Network with Lovász-Softmax Loss Optimization

Guo,

Zhao,

Zuo

et al. 2023

Remote Sensing

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The deep learning technique has already been successfully applied in the field of microwave remote sensing. Especially, convolutional neural networks have demonstrated remarkable effectiveness in synthetic aperture radar (SAR) image semantic segmentation. In this paper, a Lovász-softmax loss optimization SAR net (LoSARNet) is proposed which optimizes the semantic segmentation metric intersection over union (IOU) instead of using the traditional cross-entropy loss. Meanwhile, making use of the advantages of the dual-path structure, the network extracts feature through the spatial path (SP) and the context path (CP) to achieve a balance between efficiency and accuracy. Aiming at a polarimetric SAR (PolSAR) image, the proposed network is conducted on the PolSAR datasets for terrain segmentation. Compared to the typical dual-path network, which is the bilateral segmentation network (BiSeNet), the proposed LoSARNet can obtain better mean intersection over union (MIOU). And the proposed network also shows the highest evaluation index and the best performance when compared with several typical networks.

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“…Semantic segmentation needs to determine the category label for each pixel in the image and perform accurate segmentation. At present, there are few studies on Arctic Sea ice identification from remote sensing satellite images using semantic segmentation algorithms, 11 – 15 although there are some for ground-based data 16 . Relevant studies use the U-NET or Deeplabv3 model to realize the semantic segmentation of Arctic Sea ice.…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of Arctic Sea ice in summer from remote sensing satellite images based on BAU-NET

Ji¹,

Fang

Feng³

et al. 2022

J. Appl. Rem. Sens.

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To effectively solve the accurate identification of gray ice, melt ponds water, floe, brash ice, and thin ice in the melting state of the Arctic Sea ice during summer, we propose adding a batch normalization layer and adaptive moment estimation optimizer of a U-NET (BAU-NET) method for Arctic Sea ice semantic segmentation in summer from remote sensing satellite optical images. The U-NET network structure is optimized to 18 convolution layers, and a batch normalization layer and a nonlinear activation function rectified linear unit are added behind each convolution layer. Then the hyper-parameters of the network structure are adjusted. The cross-entropy loss function based on SOFTMAX and L2 regularization are used in training the model, and the adaptive moment estimation optimizer is used for iterative training until the error convergence. The experimental results show that the accuracy, precision, recall, and F1 score of sea ice extraction results reaches more than 97.26%. Compared with the DeepLabv3 and U-Net methods, the sea ice prediction time efficiency is improved by 90.99s and 1.57s, respectively, and the accuracy is improved by 6.59% and 8.05%, respectively, which indicate that the sea ice prediction time efficiency and accuracy of the BAU-NET method are significantly improved.

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In-situ Sea Ice Detection using DeepLabv3 Semantic Segmentation

Cited by 8 publications

References 20 publications

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Polarimetric Synthetic Aperture Radar Image Semantic Segmentation Network with Lovász-Softmax Loss Optimization

Semantic segmentation of Arctic Sea ice in summer from remote sensing satellite images based on BAU-NET

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