ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

Zhang, Xiuwei; Jin, Jiaojiao; Lan, Zeze; Li, Chunjiang; Fan, Minhao; Yu, Xin; Zhang, Yanning

doi:10.3390/rs12020221

Cited by 41 publications

(23 citation statements)

References 49 publications

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“…There are no suitable UAV image datasets for the fine-grained river ice segmentation of the Yellow River. Therefore, based on our previous NWPU_YRCC dataset [12], we further built the NWPU_YRCC2 dataset. The NWPU_YRCC2 dataset contains four categories: Shore ice, drift ice, water, and others.…”

Section: Dataset and Analysismentioning

confidence: 99%

“…The dataset building process is basically similar to that of NWPU_YRCC [12]. The aerial images were taken annually from 2015 to 2019 at the Ningxia-Inner Mongolia reach of the Yellow River from November to March.…”

Section: Dataset and Analysismentioning

confidence: 99%

“…Many efforts have been spent on studying river ice segmentation based on imaging monitoring. In terms of monitoring, there are typically three kinds, including satellitebased monitoring [4][5][6][7], shore-based terrestrial monitoring [8,9], and UAV (unmanned aerial vehicle)-based aerial monitoring [10][11][12]. The advantage of satellite-based monitoring is that its observation range is very large and it is not limited by national boundaries and geographical conditions.…”

Section: Introductionmentioning

confidence: 99%

“…These models are mainly UNet [17], SegNet [18], and DeepLab [19]. In 2020, we [12] also designed a semantic segmentation deep convolution neural network, named ICENET, for river ice semantic segmentation. It revealed that applying deep convolutional neural networks into ice detection and fine-grained ice segmentation is promising.…”

Section: Introductionmentioning

confidence: 99%

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ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images

et al. 2021

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Accurate ice segmentation is one of the most crucial techniques for intelligent ice monitoring. Compared with ice segmentation, it can provide more information for ice situation analysis, change trend prediction, and so on. Therefore, the study of ice segmentation has important practical significance. In this study, we focused on fine-grained river ice segmentation using unmanned aerial vehicle (UAV) images. This has the following difficulties: (1) The scale of river ice varies greatly in different images and even in the same image; (2) the same kind of river ice differs greatly in color, shape, texture, size, and so on; and (3) the appearances of different kinds of river ice sometimes appear similar due to the complex formation and change procedure. Therefore, to perform this study, the NWPU_YRCC2 dataset was built, in which all UAV images were collected in the Ningxia–Inner Mongolia reach of the Yellow River. Then, a novel semantic segmentation method based on deep convolution neural network, named ICENETv2, is proposed. To achieve multiscale accurate prediction, we design a multilevel features fusion framework, in which multi-scale high-level semantic features and lower-level finer features are effectively fused. Additionally, a dual attention module is adopted to highlight distinguishable characteristics, and a learnable up-sampling strategy is further used to improve the segmentation accuracy of the details. Experiments show that ICENETv2 achieves the state-of-the-art on the NWPU_YRCC2 dataset. Finally, our ICENETv2 is also applied to solve a realistic problem, calculating drift ice cover density, which is one of the most important factors to predict the freeze-up data of the river. The results demonstrate that the performance of ICENETv2 meets the actual application demand.

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Section: Dataset and Analysismentioning

confidence: 99%

Section: Dataset and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images

et al. 2021

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“…Recently, Singh [60] et al compared various segmentation models (e.g., DeepLab [57], UNet [18], SegNet [61], DenseNet [9]) for the task of river ice floe segmentation. Zhang et al [62] introduced a convolutional network with dual attention streams for ice segmentation in rivers. Both of these studies use optical image datasets.…”

Section: Introductionmentioning

confidence: 99%

RUF: Effective Sea Ice Floe Segmentation Using End-to-End RES-UNET-CRF with Dual Loss

et al. 2021

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Sea ice observations through satellite imaging have led to advancements in environmental research, ship navigation, and ice hazard forecasting in cold regions. Machine learning and, recently, deep learning techniques are being explored by various researchers to process vast amounts of Synthetic Aperture Radar (SAR) data for detecting potential hazards in navigational routes. Detection of hazards such as sea ice floes in Marginal Ice Zones (MIZs) is quite challenging as the floes are often embedded in a multiscale ice cover composed of ice filaments and eddies in addition to floes. This study proposes a segmentation model tailored for detecting ice floes in SAR images. The model exploits the advantages of both convolutional neural networks and convolutional conditional random field (Conv-CRF) in a combined manner. The residual UNET (RES-UNET) computes expressive features to generate coarse segmentation maps while the Conv-CRF exploits the spatial co-occurrence pairwise potentials along with the RES-UNET unary/segmentation maps to generate final predictions. The whole pipeline is trained end-to-end using a dual loss function. This dual loss function is composed of a weighted average of binary cross entropy and soft dice loss. The comparison of experimental results with the conventional segmentation networks such as UNET, DeepLabV3, and FCN-8 demonstrates the effectiveness of the proposed architecture.

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Oil Spill Identification based on Dual Attention UNet Model Using Synthetic Aperture Radar Images

Mahmoud

Mohamed

El-Khoriby

et al. 2022

J Indian Soc Remote Sens

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Oil spills cause tremendous damage to marine, coastal environments, and ecosystems. Previous deep learning-based studies have addressed the task of detecting oil spills as a semantic segmentation problem. However, further improvement is still required to address the noisy nature of the Synthetic Aperture Radar (SAR) imagery problem, which limits segmentation performance. In this study, a new deep learning model based on the Dual Attention Model (DAM) is developed to automatically detect oil spills in a water body. We enhanced a conventional UNet segmentation network by integrating a dual attention model DAM to selectively highlight the relevant and discriminative global and local characteristics of oil spills in SAR imagery. DAM is composed of a Channel Attention Map and a Position Attention Map which are stacked in the decoder network of UNet. The proposed DAM-UNet is compared with four baselines, namely fully convolutional network, PSPNet, LinkNet, and traditional UNet. The proposed DAM-UNet outperforms the four baselines, as demonstrated empirically. Moreover, the EG-Oil Spill dataset includes a large set of SAR images with 3000 image pairs. The obtained overall accuracy of the proposed method increased by 3.2% and reaches 94.2% compared with that of the traditional UNet. The study opens new development ideas for integrating attention modules into other deep learning tasks, including machine translation, image-based analysis, action recognition, and speech recognition.

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ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

Cited by 41 publications

References 49 publications

ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images

ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images

RUF: Effective Sea Ice Floe Segmentation Using End-to-End RES-UNET-CRF with Dual Loss

Oil Spill Identification based on Dual Attention UNet Model Using Synthetic Aperture Radar Images

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