SeNet: Structured Edge Network for Sea–Land Segmentation

Cheng, Dongcai; Meng, Guang; Cheng, Guangliang; Chen, Pan

doi:10.1109/lgrs.2016.2637439

Cited by 99 publications

(60 citation statements)

References 17 publications

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“…This lowers the contrast between land ice and ocean, making the glacier and ice shelf front extraction very challenging. Within the last years, CNNs performed well for coastline extraction on non-polar environments, which yields potential for the application on polar coastlines if the low contrast between different ice types can be tackled [19,26]. Recent studies on single glaciers presented first achievements by implementing convolutional neural networks for glacier front detection [27,28].…”

Section: Coastline Extractionmentioning

confidence: 99%

“…For coastline extraction, traditional thresholding and segmentation approaches were tested against different FCN architectures. The latter outperformed the traditional image processing techniques with better accuracies [26]. The best performance for segmentation in coastal areas was performed by FCNs based on the U-Net architecture [19] introduced by Ronneberger et al [22].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Coastline Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Remote sensing (RS) image segmentation technology plays a key role in the fields of urban planning [1], RS mapping [2,3], precision agriculture [4,5], landscape classification [6,7], traffic monitoring [8], environmental protection [9], climate change [10] and forest vegetation [11], and therefore provides important decision support for human work and life.…”

Section: Introductionmentioning

confidence: 99%

Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation

Chen

Wei

et al. 2019

Applied Sciences

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Recent developments in Convolutional Neural Networks (CNNs) have allowed for the achievement of solid advances in semantic segmentation of high-resolution remote sensing (HRRS) images. Nevertheless, the problems of poor classification of small objects and unclear boundaries caused by the characteristics of the HRRS image data have not been fully considered by previous works. To tackle these challenging problems, we propose an improved semantic segmentation neural network, which adopts dilated convolution, a fully connected (FC) fusion path and pre-trained encoder for the semantic segmentation task of HRRS imagery. The network is built with the computationally-efficient DeepLabv3 architecture, with added Augmented Atrous Spatial Pyramid Pool and FC Fusion Path layers. Dilated convolution enlarges the receptive field of feature points without decreasing the feature map resolution. The improved neural network architecture enhances HRRS image segmentation, reaching the classification accuracy of 91%, and the precision of recognition of small objects is improved. The applicability of the improved model to the remote sensing image segmentation task is verified.

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“…Since Long et al [16] adapted the classification network into fully convolutional network (FCN) for semantic segmentation, FCN and its extensions have gradually become the preferred solution in the field of semantic labeling [17][18][19][20]. Though FCN-based methods can produce dense pixel-wise output directly, the pixel-wise classification derived from the final score map is quite coarse because of the sequential sub-sampling operations in the FCN.To address the problem of coarse predictions, recent research [21][22][23][24][25][26] have further improved FCN-based methods for semantic labeling of remote sensing images. There is a growing body of literature that many studies [27][28][29][30][31] employ the encoder-decoder architecture with skip connection.…”

mentioning

confidence: 99%

“…To address the problem of coarse predictions, recent research [21][22][23][24][25][26] have further improved FCN-based methods for semantic labeling of remote sensing images. There is a growing body of literature that many studies [27][28][29][30][31] employ the encoder-decoder architecture with skip connection.…”

mentioning

confidence: 99%

Building Extraction from Very High Resolution Aerial Imagery Using Joint Attention Deep Neural Network

Gan

et al. 2019

Remote Sensing

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Automated methods to extract buildings from very high resolution (VHR) remote sensing data have many applications in a wide range of fields. Many convolutional neural network (CNN) based methods have been proposed and have achieved significant advances in the building extraction task. In order to refine predictions, a lot of recent approaches fuse features from earlier layers of CNNs to introduce abundant spatial information, which is known as skip connection. However, this strategy of reusing earlier features directly without processing could reduce the performance of the network. To address this problem, we propose a novel fully convolutional network (FCN) that adopts attention based re-weighting to extract buildings from aerial imagery. Specifically, we consider the semantic gap between features from different stages and leverage the attention mechanism to bridge the gap prior to the fusion of features. The inferred attention weights along spatial and channel-wise dimensions make the low level feature maps adaptive to high level feature maps in a target-oriented manner. Experimental results on three publicly available aerial imagery datasets show that the proposed model (RFA-UNet) achieves comparable and improved performance compared to other state-of-the-art models for building extraction.to detect large objects [14,15]. Since Long et al. [16] adapted the classification network into fully convolutional network (FCN) for semantic segmentation, FCN and its extensions have gradually become the preferred solution in the field of semantic labeling [17][18][19][20]. Though FCN-based methods can produce dense pixel-wise output directly, the pixel-wise classification derived from the final score map is quite coarse because of the sequential sub-sampling operations in the FCN.To address the problem of coarse predictions, recent research [21][22][23][24][25][26] have further improved FCN-based methods for semantic labeling of remote sensing images. There is a growing body of literature that many studies [27][28][29][30][31] employ the encoder-decoder architecture with skip connection. UNet [32], a typical model in the style of encoder-decoder, reuses low-level information to refine the output, and results in better performance. For obtaining accurate labeling of VHR images, an effective structure to integrate the high-resolution, low-level features, and the low-resolution, high-level features is needed. The skip connection fuses features so as to compensate the loss of spatial information caused by repeating local operations (e.g., pooling and strided convolution). Features via skip connection are multi-scale in nature due to the increasingly large receptive field sizes [33]. However, one thing to note is that most existing approaches that are built on top of a contemporary classification network are good at aggregating global contexts. While the reuse of information from early encoding layers contributes to localization in the decoding phase, it may introduce redundant information which results in over-segmentation [3...

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SeNet: Structured Edge Network for Sea–Land Segmentation

Cited by 99 publications

References 17 publications

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

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

Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation

Building Extraction from Very High Resolution Aerial Imagery Using Joint Attention Deep Neural Network

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