Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images

Chen, Guanzhou; Zhang, Xiaodong; Wang, Qing; Fan, Deng-Ping; Gong, Yue; Zhu, Kun

doi:10.1109/jstars.2018.2810320

Cited by 139 publications

(72 citation statements)

References 47 publications

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“…The output of each convolution layer, as the input to the next convolution layer, is combined with the output of the other convolution layers which are sequentially fed to the other convolution layers and a Batch normalization layer is added at the end of the last convolution layer. The Batch normalization layer has the ability to handle the interior covariate shift issues and speeds up the training process as it pushes the mean activation of the input data near 0 and the standard deviation near 1 [38].…”

Section: B Densely Connected Residual Networkmentioning

confidence: 99%

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Shamsolmoali

Zareapoor

Wang

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

103

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Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sealand segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and lands. Although several Convolutional Neural Networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixelwise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is a combination of both downsampling and upsampling paths to achieve satisfactory results. In each down-and up-sampling path, in addition to the convolution layers, several densely connected residual network blocks are proposed to systematically aggregate multi-scale contextual information. Each dense network block contains multilevel convolution layers, short-range connections and an identity mapping connection which facilitates features reuse in the network and makes full use of the hierarchical features from the original images. These proposed blocks have a certain number of connections that are designed with shorter distance backpropagation between the layers and can significantly improve segmentation results whilst minimizing computational costs. We have performed extensive experiments on two real datasets Google-Earth and ISPRS and compare the proposed RDU-Net against several variations of Dense Networks. The experimental results show that RDU-Net outperforms the other state-of-the-art approaches on the sea-land segmentation tasks.Index Term-Deep neural network (DNN), dense network (DenseNet), remote sensing images, sea-land segmentation, U-Net.

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Section: B Densely Connected Residual Networkmentioning

confidence: 99%

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Shamsolmoali

Zareapoor

Wang

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

103

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“…Shahzad et al [14] used Fully Convolution Neural Networks to automatically detect man-made structures, especially buildings in very HR SAR Images. Chen et al [15], based on a fully convolutional network (FCN), proposed a symmetrical dense-shortcut FCN (SDFCN) and a symmetrical normal-shortcut FCN (SNFCN) for the semantic segmentation of very HR remote sensing images. Yu et al [16] came up with an end-to-end semantic segmentation framework that can simultaneously segment multiple ground objects from HR images.…”

Section: Introductionmentioning

confidence: 99%

HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery

Wei

Liu

et al. 2020

Remote Sensing

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Instance segmentation in high-resolution (HR) remote sensing imagery is one of the most challenging tasks and is more difficult than object detection and semantic segmentation tasks. It aims to predict class labels and pixel-wise instance masks to locate instances in an image. However, there are rare methods currently suitable for instance segmentation in the HR remote sensing images. Meanwhile, it is more difficult to implement instance segmentation due to the complex background of remote sensing images. In this article, a novel instance segmentation approach of HR remote sensing imagery based on Cascade Mask R-CNN is proposed, which is called a high-quality instance segmentation network (HQ-ISNet). In this scheme, the HQ-ISNet exploits a HR feature pyramid network (HRFPN) to fully utilize multi-level feature maps and maintain HR feature maps for remote sensing images' instance segmentation. Next, to refine mask information flow between mask branches, the instance segmentation network version 2 (ISNetV2) is proposed to promote further improvements in mask prediction accuracy. Then, we construct a new, more challenging dataset based on the synthetic aperture radar (SAR) ship detection dataset (SSDD) and the Northwestern Polytechnical University very-high-resolution 10-class geospatial object detection dataset (NWPU VHR-10) for remote sensing images instance segmentation which can be used as a benchmark for evaluating instance segmentation algorithms in the high-resolution remote sensing images. Finally, extensive experimental analyses and comparisons on the SSDD and the NWPU VHR-10 dataset show that (1) the HRFPN makes the predicted instance masks more accurate, which can effectively enhance the instance segmentation performance of the high-resolution remote sensing imagery; (2) the ISNetV2 is effective and promotes further improvements in mask prediction accuracy; (3) our proposed framework HQ-ISNet is effective and more accurate for instance segmentation in the remote sensing imagery than the existing algorithms.

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“…Another method uses a large kernel size and combines features from different stages of the network to capture long-range information [17,18]. To further recover feature map spatial information, the encoder-decoder architecture is proposed to aggregate different levels' features [19][20][21]. Although context fusion considers different-scale objects, it ignores long-range context relationships between objects.…”

mentioning

confidence: 99%

Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

Liu

Zeng

et al. 2020

Remote Sensing

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Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km 2 . The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.Automatic urban land cover classification in remote sensing images is a difficult task due to several challenges. First of all, high-resolution remote sensing images with abundant details generally have characteristics of high intraclass variance and low inter-class variance [10]. For example, different classes of ground objects may have a similar appearance in remote sensing images, such as trees and low vegetation or roofs and roads. Meanwhile, there is occlusion between different objects. To solve this issue, context information has been widely studied in remote sensing image classification [11]. Contextual information refers to the dependencies between objects, such as cars on roads. Misclassification will be reduced when taking these context relationships into account. In addition, ground objects often have various scales in remote sensing images. Cars and buildings have large differences in size. It is difficult to simultaneously distinguish objects with distinct scales. Generally, deeper layers with larger receptive fields are more suited for segmenting large objects, while shallower layers with smaller receptive fields are suitable to segment small objects [12]. Therefore, it is necessary to enhance global context relationships and fuse multiscale information for pixel-...

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Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images

Cited by 139 publications

References 47 publications

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery

Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

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