An End-to-End Neural Network for Road Extraction From Remote Sensing Imagery by Multiple Feature Pyramid Network

Gao, Xun; Sun, Xian; Zhang, Yi; Yan, Menglong; Xu, Guangluan; Sun, Hao; Jiao, Jiao; Fu, Kun

doi:10.1109/access.2018.2856088

Cited by 83 publications

(51 citation statements)

References 29 publications

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“…In this paper, we utilized the widely used average precision (AP) [22][23][24], precision-recall curve (PRC) [25][26][27], and F-measure (F1) [25,27,36] metrics to quantitatively evaluate the performance of the proposed SCFPN. The AP computes the average value of the precision over the interval from recall = 0 to recall = 1, and the higher the AP, the better the performance.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network

et al. 2019

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Object detection has attracted increasing attention in the field of remote sensing image analysis. Complex backgrounds, vertical views, and variations in target kind and size in remote sensing images make object detection a challenging task. In this work, considering that the types of objects are often closely related to the scene in which they are located, we propose a convolutional neural network (CNN) by combining scene-contextual information for object detection. Specifically, we put forward the scene-contextual feature pyramid network (SCFPN), which aims to strengthen the relationship between the target and the scene and solve problems resulting from variations in target size. Additionally, to improve the capability of feature extraction, the network is constructed by repeating a building aggregated residual block. This block increases the receptive field, which can extract richer information for targets and achieve excellent performance with respect to small object detection. Moreover, to improve the proposed model performance, we use group normalization, which divides the channels into groups and computes the mean and variance for normalization within each group, to solve the limitation of the batch normalization. The proposed method is validated on a public and challenging dataset. The experimental results demonstrate that our proposed method outperforms other state-of-the-art object detection models.

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Section: Evaluation Metricsmentioning

confidence: 99%

Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network

et al. 2019

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“…To make networks more robust for geometric variations of objects in remote sensing images, one effective way is to make use of features of middle layers from CNN and then exploit the multi-scale features with multi-level information [25][26][27][28][29][30][31]. Ding et al [25] directly concatenate the multi-scale features of CNN to obtain the fine-grained details for detecting small objects.…”

Section: Shipmentioning

confidence: 99%

“…Furthermore, Li et al [30] proposed a hierarchical selective filtering layer that mapped features of multiple scales to the same scale space for ship detection with various scales. Gao et al [31] designed a tailored pooling pyramid module (TPPM) to take advantage of the contextual information of different subregions with different scales.…”

Section: Shipmentioning

confidence: 99%

A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images

Qiu

et al. 2019

Remote Sensing

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Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, we propose a novel end-to-end Adaptively Aspect Ratio Multi-Scale Network (A 2 RMNet) to solve this problem. On the one hand, we design a multi-scale feature gate fusion network to adaptively integrate the multi-scale features of objects. This network is composed of gate fusion modules, refine blocks and region proposal networks. On the other hand, an aspect ratio attention network is leveraged to preserve the aspect ratios of objects, which alleviates the excessive shape distortions of objects caused by aspect ratio changes during training. Experiments show that the proposed A 2 RMNet significantly outperforms the previous state of the arts on the DOTA dataset, NWPU VHR-10 dataset, RSOD dataset and UCAS-AOD dataset by 5.73 % , 7.06 % , 3.27 % and 2.24 % , respectively.

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“…[64] Used a classical Fuzzy C-means (FCM) method was for the coastline detection, but had been improved by combining the Wavelet decomposition algorithm to better suppress the inherent speckle noises of SAR image. In [65], [66] the authors proposed an end-to-end framework called multiple feature pyramid network (MFPN). In MFPN, an effective feature pyramid and a tailored pyramid pooling module are implemented that takes advantage of multilevel semantic features of high resolution remote sensing images.…”

Section: Introductionmentioning

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

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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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An End-to-End Neural Network for Road Extraction From Remote Sensing Imagery by Multiple Feature Pyramid Network

Cited by 83 publications

References 29 publications

Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network

Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network

A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images

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

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