Context Aggregation Network for Semantic Labeling in Aerial Images

Cheng, Wensheng; Yang, Wen; Wang, Min; Wang, Gang; Chen, Jinyong

doi:10.3390/rs11101158

Cited by 31 publications

(14 citation statements)

References 45 publications

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“…On the other hand, Vaihingen is a village with small and scattered buildings. The Vaihingen dataset contains 3-band IRRG (infrared, red, and green) image data, corresponding DSM, and a normalized digital surface model (NDSM) data [41]. At a GSD of about 9 cm, there are 33 images of about 2500 × 2000 pixels.…”

Section: Datasets Descriptionmentioning

confidence: 99%

Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images

et al. 2020

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Semantic segmentation of high-resolution remote sensing images is highly challenging due to the presence of a complicated background, irregular target shapes, and similarities in the appearance of multiple target categories. Most of the existing segmentation methods that rely only on simple fusion of the extracted multi-scale features often fail to provide satisfactory results when there is a large difference in the target sizes. Handling this problem through multi-scale context extraction and efficient fusion of multi-scale features, in this paper we present an end-to-end multi-scale adaptive feature fusion network (MANet) for semantic segmentation in remote sensing images. It is a coding and decoding structure that includes a multi-scale context extraction module (MCM) and an adaptive fusion module (AFM). The MCM employs two layers of atrous convolutions with different dilatation rates and global average pooling to extract context information at multiple scales in parallel. MANet embeds the channel attention mechanism to fuse semantic features. The high- and low-level semantic information are concatenated to generate global features via global average pooling. These global features are used as channel weights to acquire adaptive weight information of each channel by the fully connected layer. To accomplish an efficient fusion, these tuned weights are applied to the fused features. Performance of the proposed method has been evaluated by comparing it with six other state-of-the-art networks: fully convolutional networks (FCN), U-net, UZ1, Light-weight RefineNet, DeepLabv3+, and APPD. Experiments performed using the publicly available Potsdam and Vaihingen datasets show that the proposed MANet significantly outperforms the other existing networks, with overall accuracy reaching 89.4% and 88.2%, respectively and with average of F1 reaching 90.4% and 86.7% respectively.

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Section: Datasets Descriptionmentioning

confidence: 99%

Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images

et al. 2020

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“…Therefore, we used the four image pieces for four different training and testing sessions. Since training images are limited, like the papers [38][39][40], we did not set up a validation set to ensure adequate training data; (2) The second dataset was GID, which is a high-resolution dataset for land pixel level classification. It contains 150 high-resolution Gaofen-2 (GF-2) images acquired from more than 60 different cities in China [37] from 5 December 2014 to 13 October 2016.…”

Section: Experiments Datamentioning

confidence: 99%

An End-To-End Bayesian Segmentation Network Based on a Generative Adversarial Network for Remote Sensing Images

Xiong

Liu

et al. 2020

Remote Sensing

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Due to the development of deep convolutional neural networks (CNNs), great progress has been made in semantic segmentation recently. In this paper, we present an end-to-end Bayesian segmentation network based on generative adversarial networks (GANs) for remote sensing images. First, fully convolutional networks (FCNs) and GANs are utilized to realize the derivation of the prior probability and the likelihood to the posterior probability in Bayesian theory. Second, the cross-entropy loss in the FCN serves as an a priori to guide the training of the GAN, so as to avoid the problem of mode collapse during the training process. Third, the generator of the GAN is used as a teachable spatial filter to construct the spatial relationship between each label. Some experiments were performed on two remote sensing datasets, and the results demonstrate that the training of the proposed method is more stable than other GAN based models. The average accuracy and mean intersection (MIoU) of the two datasets were 0.0465 and 0.0821, and 0.0772 and 0.1708 higher than FCN, respectively.

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“…To use all the information in the training process, the image was divided into four blocks, three of which were trained in each experiment and one was used for testing. Since the data set is small, following [41][42][43], we have not set a validation set in order to ensure adequate training. Then, four times of training were carried out to get four similar models and corresponding test pictures.…”

Section: Experiments Datamentioning

confidence: 99%

An End-to-End Conditional Random Fields and Skip-Connected Generative Adversarial Segmentation Network for Remote Sensing Images

Fang

Zhang

et al. 2019

Remote Sensing

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Semantic segmentation is an important process of scene recognition with deep learning frameworks achieving state of the art results, thus gaining much attention from the remote sensing community. In this paper, an end-to-end conditional random fields generative adversarial segmentation network is proposed. Three key factors of this algorithm are as follows. First, the network combines generative adversarial network and Bayesian framework to realize the estimation from the prior probability to the posterior probability. Second, the skip connected encoder-decoder network is combined with CRF layer to implement end-to-end network training. Finally, the adversarial loss and the cross-entropy loss guide the training of the segmentation network through back propagation. The experimental results show that our proposed method outperformed FCN in terms of mIoU for 0.0342 and 0.11 on two data sets, respectively.

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Context Aggregation Network for Semantic Labeling in Aerial Images

Cited by 31 publications

References 45 publications

Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images

Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images

An End-To-End Bayesian Segmentation Network Based on a Generative Adversarial Network for Remote Sensing Images

An End-to-End Conditional Random Fields and Skip-Connected Generative Adversarial Segmentation Network for Remote Sensing Images

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