Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images

Shi, Hao; Fan, Jiahe; Wang, Yupei; Chen, Liang

doi:10.3390/rs13183715

Cited by 13 publications

(12 citation statements)

References 59 publications

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“…The MAT achieved an 88.70 average F1-score on the five foreground classes and a 79.93 mIoU, respectively. Our performance is still competitive with previous works such as UFMG_4 [69].…”

Section: Resultsmentioning

confidence: 55%

Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

et al. 2021

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The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods.

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“…The MAT achieved an 88.70 average F1-score on the five foreground classes and a 79.93 mIoU, respectively. Our performance is still competitive with previous works such as UFMG_4 [69].…”

Section: Resultsmentioning

confidence: 55%

Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

et al. 2021

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show abstract

“…Compared with the existing results of the aforementioned Dual Attention Feature fusion [54] and Class-Wise FCN [55], our method improved the results in classes of building and others, but was lower in car class. With the target of generating the land use classification of an urban area into the bigger and continuous block, our method will do better for bigger classes on images.…”

Section: Discussionmentioning

confidence: 72%

“…In particular, the high accuracy of identifying the building class in the Potsdam datasets is due to the fact that people live in similar residential areas with similar architectural features, proving that the method in this paper takes into account the correlation between neighboring pixels of buildings. The recent proposed Dual Attention Feature fusion method [54] and Class-Wise FCN [55] also use these two datasets, and we compared the performances with the DAU-Net. Table 6 shows the results.…”

Section: Results Of Isprs Vaihingen Dataset and Potsdam Datasetmentioning

confidence: 99%

A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data

Zeng

et al. 2022

Remote Sensing

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The classification of urban land-use information has become the underlying database for a variety of applications including urban planning and administration. The lack of datasets and changeable semantics of land-use make deep learning methods suffer from low precision, which prevent improvements in the effectiveness of using AI methods for applications. In this paper, we first used GIS data to produce a well-tagged and high-resolution urban land-use image dataset. Then, we proposed a combined convolutional neural network named DUA-Net for complex and diverse urban land-use classification. The DUA-Net combined U-Net and Densely connected Atrous Spatial Pyramid Pooling (DenseASPP) to extract Remote Sensing Imagers (RSIs) features in parallel. Then, channel attention was used to efficiently fuse the multi-source semantic information from the output of the double-layer network to learn the association between different land-use types. Finally, land-use classification of high-resolution urban RSIs was achieved. Experiments were performed on the dataset of this paper, the publicly available Vaihingen dataset and Potsdam dataset with overall accuracy levels reaching 75.90%, 89.71% and 89.91%, respectively. The results indicated that the complex land-use types with heterogeneous features were more difficult to extract than the single-feature land-cover types. The proposed DUA-Net method proved suitable for high-precision urban land-use classification, which will be of great value for urban planning and national land resource surveying.

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“…Semantic segmentation aims to associate a label or category with each pixel in an image and identify collections of pixels that constitute different types [108]- [110]. There are two main types of semantic segmentation research: 1) the probabilistic graph model, such as [45] and 2) the DL-based methods [111] that have emerged over the past few years.…”

Section: Semantic Segmentationmentioning

confidence: 99%

A Comprehensive Review for Typical Applications Based Upon Unmanned Aerial Vehicle Platform

Han

Liu

Wang

et al. 2022

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

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Unmanned Aerial Vehicles (UAVs) have been widely applied in military and civilian fields due to their flexibility and effectiveness. As a vital component of UAVs, the vision system has taken on great significance in different applications (e.g., autonomous landing, traffic surveillance, and disaster rescue) to attract widespread attention in recent years. Therefore, the automatic understanding of visual data collected from these air platforms becomes urgently needed in UAV systems. In this review, we revisit and summarize the recent techniques and developments for several typical UAV applications, including object detection, object tracking, and semantic segmentation. In addition, we also highlight the difficulties and subsequent orientations from different perspectives, which may stimulate future research and applications in the UAV vision era.

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Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images

Cited by 13 publications

References 59 publications

Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data

A Comprehensive Review for Typical Applications Based Upon Unmanned Aerial Vehicle Platform

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