Semantic segmentation of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3+

Li, Xiaolong; Li, Yuyin; Ai, Jinquan; Shu, Zhaohan; Xia, Jing; Xia, Yun

doi:10.1371/journal.pone.0279097

Cited by 9 publications

(7 citation statements)

References 34 publications

(46 reference statements)

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“…Li, X. et al found that small objects tend to be overlooked when applying Deeplabv3+ to drone datasets. As a result, they proposed EMNet, which is based on edge feature fusion and multi-level upsampling [25]. Wang, X. et al achieved promising results when applied to high-resolution remote sensing images using a joint model constructed from improved UNet and SegNet [26].…”

Section: Dcnn In the Remote Sensing Domainmentioning

confidence: 99%

MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data

Liu,

Xi,

Liu

et al. 2023

Applied Sciences

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The use of deep learning methods to extract buildings from remote sensing images is a key contemporary research focus, and traditional deep convolutional networks continue to exhibit limitations in this regard. This study introduces a novel multi-feature fusion network (MFFNet), with the aim of enhancing the accuracy of building extraction from high-resolution remote sensing images of various sources. MFFNet improves feature capture for building targets by integrating deep semantic information from various attention mechanisms with multi-scale spatial information from a spatial pyramid module, significantly enhancing the results of building extraction. The performance of MFFNet was tested on three datasets: the self-constructed Jilin-1 building dataset, the Massachusetts building dataset, and the WHU building dataset. Notably, experimental results from the Jilin-1 building dataset demonstrated that MFFNet achieved an average intersection over union (MIoU) of 89.69%, an accuracy of 97.05%, a recall rate of 94.25%, a precision of 94.66%, and an F1 score of 94.82%. Comparisons with the other two public datasets also showed MFFNet’s significant advantages over traditional deep convolutional networks. These results confirm the superiority of MFFNet in extracting buildings from different high-resolution remote sensing data compared to other network models.

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Section: Dcnn In the Remote Sensing Domainmentioning

confidence: 99%

MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data

Liu,

Xi,

Liu

et al. 2023

Applied Sciences

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“…The generator [5,30,31] receives a multifaceted input comprising various components: an initial frame image, an intermediary frame image, a concluding frame image, and their corresponding labeled image. In this study, due to the relative displacement of the UAV when photographing the buildings, the external shape of the buildings does not change with its movement; therefore, we classify this 'building movement' as rigid motion, leading us to adopt an optical flow model with a uniform smoothing strategy.…”

Section: Semi-supervised Optical Flow Estimation Channel In Dual-chan...mentioning

confidence: 99%

Dual-Channel Semi-Supervised Adversarial Network for Building Segmentation from UAV-Captured Images

Zhang,

Wu,

Man

et al. 2023

Remote Sensing

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Accurate building extraction holds paramount importance in various applications such as urbanization rate calculations, urban planning, and resource allocation. In response to the escalating demand for precise low-altitude unmanned aerial vehicle (UAV) building segmentation in intricate scenarios, this study introduces a semi-supervised methodology to alleviate the labor-intensive process of procuring pixel-level annotations. Within the framework of adversarial networks, we employ a dual-channel parallel generator strategy that amalgamates the morphology-driven optical flow estimation channel with an enhanced multilayer sensing Deeplabv3+ module. This approach aims to comprehensively capture both the morphological attributes and textural intricacies of buildings while mitigating the dependency on annotated data. To further enhance the network’s capability to discern building features, we introduce an adaptive attention mechanism via a feature fusion module. Additionally, we implement a composite loss function to augment the model’s sensitivity to building structures. Across two distinct low-altitude UAV datasets within the domain of UAV-based building segmentation, our proposed method achieves average mean pixel intersection-over-union (mIoU) ratios of 82.69% and 79.37%, respectively, with unlabeled data constituting 70% of the overall dataset. These outcomes signify noteworthy advancements compared with contemporaneous networks, underscoring the robustness of our approach in tackling intricate building segmentation challenges in the domain of UAV-based architectural analysis.

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“…On considering the documented behaviour of the beluga whale in human care, the beluga whale has performed the social-sexual mannerism under various postures like pair swims of two BW, which is closely together with the mirrored manner. Hence, the location of the searching agents is determined through the pair swim of BW as well as the location of the beluga whale is updated as given in Eq (11).…”

Section: Plos Onementioning

confidence: 99%

“…As inspired by FCN, a variety of structures and techniques have been recommended to enhance the semantic segmentation process to next level like encoder-decoder architecture using SegNet, DeepLabv2 and spatial pyramid pooling structure on the PSPNet and DeepLabv3+ for semantic segmentation of UAV remote sensing images based on edge feature fusing and multilevel upsampling. Being diverse from the multimedia images, the higher resolution aerial images generally cover a larger area as well as include complex scenes that have brought limitations to the semantic segmentation tasks [ 11 ]. In another case, the high-resolution aerial images also contain rich geographical details like Digital Surface Model (DSM).…”

Section: Introductionmentioning

confidence: 99%

An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images

P.,

2023

PLoS ONE

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Semantic segmentation process over Remote Sensing images has been regarded as hot research work. Even though the Remote Sensing images provide many essential features, the sampled images are inconsistent in size. Even if a similar network can segment Remote Sensing images to some extents, segmentation accuracy needs to be improved. General neural networks are used to improve categorization accuracy, but they also caused significant losses to target scale and spatial features, and the traditional common features fusion techniques can only resolve some of the issues. A segmentation network has been designed to resolve the above-mentioned issues as well. With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. This model has adopted a new Adaptive Multichannel Deeplabv3+ (AMC-Deeplabv3+) with the help of a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO). Here, the hyperparameters of Multichannel deeplabv3+ are optimized by the IBWO algorithm. The proposed model significantly enhances the performance of the overall system by measuring the accuracy and dice coefficient. The proposed model attains improved accuracies of 98.65% & 98.72% for dataset 1 and 2 respectively and also achieves the dice coefficient of 98.73% & 98.85% respectively with a computation time of 113.0123 seconds. The evolutional outcomes of the proposed model show significantly better than the state of the art techniques like CNN, MUnet and DFCNN models.

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Semantic segmentation of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3+

Cited by 9 publications

References 34 publications

MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data

MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data

Dual-Channel Semi-Supervised Adversarial Network for Building Segmentation from UAV-Captured Images

An improved beluga whale optimizer—Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images

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