CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images

Ye, Huanran; Liu, Sheng; Jin, Kun; Cheng, Haohao

doi:10.1109/icpr48806.2021.9412355

Cited by 17 publications

(5 citation statements)

References 18 publications

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“…The UNet [17] is the most commonly used network structure and its special encoder-decoder structure shows excellent performance in the segmentation tasks. The improved versions of the UNet have also been proposed by introducing a residual module or a skip connection into the convolution operation to supplement the information loss in the reconstruction process [20][21][22] and using the attention modules at different locations in the network to help focus on more important features [23][24][25][26][27]. The NestedUNet [28] is a special structure that uses the dense skip connections across multiple scales to extract features, but it involves the complicated computation.…”

Section: Convolutional Neural Network Based Segmentation Methodsmentioning

confidence: 99%

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Wen

Zhou

Tao

et al. 2023

CAAI Trans on Intel Tech

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Tumour segmentation in medical images (especially 3D tumour segmentation) is highly challenging due to the possible similarity between tumours and adjacent tissues, occurrence of multiple tumours and variable tumour shapes and sizes. The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network (CNN) and Transformer. The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data. The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN. To address these issues, a short‐term and long‐term memory self‐attention network is proposed. Firstly, a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN. Then, the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours. Finally, the multi‐layer reconstruction blocks will predict the tumour boundaries. Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation. Compared with the most competitive method, the proposed method provides Dice (82.4% vs. 76.6%) and Hausdorff distance 95% (HD95) (10.66 vs. 11.54 mm) on the KiTS19 as well as Dice (80.2% vs. 78.4%) and HD95 (9.632 vs. 12.17 mm) on the LiTS.

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Section: Convolutional Neural Network Based Segmentation Methodsmentioning

confidence: 99%

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Wen

Zhou

Tao

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…One of the main challenges in the utilization of UAV and remote sensing images for building extraction lies in the inconsistency of lighting conditions and the presence of shadows, which can significantly impact segmentation accuracy [8][9][10][11][12][13][14][15]. The study is shown in Figure 5.…”

Section: Imaging Conditions Of Uneven Illuminationmentioning

confidence: 99%

“…Nevertheless, these approaches frequently hinge on intricate pixel-level annotations and comprehensive building outlines, which are not readily obtainable for drone-sourced images [6,7]. Furthermore, the inherent feature similarity between buildings and their backgrounds might result in internal inconsistencies within the segmentation outcomes [8], potentially leading to the misclassification of buildings as background entities.…”

Section: Introductionmentioning

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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“…The model uses ResNet101 weights pretrained on ImageNet [28] with the standard Unet structure described in the paper. We chose to use the Unet due to its popularity and performance in segmentation tasks [46,1,18,22]. Its simple and intuitive design has allowed for many variants, including the transformer based variants we considered.…”

Section: Benchmark Segmentation Modelsmentioning

confidence: 99%

Transformers For Recognition In Overhead Imagery: A Reality Check

Luzi¹,

Gupta²,

Collins³

et al. 2022

Preprint

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There is evidence that transformers offer state-of-theart recognition performance on tasks involving overhead imagery (e.g., satellite imagery). However, it is difficult to make unbiased empirical comparisons between competing deep learning models, making it unclear whether, and to what extent, transformer-based models are beneficial. In this paper we systematically compare the impact of adding transformer structures into state-of-the-art segmentation models for overhead imagery. Each model is given a similar budget of free parameters, and their hyperparameters are optimized using Bayesian Optimization with a fixed quantity of data and computation time. We conduct our experiments with a large and diverse dataset comprising two large public benchmarks: Inria and DeepGlobe. We perform additional ablation studies to explore the impact of specific transformer-based modeling choices. Our results suggest that transformers provide consistent, but modest, performance improvements. We only observe this advantage however in hybrid models that combine convolutional and transformer-based structures, while fully transformerbased models achieve relatively poor performance.

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CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images

Cited by 17 publications

References 18 publications

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

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

Transformers For Recognition In Overhead Imagery: A Reality Check

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