Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation

Wu, Linshan; Lu, Ming; Fang, Leyuan

doi:10.1109/tgrs.2022.3163278

Cited by 24 publications

(11 citation statements)

References 34 publications

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“…SSL for medical image analysis. Due to the high potential in label-efficient learning [29,56,59,57,58,37], SSL has also received significant attention in the field of medical image analysis [68,32,31,50,19]. Existing methods are mainly based on comparative SSL [69].…”

Section: Related Workmentioning

confidence: 99%

Distributed contrastive learning for medical image segmentation

Zeng

Wang

et al. 2022

Medical Image Analysis

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Section: Related Workmentioning

confidence: 99%

Distributed contrastive learning for medical image segmentation

Zeng

Wang

et al. 2022

Medical Image Analysis

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“…Subsequently, with the continuous improvement of generative adversarial networks (GANs), it has become a popular solution to extract the domain-invariant features using adversarial learning [7]. More recently, the self-supervised learning methods have been gradually applied with the stable and efficient training process and superior performance, further improving the segmentation accuracy of target RSIs [8].…”

Section: Introductionmentioning

confidence: 99%

Learning General-Purpose Representations for Cross-Domain Hyperspectral Images Classification with Small Samples

Gao

Xiong

et al. 2023

Remote Sensing

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Cross-domain classification with small samples is a more challenging and realistic experimental setup. Until now, few studies have focused on the problem of small-sample cross-domain classification between completely different hyperspectral images (HSIs) since they possess different land cover types and statistical characteristics. To this end, this paper proposes a general-purpose representation learning method for cross-domain HSI classification, aiming to enable the model to learn more general-purpose deep representations that can quickly adapt to different target domains with small samples. The core of this method is to propose a novel three-level distillation strategy to transfer knowledge from multiple models well-trained on source HSIs into a single distilled model at the channel-, feature- and logit-level simultaneously. The learned representations can be further fine-tuned with small samples and quickly adapt to new target HSIs and previously unseen classes. Specifically, to transfer and fuse knowledge from multiple-source domains into a single model simultaneously and solve the inconsistency of the number of bands in different HSIs, an extensible multi-task model, including the channel transformation module, the feature extraction module and the linear classification module, is designed. Only the feature extraction module is shared across different HSIs, while the other two modules are domain-specific. Furthermore, the typical episode-based learning strategy of the metric-based meta-learning is adopted in the whole learning process to further improve the generalization ability and data efficiency. Extensive experiments are conducted on six source HSIs and four target HSIs, and the results demonstrate that the proposed method outperforms the existing advanced methods in cross-domain HSI classification with small samples.

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“…With the advent of deep learning techniques, particularly CNNs, automatic LULC feature extraction has become much faster and more accurate [23][24][25][26] . The automatic identification and mapping of different land use and cover types provide valuable information for various applications 17,[27][28][29][30][31] , including urban planning, agriculture, forestry, disaster management, and environmental monitoring.…”

Section: Introductionmentioning

confidence: 99%

MeViT: Medium-Resolution Vision Transformer for Semantic Segmentation on Landsat Satellite Imagery for Agriculture in Thailand

Panboonyuen

Charoenphon

Satirapod

2023

Preprint

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Semantic segmentation is a fundamental task in remote sensing image analysis that aims to classify each pixel in an image into different land use and land cover (LULC) segmentation task. In this paper, We propose MeViT (Medium-Resolution Vision Transformer) on Landsat satellite imagery for the main economic crops in Thailand as follows: (i) para rubber, (ii) corn, and (iii) pineapple. Therefore, our proposed MeViT enhances Vision Transformers (ViTs), one of the modern deep learning on computer vision tasks, to learn semantically rich and spatially-precise multi-scale representations by integrating medium-resolution multi-branch architectures with ViTs. We revised mixed-scale convolutional feedforward networks (MixCFN) by incorporating multiple depth-wise convolution paths to extract multi-scale local information to balance the model’s performance and efficiency. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on the publicly available dataset of Thailand scenes and compare the results with several state-of-the-art deep learning methods such as AutoDeeplab1, SwinTransformer2, Twins3, CSWinTransformer4, SegFormer5, and HRViT6. The experimental results demonstrate that our proposed MeViT outperforms existing methods and performs better in the semantic segmentation of Thailand scenes The evaluation metrics used are precision, recall, F1 score, and mean Intersection over Union (IoU). Among the models compared, MeViT, our proposed model, achieves the best performance in all evaluation metrics. MeViT achieves a precision of 0.9222, a recall of 0.9469, an F1 score of 0.9344, and a mean IoU of 0.8363. These results demonstrate the effectiveness of our proposed approach in accurately segmenting Thai Landsat-8 data. The achieved F1 score of overall using our proposed MeViT is 93.44% that is a major significance of this work.

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Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation

Cited by 24 publications

References 34 publications

Distributed contrastive learning for medical image segmentation

Distributed contrastive learning for medical image segmentation

Learning General-Purpose Representations for Cross-Domain Hyperspectral Images Classification with Small Samples

MeViT: Medium-Resolution Vision Transformer for Semantic Segmentation on Landsat Satellite Imagery for Agriculture in Thailand

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