Enhanced Feature Pyramid Vision Transformer for Semantic Segmentation on Thailand Landsat-8 Corpus

Intarat, Kritchayan; Rakwatin, Preesan; Panboonyuen, Teerapong

doi:10.3390/info13050259

Cited by 2 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thanks to the self-attention mechanism, it can extract richer image information. ViT shows good classification and image segmentation performance on large-scale datasets of ImageNet and COCO [23]. Because there are numerous calculations in the self-attention mechanism, it is not conducive to the training and reasoning of the model.…”

Section: Vision Transformermentioning

confidence: 99%

An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

Fan

Liu

et al. 2022

Information

View full text Add to dashboard Cite

Macaque monkey is a rare substitute which plays an important role for human beings in relation to psychological and spiritual science research. It is essential for these studies to accurately estimate the pose information of macaque monkeys. Many large-scale models have achieved state-of-the-art results in pose macaque estimation. However, it is difficult to deploy when computing resources are limited. Combining the structure of high-resolution network and the design principle of light-weight network, we propose the attention-refined light-weight high-resolution network for macaque monkey pose estimation (HR-MPE). The multi-branch parallel structure is adopted to maintain high-resolution representation throughout the process. Moreover, a novel basic block is designed by a powerful transformer structure and polarized self-attention, where there is a simple structure and fewer parameters. Two attention refined blocks are added at the end of the parallel structure, which are composed of light-weight asymmetric convolutions and a triplet attention with almost no parameter, obtaining richer representation information. An unbiased data processing method is also utilized to obtain an accurate flipping result. The experiment is conducted on a macaque dataset containing more than 13,000 pictures. Our network has reached a 77.0 AP score, surpassing HRFormer with fewer parameters by 1.8 AP.

show abstract

Section: Vision Transformermentioning

confidence: 99%

An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

Fan

Liu

et al. 2022

Information

View full text Add to dashboard Cite

show abstract

“…1) is critical for sustainable land management, urban planning, natural disaster management, and environmental conservation. Thailand's diverse landscape includes dense forests, fertile farmland, and urban areas 7 . Remote sensing, with its ability to provide frequent and synoptic coverage of large areas, is a valuable tool for LULC mapping [8][9][10] .…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Enhanced Feature Pyramid Vision Transformer for Semantic Segmentation on Thailand Landsat-8 Corpus

Cited by 2 publications

References 22 publications

An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

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

Contact Info

Product

Resources

About