Prof. Tianxu Liu Profile

Prof. Tianxu Liu

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SPIE Journal Paper | 18 April 2024

CDCTA: cascaded dual-constrained transformer autoencoder for hyperspectral unmixing with endmember variability and spectral geometry

Yuanhui Yang, Ying Wang, Tianxu Liu

JARS, Vol. 18, Issue 02, 026502, (April 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.026502

KEYWORDS: Transformers, Matrices, Education and training, Data modeling, Ablation, Roads, Image processing, Image restoration, Hyperspectral imaging, Visualization

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Hyperspectral unmixing (HU) in hyperspectral image (HSI) processing is a crucial step. However, the accuracy of unmixing methods is limited by the variability in endmember and the complexity of the HSI structure found in natural scenes. Endmember variability refers to the variations or differences exhibited by endmembers in different locations or under varying conditions within a hyperspectral remote sensing scene. Therefore, to enhance the accuracy of unmixing results, it is crucial to fully leverage spectral, geometric, and spatial information within HSIs, comprehensively exploring the spectral characteristics of endmembers. We present a cascaded dual-constrained transformer autoencoder (AE) for HU with endmember variability and spectral geometry. The model utilizes a transformer AE network to extract the global spatial features in the HSI. Additionally, it incorporates the minimum distance constraint to account for the geometric information of the HSI. Given the similarity in shape exhibited by endmembers of each individual material, with the primary endmember variability being expressed through overall intensity fluctuations, an abundance-weighted constraint method for endmember spectral angle distance is proposed. During training, the architecture utilizes two cascaded networks to preserve the detailed information in the HSI. We evaluate the proposed model using three real datasets. The experimental results indicate that the proposed method achieves superior performance in abundance estimation and endmember extraction. Furthermore, the effectiveness of the two constraint methods was verified through ablation experiments.

Proceedings Article | 23 August 2023 Paper

Lightweight small target detection algorithm based on YOLOv5

Nan Song, Ying Wang, Zhiyi Xin, Huili Xin, Tianxu Liu

Proceedings Volume 12784, 127843H (2023) https://doi.org/10.1117/12.2692005

KEYWORDS: Small targets, Target detection, Detection and tracking algorithms, Object detection, Convolution, Feature extraction

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To address the problems of current small target detection algorithms such as slow detection speed, high memory consumption, and not well applied to mobile devices, an improved lightweight small target detection algorithm MTG-YOLOv5 is proposed based on YOLOv5s algorithm. firstly, MobileNet V3 is used instead of the backbone network to reduce the number of network parameters and increase the speed of operation. Secondly, the small target detection layer is added to the original network structure to enhance the detection capability of small targets. Finally, Ghost module is introduced to replace the commonly used convolutional kernel to ensure detection accuracy and further light weighting. The improved algorithm was tested on the processed TinyPerson dataset. The experimental results show that the overall average accuracy of mAP of YOLOv5s algorithm is improved by 4.5%, the parameters are reduced by 1/2, the FPS is improved from 123.54 to 187.62, and the speed is enhanced by 51.87%. The algorithm effectively improves the detection speed while ensuring the detection accuracy.

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