Dr. Qiuxia Li Profile

Dr. Qiuxia Li

at Xi'an Jiaotong Univ

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Publications (3)

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Proceedings Article | 15 January 2021 Paper

Hyperspectral image classification using 3D-2D CNN with multi-scale information extraction and fusion module

Hang Gong, Tingkui Mu, Qiuxia Li, Feng Han, Abudusalamu Tuniyazi, Haoyang Li, Wenjing Wang, Zhiping He, Chunlai Li, Haishan Dai

Proceedings Volume 11761, 1176116 (2021) https://doi.org/10.1117/12.2586803

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Classification is the focus and difficulty of hyperspectral imaging technology. Hyperspectral data have twodimensional spatial information and one-dimensional spectral information, which are presented as three-dimensional data blocks with large amount of information, meanwhile high-dimension, high nonlinearity and limited training samples bring great challenges. Deep learning can extract and analyze the features of target data step by step by building multi-layer deep nonlinear structure. The advanced feature, multi scale abstract information extracted by convolution neural network applied to image processing can improve the classification accuracy of complex hyperspectral data. We regard pixel level hyperspectral classification as semantic segmentation network, and creatively introduce squeeze-and-excitation network and pyramid pooling network into hyperspectral classification network and proposed a model based on the structure of 2D-3D hybrid convolution neural network, it can learn deeper spatial spectral features and fusion to improve the accuracy and speed of hyperspectral classification.

Proceedings Article | 15 January 2021 Paper

Iterative reconstruction for linear imaging spectropolarimetry without polarimetric calibration

Feng Han, Tingkui Mu, Abudusalamu Tuniyazi, Qiuxia Li, Hang Gong, Haoyang Li, Wenjing Wang

Proceedings Volume 11761, 1176109 (2021) https://doi.org/10.1117/12.2587680

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Usually, the practical analysis states of an imaging polarimeter needs to be calibrated, with a set of standard polarization states, for the accurate reconstruction of Stokes parameters. However, it is really challenged to get the standard elements over wide field of view (FOV), broad waveband, large aperture, or other non-trivial conditions. Even if the system is well calibrated, the calibrated system will be disturbed in the vibration environment. To avoid the difficult from the standard polarization states, an iterative reconstruction method is presented at the first time to recover the polarization parameters from the data acquired by linear-Stokes polarimeters without polarimetric calibrations. Inspired from phase shifting interferometry, the method employs two least-squares iterative procedure and requires no any extra element for assistant. And we extend the method to a channeled linear imaging spectropolarimeter, channeled linear imaging spectropolarimeter can measure a two-dimensional distribution of spectrally-resolved linear Stokes parameters in a single-shot polarization modulation. However, the state-of-art reconstruction method, Fourier transform method (FTM), usually transforms the modulated spectrum into the frequency domain for further processing. As a result, there is channel crosstalk issue that limits available frequency bandwidth. In addition, FTM needs extra phase calibration to decode final spectra. We present a continuous slide iterative method (CSIM) in the spectral domain to avoid the use of the Fourier transform and phase calibration. It combines a sliding unit cell kernel in the spectral domain that provides unit cell tracking and a loop of twostep least-squares fit that estimates spatially-resolved polarized spectra.

Proceedings Article | 15 January 2021 Paper

Hyperspectral image change detection using two-branch Unet network with feature fusion

Qiuxia Li, Tingkui Mu, Yusen Feng, Hang Gong, Feng Han, Abudusalamu Tuniyazi, Haoyang Li, Wenjing Wang, Chunlai Li, Zhiping He, Haishan Dai

Proceedings Volume 11761, 117611O (2021) https://doi.org/10.1117/12.2586808

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Change detection (CD) is the process of identifying differences in the state of an object or phenomenon by observing it at different times. CD is one of the earliest and most important applications of remote sensing technology. The hyperspectral image (HSI) of remote sensing satellite provides an important and unique data source for CD, but its high dimension, noise and limited data set make the task of CD very challenging. Traditional algorithms are no longer suitable for hyperspectral data processing. Recently, the success of deep convolutional neural networks (CNN) has widely spread across the whole field of computer vision for their powerful representation abilities. Therefore, this paper combines traditional algorithms and deep learning techniques to solve the CD task of hyperspectral remote sensing images. The proposed two-branch Unet network with feature fusion (Unet-ff) model in this paper uses neural networks to automatically extract features to achieve end-to-end change information detection. In order to improve the degree of automation in the application, we select the most effective results as the training sample for the neural network which obtained by various traditional algorithms, and use ground truth to evaluate the detection results. For the characteristics of hyperspectral data, we use effective dimensionality reduction methods and rich data amplification methods to improve the detection accuracy. Experimental results show that our method can achieve better results on the existing classical datasets.

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