Research on the shortwave infrared hyperspectral imaging technology based on Integrated Stepwise filter

Wei, Liqing; Xiao, Xurui; Wang, Yueming; Zhuang, Xiaoqiong; Wang, Yunlong

doi:10.1016/j.infrared.2017.08.023

Cited by 13 publications

(2 citation statements)

References 18 publications

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“…The instrument for wavelength scanning was an iHR550 monochromator produced by HORIBA, Ltd. (Kyoto, Japan), and the calibration light source was an LSH-250 tungsten halogen lamp. The spectrometer for spectral calibration was a full spectral airborne hyperspectral imager (FSAHI) [19,20,21], which was developed by the Shanghai Institute of Technical Physics, Chinese Academy of Sciences (Shanghai, China). The main FSAHI parameters are shown in Table 1.…”

Section: Experiments Validationmentioning

confidence: 99%

Preflight Spectral Calibration of Airborne Shortwave Infrared Hyperspectral Imager with Water Vapor Absorption Characteristics

Liu

Zhang

Wang

2019

Sensors

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Due to the strong absorption of water vapor at wavelengths of 1350–1420 nm and 1820–1940 nm, under normal atmospheric conditions, the actual digital number (DN) response curve of a hyperspectral imager deviates from the Gaussian shape, which leads to a decrease in the calibration accuracy of an instrument’s spectral response functions (SRF). The higher the calibration uncertainty of SRF, the worse the retrieval accuracy of the spectral characteristics of the targets. In this paper, an improved spectral calibration method based on a monochromator and the spectral absorptive characteristics of water vapor in the laboratory is presented. The water vapor spectral calibration method (WVSCM) uses the difference function to calculate the intrinsic DN response functions of the spectral channels located in the absorptive wavelength range of water vapor and corrects the wavelength offset of the monochromator via the least-square procedure to achieve spectral calibration throughout the full spectral responsive range of the hyper-spectrometer. The absolute spectral calibration uncertainty is ±0.125 nm. We validated the effectiveness of the WVSCM with two tunable semiconductor lasers, and the spectral wavelength positions calibrated by lasers and the WVSCM showed a good degree of consistency.

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Section: Experiments Validationmentioning

confidence: 99%

Preflight Spectral Calibration of Airborne Shortwave Infrared Hyperspectral Imager with Water Vapor Absorption Characteristics

Liu

Zhang

Wang

2019

Sensors

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“…Among these applications, hyperspectral image (HSI) classification (or semantic segmentation), which aims at assigning a unique label to each pixel of HSI, is a critical enabling step for land-cover monitoring, ecological science, environmental science, and precision agriculture [4,5]. Even though it has attracted considerable attention, it remains a challenging problem because of the limited number of training samples and the spatial variability of spectral signatures [6].…”

Section: Introductionmentioning

confidence: 99%

Triple-Attention-Based Parallel Network for Hyperspectral Image Classification

Zhu

Zheng

et al. 2021

Remote Sensing

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Convolutional neural networks have been highly successful in hyperspectral image classification owing to their unique feature expression ability. However, the traditional data partitioning strategy in tandem with patch-wise classification may lead to information leakage and result in overoptimistic experimental insights. In this paper, we propose a novel data partitioning scheme and a triple-attention parallel network (TAP-Net) to enhance the performance of HSI classification without information leakage. The dataset partitioning strategy is simple yet effective to avoid overfitting, and allows fair comparison of various algorithms, particularly in the case of limited annotated data. In contrast to classical encoder–decoder models, the proposed TAP-Net utilizes parallel subnetworks with the same spatial resolution and repeatedly reuses high-level feature maps of preceding subnetworks to refine the segmentation map. In addition, a channel–spectral–spatial-attention module is proposed to optimize the information transmission between different subnetworks. Experiments were conducted on three benchmark hyperspectral datasets, and the results demonstrate that the proposed method outperforms state-of-the-art methods with the overall accuracy of 90.31%, 91.64%, and 81.35% and the average accuracy of 93.18%, 87.45%, and 78.85% over Salinas Valley, Pavia University and Indian Pines dataset, respectively. It illustrates that the proposed TAP-Net is able to effectively exploit the spatial–spectral information to ensure high performance.

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