High spatial resolution shortwave infrared imaging technology based on time delay and digital accumulation method

Jia, Jianxin; Wang, Yueming; Zhuang, Xiaoqiong; Yi, Yong; Wang, Shengwei; Zhao, Ding; Shu, Rong; Wang, Yunlong

doi:10.1016/j.infrared.2017.01.017

Cited by 33 publications

(5 citation statements)

References 13 publications

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“…It can be evaluated using the design values of the instrument parameters before developing the instrument and tested through auxiliary equipment after the instrument is developed. As indicated 2), the performance of the hyperspectral imager can be evaluated using suitable SNR calculation models, before developing the instrument [15], [29], [30]. Laboratorybased [31] and dark current [32] methods are always used to test the SNR after the designers develop the instrument.…”

Section: A Evaluation and Testing Of Snrmentioning

confidence: 99%

Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study

Jia

Chen

Zheng

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer

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Section: A Evaluation and Testing Of Snrmentioning

confidence: 99%

Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study

Jia

Chen

Zheng

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Therefore, the comparisons of image non-uniformity could be used to quantitatively validate the destriping ability of algorithms. The lower value of non-uniformity indicates the improvement of image uniformity, which can be calculated as [46]:…”

Section: Image Uniformitymentioning

confidence: 99%

Column-Spatial Correction Network for Remote Sensing Image Destriping

Zeng

Zhang

et al. 2022

Remote Sensing

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The stripe noise in the multispectral remote sensing images, possibly resulting from the instrument instability, slit contamination, and light interference, significantly degrades the imaging quality and impairs high-level visual tasks. The local consistency of homogeneous region in striped images is damaged because of the different gains and offsets of adjacent sensors regarding the same ground object, which leads to the structural characteristics of stripe noise. This can be characterized by the increased differences between columns in the remote sensing image. Therefore, the destriping can be viewed as a process of improving the local consistency of homogeneous region and the global uniformity of whole image. In recent years, convolutional neural network (CNN)-based models have been introduced to destriping tasks, and have achieved advanced results, relying on their powerful representation ability. Therefore, to effectively leverage both CNNs and the structural characteristics of stripe noise, we propose a multi-scaled column-spatial correction network (CSCNet) for remote sensing image destriping, in which the local structural characteristic of stripe noise and the global contextual information of the image are both explored at multiple feature scales. More specifically, the column-based correction module (CCM) and spatial-based correction module (SCM) were designed to improve the local consistency and global uniformity from the perspectives of column correction and full image correction, respectively. Moreover, a feature fusion module based on the channel attention mechanism was created to obtain discriminative features derived from different modules and scales. We compared the proposed model against both traditional and deep learning methods on simulated and real remote sensing images. The promising results indicate that CSCNet effectively removes image stripes and outperforms state-of-the-art methods in terms of qualitative and quantitative assessments.

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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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High spatial resolution shortwave infrared imaging technology based on time delay and digital accumulation method

Cited by 33 publications

References 13 publications

Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study

Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study

Column-Spatial Correction Network for Remote Sensing Image Destriping

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

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