New generation VNIR/SWIR/TIR airborne imaging spectrometer

Wang, Yueming; Wei, Liqin; Yuan, Liyin; Li, Chunlai; Lv, Gang; Xie, Feng; Han, Guicheng; Shu, Rong; Wang, Yunlong

doi:10.1117/12.2245541

Cited by 18 publications

(5 citation statements)

References 2 publications

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“…Nine spectrometers were integrated into one imaging system to ensure that the hyperspectral images from VNIR to LWIR could be acquired. The final system was integrated successfully to meet the precision requirement of spectral registration by adapting the mount interface for the PAV80 Gyro-stabilized platform [26]. Compared with other existing airborne hyperspectral imagers, AMMIS is highly competitive in terms of spectral range, spectral resolution, spatial resolution, and FOV, as evident from Table I [15].…”

Section: A Ammismentioning

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 Ammismentioning

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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“…The two hyperspectral remote sensing images produced by the full spectrum airborne hyperspectral imager (FAHI), including the near-infrared (VNIR) and shortwave-infrared (SWIR) images, were utilized to test the destriping performances of nine methods. FAHI is the Chinese nextgeneration pushbroom hyperspectral image instrument [45], and the main parameters are listed in Table 3. Other test images were the public CHRIS images, including CHRIS_AM and CHRIS_UK, and Terra MODIS.…”

Section: Discussionmentioning

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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“…Experiments were undertaken using real images afflicted with stripe non-uniformity, sourced from a hyperspectral imaging instrument [38], as well as public remote sensing datasets, specifically CHRIS AM and Terra MODIS. In each experiment, we present corrected images employing various destriping techniques.…”

Section: Real Experimentsmentioning

confidence: 99%

Effective three-step method for efficient correction of stripe noise and non-uniformity in infrared remote sensing images

Hamadouche

2024

Phys. Scr.

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The non-uniformity inherent in infrared detectors’ readout circuits often manifests as stripe noise, significantly impacting the interpretability and utility of infrared images in remote sensing applications. This paper introduces a novel three-step approach designed to overcome the challenges posed by stripe noise, offering a balance between real-time performance, detail preservation, and noise suppression. The proposed method involves subtracting the average of image columns from the noisy image, adding the wavelet denoised average signal to the subtraction result, and finally correcting the resulting image using an image-guidance mechanism. This unique three-step process ensures effective noise removal while preserving image details. The incorporation of wavelet transform leverages the sparsity of noise in the wavelet domain, enhancing denoising without introducing blurring. In a further refinement, the third step utilizes an image-guidance mechanism to recover small details with increased precision. This comprehensive approach addresses both stripe noise and non-uniformity, offering an easy, efficient, and fast technique for image correction. A comprehensive set of experiments, which involves comparisons with state-of-the-art algorithms, serves to substantiate the effectiveness and superior performance of the proposed method in real-world remote sensing and infrared images. Various examples, encompassing both real and artificial noise, are presented to showcase the robustness and applicability of our approach.

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New generation VNIR/SWIR/TIR airborne imaging spectrometer

Cited by 18 publications

References 2 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

Effective three-step method for efficient correction of stripe noise and non-uniformity in infrared remote sensing images

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