Fourier transform imaging spectrometers (FTISs) are widely used in global hyperspectral remote sensing due to the advantages of high stability, high throughput, and high spectral resolution. Spectrum reconstruction (SpecR) is a classic problem of FTISs determining the acquired data quality and application potential. However, the state-of-the-art SpecR algorithms were restricted by the length of maximum optical path difference (MOPD) of FTISs and apodization processing, resulting in a decrease in spectral resolution; thus, the applications of FTISs were limited. In this study, a deep learning SpecR method, which directly learned an end-to-end mapping between the interference/spectrum information with limited MOPD and without apodization processing, was proposed. The mapping was represented as a fully connected U-Net (FCUN) that takes the interference fringes as the input and outputs the highly precise spectral curves. We trained the proposed FCUN model using the real spectra and simulated pulse spectra, as well as the corresponding simulated interference curves, and achieved good results. Additionally, the performance of the proposed FCUN on real interference and spectral datasets was explored. The FCUN could obtain similar spectral values compared with the state-of-the-art fast Fourier transform (FFT)-based method with only 150 and 200 points in the interferograms. The proposed method could be able to enhance the resolution of the reconstructed spectra in the case of insufficient MOPD. Moreover, the FCUN performed well in visual quality using noisy interferograms and gained nearly 70% to 80% relative improvement over FFT for the coefficient of mean relative error (MRE). All the results based on simulated and real satellite datasets showed that the reconstructed spectra of the FCUN were more consistent with the ideal spectrum compared with that of the traditional method, with higher PSNR and lower values of spectral angle (SA) and relative spectral quadratic error (RQE).

The Bidirectional Reflectance Distribution Function (BRDF) is a critical spatial distribution parameter in the quantitative research of remote sensing and has a wide range of applications in radiometric correction, elemental inversion, and surface feature estimation. As a new means of BRDF modeling, UAV push-broom hyperspectral imaging is limited by the push-broom imaging method, and the multi-angle information is often difficult to obtain. In addition, the random variation of solar illumination during UAV low-altitude flight makes the irradiance between different push-broom hyperspectral rows and different airstrips inconsistent, which significantly affects the radiometric consistency of BRDF modeling and results in the difficulty of accurately portraying the three-dimensional spatial reflectance distribution in the UAV model. These problems largely impede the application of outdoor BRDF. Based on this, this paper proposes a fast multi-angle information acquisition scheme with a high-accuracy BRDF modeling method considering illumination variations, which mainly involves a lightweight system for BRDF acquisition and three improved BRDF models considering illumination corrections. We adopt multi-rectangular nested flight paths for multi-gray level targets, use multi-mode equipment to acquire spatial illumination changes and multi-angle reflectivity information in real-time, and introduce the illumination correction factor K through data coupling to improve the kernel, Hapke, and RPV models, and, overall, the accuracy of the improved model is increased by 20.83%, 11.11%, and 31.48%, respectively. The results show that our proposed method can acquire multi-angle information quickly and accurately using push-broom hyperspectral imaging, and the improved model eliminates the negative effect of illumination on BRDF modeling. This work is vital for expanding the multi-angle information acquisition pathway and high-efficiency and high-precision outdoor BRDF modeling.

The lunar spectrum has been used in radiometric calibration and sensor stability monitoring for spaceborne optical sensors. A ground-based large-aperture static image spectrometer (LASIS) can be used to acquire the lunar spectral image for lunar radiance model improvement when the moon orbits over its viewing field. The lunar orbiting behavior is not consistent with the desired scanning speed and direction of LASIS. To correctly extract interferograms from the obtained data, a translation correction method based on image correlation is proposed. This method registers the frames to a reference frame to reduce accumulative errors. Furthermore, we propose a circle-matching-based approach to achieve even higher accuracy during observation of the full moon. To demonstrate the effectiveness of our approaches, experiments are run on true lunar observation data. The results show that the proposed approaches outperform the state-of-the-art methods.

Against the background of the ongoing atmospheric warming, the glacial lakes that are nourished and expanded in High Mountain Asia pose growing risks of glacial lake outburst floods (GLOFs) hazards and increasing threats to the downstream areas. Effectively extracting the area and consistently monitoring the dynamics of these lakes are of great significance in predicting and preventing GLOF events. To automatically extract the lake areas, many deep learning (DL) methods capable of capturing the multi-level features of lakes have been proposed in segmentation and classification tasks. However, the portability of these supervised DL methods need to be improved in order to be directly applied to different data sources, as they require laborious effort to collect the labeled lake masks. In this work, we proposed a simple glacial lake extraction model (SimGL) via weakly-supervised contrastive learning to extend and improve the extraction performances in cases that lack the labeled lake masks. In SimGL, a Siamese network was employed to learn similar objects by maximizing the similarity between the input image and its augmentations. Then, a simple Normalized Difference Water Index (NDWI) map was provided as the location cue instead of the labeled lake masks to constrain the model to capture the representations related to the glacial lakes and the segmentations to coincide with the true lake areas. Finally, the experimental results of the glacial lake extraction on the 1540 Landsat-8 image patches showed that our approach, SimGL, offers a competitive effort with some supervised methods (such as Random Forest) and outperforms other unsupervised image segmentation methods in cases that lack true image labels.

哈达玛变换光谱成像技术（Hardmard Transfer Imaging Spectrometer, HTIS）是一种在不牺牲分辨率的情况下，通过多通道复用的方法增加光学系统光通量，使得系统信噪比显著高于传统成像的新型计算光谱成像技术。本文针对系统成像过程中数字微镜器件（Digtial Micromirror Devices，DMD）衍射造成的图像降质问题，建立了基于标量衍射理论的哈达玛编码光谱成像图谱数据退化模型，提出基于Lucy-Richard算法为核心的重构数据修正算法，提高重构数据立方体的图像质量和光谱精度。通过对成像过程的模拟和重构算法的仿真实验，一方面证明系统成像衍射降质是造成重构数据失真的主要原因，另一方面验证了本文所采用修正方法的有效性。经过L-R修正之后的复原数据立方体的光谱角距离评价结果为0.1296，图像相似度评价因子优于0.85，相比修正前的重构数据质量有较大提升，表明算法对哈达玛编码光谱成像数据的重构及修正具有较好的效果。

The accuracy and consistency of Earth observation (EO) instrument radiometric calibration is a fundamental prerequisite for achieving accurate results and delivering reliable predictions. Frequent calibration and validation (Cal/Val) activities are needed during the instrument’s lifetime, and this procedure is often extended to historical archives. Numerous satellites in orbit and proposed future missions have incorporated lunar observation into their vicarious calibration components over recent years, facilitated by the extreme long-term photometric stability of the Moon. Since the birth of the first lunar calibration reference model, lunar-dependent calibration techniques have developed rapidly, and the application and refinement of the lunar radiometric model have become a welcome research focus in the calibration community. Within the context of the development of lunar observation activities and calibration systems globally, we provide a comprehensive review of the activities and results spawned by treating the Moon as a reference for instrument response and categorize them against the understanding of lunar radiometric reference. In general, this appears to be a process of moving from data to instruments, then back into data, working towards a stated goal. Here we highlight lunar radiometric models developed by different institutions or agencies over the last two decades while reporting on the known limitations of these solutions, with unresolved challenges remaining and multiple lunar observation plans and concepts attempting to address them from various perspectives, presenting a temporal development. We also observe that the methods seeking uncertainty reduction at this stage are rather homogeneous, lacking the combination of approaches or results from lunar surface studies conducted by many spacecraft missions, and joint deep learning methods to extract information. The factors that influence the accuracy of the measurement irradiance may be regulated when practical models arrive. As a central element in lunar calibration, the development of an absolute radiometric datum helps to better understand the Earth system.