Methane, a powerful greenhouse gas, significantly influences climate change. Estimating methane emission fluxes is critical for implementing emission reduction strategies, and plumes segmentation is an important technique for accurate quantification. However, existing methane plumes segmentation studies focus on pixel-level localization of spatial and spectral features, which makes it difficult to capture global contextual information of remote sensing images. To tackle these challenges, we propose a novel frequency and spatial domain injection network (FSDINet) for methane plumes segmentation. FSDINet introduces three blocks that process spectral features utilizing the frequency domain to capture global contextual information and supplement local information with pixel values in the spatial domain, thus allowing for precise localization and segmentation. We conduct extensive experiments on hyperspectral and multispectral datasets. On the Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) hyperspectral dataset, the majority of our model evaluation scores exceed those of existing methods in a range of conditions, including both strong and weak plumes, with the highest precision and intersection over union (IOU) of 87.75% and 65.56%, respectively. On the WorldView-3 multispectral dataset, precision and IOU improved up to 8.76% and 4.04% over methods utilizing spatial and spectral features. Furthermore, the zero-shot experiments on the Earth Surface Mineral Dust Source Investigation (EMIT) dataset confirm the robust generalization capacity of our model when confronted with different sensor data.

To cope with global climate change and monitor the concentration distribution of CO2 and CH4 in the troposphere, the greenhouse gases monitoring instrument (GMI) carried by the GF5-02 satellite was successfully launched on September 7, 2021. GMI utilizes spatial heterodyne spectroscopy techniques to acquire interferogram images. During the acquisition process, interferogram images are inevitably corrupted by various noises, such as defective pixels, baseline drifts and random noise, which will greatly degrade the quality of recovered spectra and further reduce the retrieval accuracy of greenhouse gases. In this article, we model the mixed noise removal of interferogram images as an inverse problem and propose a novel optimization framework by integrating the robust principal component analysis (RPCA), unidirectional total variation (UTV), and penalized least squares (PLS) together. Specifically, the low-rankness of interference fringes and sparsity of defective pixels are both considered in the RPCA model, and the directionality of interference fringes is enhanced by the UTV model, while the PLS model guarantees the fidelity and smoothness of the fitting baselines. An efficient alternating direction method of multipliers procedure is designed to solve the proposed framework. A number of experiments were conducted to illustrate the superior performance of the proposed framework in terms of visual quality and quantitative assessment, as compared with the state-of-the-art algorithms.

Oblivious RAM (ORAM) conceals memory access pattern by translating a single read/write operation into the accesses to a set of randomized locations. The obliviousness is achieved by adding redundancy to the memory system, which comes at the expense of increased performance overhead. In memory systems, locality has always been a critical factor, as accessing the data with temporal or spatial locality would result in a performance gain. Although the two design considerations of obliviousness and locality may seem contradictory at first glance, combining them in a unified design can potentially hide long memory access latency in ORAM without sacrificing provable data security.This paper presents LolliRAM, a cross-layer design to exploit data locality in Oblivious RAM. LolliRAM optimizes ORAM system through two different layers: (i) the data structure in ORAM, and (ii) the fast and secure cache in ORAM controller. The reuse of redundant memory accesses at the layer of data structure in ORAM can effectively reduce memory footprints. Both temporal and spatial locality can be exploited through the elastic grouping of blocks and the optimization at the layer of ORAM controller. We conduct a set of experiments using realistic workloads that are generated from standard benchmarks. Experimental results show that LolliRAM can reduce access latency by 71.57% on average (up to 81.50%) with negligible space overhead in comparison with representative schemes.

The Greenhouse Gas Monitoring Instrument (GMI) is a short-wavelength infrared (SWIR) hyperspectral-resolution spectrometer onboard the Chinese satellite GaoFen-5 that uses a spatial heterodyne spectroscopy (SHS) interferometer to acquire interferograms. The GMI was designed to measure and study the source and sink processes of carbon dioxide and methane in the troposphere where the greenhouse effect occurs. In this study, the processing and geometric correction algorithms of the GMI Level 1 product (radiance spectrum) are introduced. The spectral quality and greenhouse gas (GHG) inversion ability of the Level 1 products are analyzed, and the results illustrate that the specifications meet the mission's requirements. An initial evaluation of the resolution, signal-to-noise ratio (SNR), and stability of the radiance spectrum reveals that the overall function and performance are within the design objectives. A comparison between our Level 1 products and the theoretical spectrum shows that the root mean square (rms) of the residual is approximately 0.8%, and the Level 1 products of the GMI captured within five months after observations have good spectral stability characteristics (less than 0.005 cm-1for Band 1, 0.003 cm-1for Band 2, 0.002 cm-1for Band 3, and 0.004 cm-1for Band 4). These results demonstrate that the GMI payload and the processing algorithm all work well and reliably. Furthermore, based on the Level 1 products, a GHG retrieval experiment is carried out, and the results are compared with data from Total Column Carbon Observing Network (TCCON) stations. The initial comparison of the XCO2results yields a value of 0.869 for R2(goodness of fit), 0.51 ppm for bias (mean of absolute error), and 0.53 ppm for σ (standard deviation of error). Similarly, the XCH4comparison yields values of 0.841 for R2, 4.64 ppb for bias, and 4.66 ppb for σ.