A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban land-cover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple compositi..

The Orbital Calibration 2 (OrCa2) CubeSat Mission

The Georgia Institute of Technology (Georgia Tech), in collaboration with the Georgia Tech Research Institute (GTRI), has developed the Orbital Calibration 2 (OrCa2) mission in an effort to improve space domain awareness. OrCa2’s external panels have precise and well-characterized reflective properties that will permit various calibration activities from ground-based optical sensors, with the goal of improving the tracking and detection of resident space objects (RSOs). OrCa2 is a 12U CubeSat designed, fabricated, assembled, and tested almost entirely in-house using GT/GTRI facilities. It will be regularly observed using Georgia Tech’s Space Object Research Telescope (GT-SORT). A number of experiments can be conducted with these measurements, such as pose estimation, validation of RSO trajectory propagations with complementary ground-based laser ranging data, multi-spectral analysis, low-light detection algorithms, and validation of atmospheric scattering models. An onboard imager will serve as both a low-accuracy star camera, as well as an on-orbit optical tracking system capable of RSO streak detection, with a mission goal of gathering simultaneous ground-based and space-borne tracking data of one or more RSOs. Additionally, the OrCa2 spacecraft will host an experimental radiation dosimeter, an experimental software defined radio (SDR) receiver, and an experimental power system. OrCa2 is currently manifested to launch in Q1 2024. An overview of the design, concept of operations, and expected outcomes of the mission will be presented

Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment

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Impact of meteorological conditions on airborne fine particle composition and secondary pollutant characteristics in urban area during winter-time

The assessment of airborne fine particle composition and secondary pollutant characteristics in the case of Augsburg, Germany, during winter (31 January–12 March 2010) is studied on the basis of aerosol mass spectrometry (3 non-refractory components and organic matter, 3 positive matrix factorizations (PMF) factors), particle size distributions (PSD, 5 size modes, 5 PMF factors), further air pollutant mass concentrations (7 gases and VOC, black carbon, PM10, PM2.5) and meteorological measurements, including mixing layer height (MLH), with one-hourly temporal resolution. Data were subjectively assigned to 10 temporal phases which are characterised by different meteorological influences and air pollutant concentrations. In each phase hierarchical clustering analysis with the Ward method was applied to the correlations of air pollutants, PM components, PM source contributions and PSD modes and correlations of these data with all meteorological parameters. This analysis resulted in different degrees of sensitivities of these air pollutant data to single meteorological parameters. It is generally found that wind speed (negatively), MLH (negatively), relative humidity (positively) and wind direction influence primary pollutant and accumulation mode particle (size range 100–500 nm) concentrations. Temperature (negatively), absolute humidity (negatively) and also relative humidity (positively) are relevant for secondary compounds of PM and particle (PM2.5, PM10) mass concentrations. NO, nucleation and Aitken mode particle and the fresh traffic aerosol concentrations are only weakly dependent on meteorological parameters and thus are driven by emissions. These daily variation data analyses provide new, detailed meteorological influences on air pollutant data with the focus on fine particle composition and secondary pollutant characteristics and can explain major parts of certain PM component and gaseous pollutant exposure