Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

多周波ステップレーダにおける目標検出のための信号処理に関する研究

電気通信大学202

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Spatio-temporal trends for long-lasting contemporary snow in Lesotho : implications for human and livestock vulnerability

M.Sc., Faculty of Science, University of the Witwatersrand, 2011Prolonged snow cover in Lesotho frequently results in human and livestock deaths, due to isolation and exposure. MODIS Rapid Response imagery is emerging as an important source of near real-time data for global hazard mapping and emergency response. A dataset of daily MODIS snow cover images for the period 2003 – 2010 was acquired for Lesotho. Combined with high-resolution SPOT satellite images for two study areas, MODIS snow cover images were used to establish the frequency, extent and timing of snowfalls. A digital elevation model was used in conjunction with mean air temperature data to investigate the effects of altitude, aspect and temperature on the mean rate of daily snowmelt. A strong correlation exists between mean day-time temperatures and the mean rate of daily snowmelt throughout the winter season. The mean rate of snow dissipation is most rapid after late season (September – November) snowfalls and least rapid after mid season (July – August) snowfalls. Snow cover persisting for 1 – 5 days dissipates at a higher mean rate than snow cover that has persisted for 6 – 10 days. Snow lasts longest on south-facing slopes above 2500m a.s.l, with evidence of increased ablation due to wind deflation and higher insolation levels in the highlands above 3400m a.s.l. The southern Drakensberg highlands in the district of Quthing have the highest mean duration of snow cover (21 – 25 days per annum). The seasonal extent and duration of snow cover was related to the spatial location of villages and roads in Lesotho, in order to determine individual vulnerability to negative impacts associated with prolonged snow cover. A ranking system was applied to each village according to the seasonal duration of snow cover, and the accessibility and proximity to the nearest road. Snowfalls occur between 1 and 8 times per annum on average. Therefore, village vulnerability is generally low, as most settlements are situated on predominantly north-facing slopes in the western lowlands and Senqu River Valley, which remain largely snow-free throughout the winter season. Few villages experience prolonged snow cover, which is limited to predominantly south-facing slopes above 2500m a.s.l along the escarpment and interior mountain ranges. Village vulnerability increases during the mid season period as a result of the increased frequency and duration of snow cover in July and August. The villages of Thoteng (Butha-Buthe), Letseng-la-Terae (Mokhotlong) and Mabalane (Butha-Buthe) have the highest vulnerability for the 2003 – 2010 period