Remote Sensing
1) RPaviaU-DPaviaC Dataset: The RPaviaU-DPaviaC dataset is constructed by amalgamating two publicly accessible HSI datasets: the ROSIS Pavia University (RPaviaU) scene and the DAIS Pavia Center (DPaviaC) scene. The RPaviaU dataset, featuring dimensions of 610 × 340 × 103, was acquired by the ROSIS HSI sensor over the terrain of the University of Pavia, Italy. Conversely, the DPaviaC dataset, with dimensions of 400 × 400 × 72, was collected using the DAIS sensor over the central area of Pavia City, Italy. These two scenes share a common set of seven land cover classes.
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Based on the analysis of laser characteristics, we have created a simulated dataset of near-infrared images of 1064nm laser spots. The spot collection process was carried out under natural light conditions. The divergence phenomenon during laser irradiation was excluded because the emitted 1064nm laser spots are adjustable.
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SECOND is a well-annotated benchmark for SCD. It collects bitemporal aerial images covering Hangzhou, Chengdu, Shanghai and other cities. The number of images totals 4662 pairs, of which 2968 pairs are used for training and 1694 pairs are used for testing. Each image has a size of 512 × 512 pixels with a spatial resolution from 0.5m to 3m. SENCOND provides unchanged masks and land cover masks in changed areas. It focuses on 6 common land cover classes, i.e., non-vegetated ground surface, tree, low vegetation, water, buildings and playgrounds.
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The study area on OH used a Sentinel-2B Level-2A product, acquired on December 1, 2020. At the time of imaging, the solar azimuth angle was 157.43°, and the solar altitude angle was 44.00°. The study area on VQ used a Sentinel-2A Level-2A product, acquired on January 15, 2019.
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In this dataset, a human detecting model using with UWB radar technology is presented. Two distinct datasets were created using the UWB radar device, leveraging its dual features. Data collection involved two main scenarios, each containing multiple sub-scenarios. These sub-scenarios varied parameters like the position, distance, angle, and orientation of the human subject relative to the radar. Unlike conventional approaches that rely on signal processing or noise/background removal, this study uniquely emphasizes analyzing raw UWB radar data directly.
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In this article, a novel method for interferometric target detection that employs coherent echoes and spatial frequency sampling is proposed. Originating from radio astronomy, interferometric passive microwave imaging has become widely applied for the passive microwave remote sensing of Earth. The proposed technique, termed Interferometric Coherent Echo Detection, is a novel active imaging method that capitalizes on coherent echoes to sample the spatial frequency domain.
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This is a wheat breeding phenotyping and yield dataset, including canopy height (CH, m), canopy volume (CV, m3), and leaf area index (LAI) collected in the field; vegetation index (VI) generated by multispectral data acquired by UAV remote sensing; trial site weather (Weather); and yield (Yield, kg). The data comes from field trials.
Data acquisition and processing are described in the relevant part of the manuscript.
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The Asian Subcontinent Dataset (ASCD) is a multisensor dataset that includes geospatial data collected from different countries within the Asian subcontinent, specifically India, Bangladesh, and Sri Lanka. The dataset consists of imagery captured through two main sources: Google Earth Pro software and IKONOS-2 satellite images. ASCD includes 14 distinct land cover classes, ranging from urban areas to natural environments, with 1,674 images. The number of images in each land cover class varies between 100 and 204.
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Accurate and spatiotemporal seamless soil moisture (SM) products are important for hydrological drought monitoring and agricultural water management. Currently, physically-based process models with data assimilation are widely used for global seamless SM generation, such as soil moisture active passive level 4 (SMAP L4), the land component of the fifth generation of European Reanalysis (ERA5-land) and Global Land Data Assimilation System Noah (GLDAS-Noah).
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