Deep learning approaches to object detection have achieved reliable detection of specific object classes in images. However, extending a model’s detection capability to new object classes requires large amounts of annotated training data, which is costly and time-consuming to acquire, especially for long-tailed classes with insufficient representation in existing datasets. We compare four distinct methods of generating synthetic data to finetune object detection models on novel object categories, particularly when limited data is available in an object-centric format (multi-view images/3D models). Our approaches are based on simple image processing techniques, 3D rendering, and image generation models, each varying in complexity and realism. We assess how our methods, which use object-centric data to synthesize realistic, cluttered images with varying contextual coherence, enable models to achieve category-level generalization in real-world data. We demonstrate significant performance boosts within this data-constrained experimental setting.

Passive microwave sounders provide the highest-impact observations ingested by major numerical weather prediction (NWP) forecast models. The Advanced Technology Microwave Sounder (ATMS), built by Northrop Grumman, Azusa, CA, USA, is the latest operational microwave sounder series being launched by the United States to provide both temperature and water vapor soundings of the atmosphere. The first ATMS was launched on the Suomi National Polar-orbiting Partnership (SNPP) satellite in 2011. This article focuses on the details of the on-orbit performance characterization of the second ATMS, which launched on November 18, 2017, on the Joint Polar Satellite System-1 (JPSS-1) satellite. After successful commissioning, JPSS-1 was renamed National Oceanic and Atmospheric Administration (NOAA)-20 (N-20). We present performance characterizations from prelaunch and postlaunch tests, including the thermal vacuum (TVAC) campaign, and postlaunch activities that contribute to the radiance data products. Significant improvements were found for reflector emissivity,$1/f$noise performance, antenna beam efficiency, interchannel noise correlation, and scan drive bearing design. New geolocation and pointing algorithms were evaluated. The N-20 ATMS has the same channel set, polarizations, scan geometry, and calibration approach as the SNPP ATMS. The N-20 ATMS meets all performance requirements with margin.

The Advanced Technology Microwave Sounder (ATMS) is a satellite-based microwave radiometer that provides temperature and humidity sounding observations from low Earth orbit. The instrument utilizes 22 channels that cover a frequency range of 23 to 183 GHz. The first ATMS instrument was launched in 2011 on the Suomi National Polar-orbiting Partnership (S-NPP) satellite and the second ATMS was launched in 2017 on the Joint Polar Satellite System-1 (JPSS-1) satellite (now NOAA-20); both on-orbit ATMS instruments are currently operational. This paper will describe the pre-launch performance of the third ATMS instrument, designated for the JPSS-2 satellite, during ground testing and calibration.