LabCompass

Kang Ni

Engineering · Purdue University West Lafayette

Mid career · publishing since 2006Rising activity

Publications

60

Citations

312

Est. group size

Recurring co-author estimate

Active years

21

Publishing since 2006

Research summary
AI-generated

Kang Ni's research focuses on developing machine learning and deep learning methods for analyzing remote sensing images, including satellite radar (SAR) and hyperspectral data, to classify land cover, detect targets, and map terrain features. Much of the work involves designing specialized neural network architectures (such as transformers and graph networks) that combine information from multiple sensor types, like optical, radar, and LiDAR data. This research supports applications in environmental monitoring, land use mapping, and object detection from aerial or satellite imagery.

Synthetic Aperture Radar (SAR) image analysisHyperspectral and multi-sensor data fusionRemote sensing scene and land cover classificationDeep learning architectures (transformers, graph networks)Target detection and recognition in satellite imagery

Publication output has grown substantially over the last decade, rising from just a few papers per year before 2022 to a peak of 14 in 2024, with continued strong activity in 2025.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence
Publications per year over the last 10 years — averaging 7.8/year recently
2017: 1 publication172018: 2 publications182019: 7 publications192020: 1 publication202021: 3 publications212022: 6 publications222023: 8 publications232024: 14 publications14242025: 9 publications252026: 2 publications26
Recent publications
Publishes in
  • IEEE Geoscience and Remote Sensing Letters×8
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing×5
  • IEEE Transactions on Geoscience and Remote Sensing×5
  • IEEE Transactions on Aerospace and Electronic Systems×4
  • Remote Sensing×3
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This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 20, 2026.

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