Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

Xin Gao, Sundaresh Ram, Rohit C. Philip, Jeffrey J. Rodríguez, Jeno Szep, Sicong Shao, Pratik Satam, Jesús Pacheco, Salim Hariri

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.

Original languageEnglish (US)
Article number255
JournalRemote Sensing
Issue number2
StatePublished - Jan 1 2022


  • Machine learning
  • Post-processing
  • Segmentation
  • Vehicle detection
  • Wide-area aerial imagery

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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