On multi-class aerial image classification using learning machines

Qurban A. Memon, Najiya Valappil

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)


Computer vision, pattern recognition, deep learning (DL), expert systems, cognitive computing, and the Internet of things are some of the innovations and terminologies that have sprung up as artificial intelligence (AI) has grown in popularity. Among these, computer vision is one of the innovations that allow computers to perceive and comprehend the visual world. Computers recognize and classify artifacts using digital images and DL representations. Computer vision technologies have exploded in popularity in the fields of automation and logistics. Despite these challenges, automation appears to be one of the most exciting regions for recently developed artificial intelligence solutions, primarily computer and machine vision frameworks. Amongst the most important problems in automation is the protection of human-computer and human-machine interactions, which necessitates the “explainability” of techniques, which also precludes the use of any DL-based solutions, regardless of their success in computer vision applications. To automate some aspects of the manual labor involved, robotic platforms have been created. Traditional analytic methods are used by many of the current systems. Usually, automation is not end-to-end, necessitating user involvement to transfer vials, create analytical methods for each compound, and interpret raw data. This chapter is addressing the issues involved with computer vision and recognition-based safe automated systems.

Original languageEnglish
Title of host publicationComputer Vision and Recognition Systems Using Machine and Deep Learning Approaches
Subtitle of host publicationFundamentals, technologies and applications
PublisherInstitution of Engineering and Technology
Number of pages34
ISBN (Electronic)9781839533235
Publication statusPublished - Jan 1 2021


  • Backpropagation through structure (BTS)
  • Convolutional neural network (CNN)
  • Deep learning networks
  • Generative adversarial network (GAN)
  • Recursive neural network (RNN)
  • Variational autoencoder (VAE)
  • unmanned aerial vehicles (UAV)

ASJC Scopus subject areas

  • General Computer Science


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