Landcover change detection using PSO-evaluated quantum CA approach on multi-temporal remote-sensing watershed images

Research output: Chapter in Book/Report/Conference proceedingBook chapter


Computer science plays a major role in image segmentation and image processing applications. Despite the computational cost, PSO evaluated QCA approaches perform comparable to or better than their crisp counterparts. This novel approach, proposed in this chapter, has been found to enhance the functionality of the CA rule base and thus enhance the established potentiality of the fuzzy-based segmentation domain with the help of quantum cellular automata. This new unsupervised method is able to detect clusters using 2-dimensional quantum cellular automata model based on PSO evaluation. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase, it utilizes a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. The authors experiment on Tilaya Reservoir Catchment on Barakar River. The clustered regions are compared with well-known PSO, FCM, and k-means methods and also with the ground truth knowledge. The results show the superiority of the new method.


  • Kalyan Mahata
  • Rajib Das
  • Subhasish Das
  • Anasua Sarkar
External organisations
  • Government College of Engineering & Leather Technology, Kolkata
  • Jadavpur University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Image Processing
  • Radiology, Nuclear Medicine and Medical Imaging
Original languageEnglish
Title of host publicationEnvironmental Information Systems
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
Place of PublicationHershey PA, USA
PublisherIGI Global
Number of pages27
ISBN (Electronic)9781522570349
ISBN (Print)9781522570332, 1522570330
Publication statusPublished - 2018 Jan 1
Publication categoryResearch
Externally publishedYes