A genetic algorithm solution to the gram-schmidt image fusion

Yilmaz V., YILMAZ Ç., GÜNGÖR O., Shan J.

INTERNATIONAL JOURNAL OF REMOTE SENSING, cilt.41, sa.4, ss.1458-1485, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 41 Sayı: 4
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1080/01431161.2019.1667553
  • Dergi Adı: INTERNATIONAL JOURNAL OF REMOTE SENSING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1458-1485
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

There is no such thing as 'the best image fusion method' in terms of both spectral and spatial fidelity. This fact encourages the researchers to develop more advanced approaches in order to optimally transfer the spatial details without distorting the colour content. Component substitution (CS)-based image fusion methods have been proven to produce sharper images but suffer from colour distortion. The aim of this study was to modify the CS-based Gram-Schmidt (GS) fusion method with the aid of the Genetic Algorithm (GA) to further improve its colour preservation performance. The GA was used to estimate a weight for each multispectral (MS) band. The obtained band weights were used to generate a low-resolution panchromatic (PAN) band, which plays a significant role in the performance of the GS method. The performance of the proposed approach was compared not only against the conventional GS, but also against widely-used CS-based, multiresolution analysis (MRA)-based and colour-based (CB) image fusion methods. The results indicated that the proposed GA-based approach produced spectrally and spatially superior results compared to the other methods used.