An Improved Bald Eagle Search Algorithm with Deep Learning Model for Forest Fire Detection Using Hyperspectral Remote Sensing Images. Issue 5 (3rd September 2022)

• Record Type: Journal Article
• Authors: Algarni, Abeer D.; Alturki, Nazik; Soliman, Naglaa F.; Abdel-Khalek, Sayed; Mousa, Abd Allah A.
• Is Part Of: Canadian journal of remote sensing. Volume 48:Issue 5(2022)
• Page Start: 609
• Page End: 620
• Publication Date: 2022-09-03
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2022.2077709
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: This paper presents an improved Bald Eagle Search Algorithm with Deep Learning model for forest fire detection (IBESDL-FFD) technique using hyperspectral images (HSRS). The major intention of the IBESDL-FFD technique is to identify the presence of forest fire in the HSRS images. To achieve this, the IBESDL-FFD technique involves data pre-processing in two stages namely data augmentation and noise removal. Besides, IBES algorithm with NASNetLarge method was utilized as a feature extractor to determine feature vectors. Finally, Firefly algorithm (FFA) with denoising autoencoder (DAE) is applied for the classification of forest fire. The design of IBES and FFA techniques helps to adjust optimally the parameters contained in the NSANetLarge and DAE models respectively. For demonstrating the better outcomes of the IBESDL-FFD approach, a wide-ranging simulation was implemented and the outcomes are examined. The results reported the better outcomes of the IBESDL-FFD technique over the existing techniques with maximum average accuracy of 93.75%.