TY - JOUR
AU - Asokan, Anju
AB - Enhancing the textural details in a satellite image play a significant part in satellite image processing. Satellite images are rich in textural and very minute spatial data. Failing to retrieve and enhance these critical details in the satellite image can lead to loss of information and hence poor results in the succeeding stages of satellite image processing. Correctly identifying the spatial and textural data in a satellite image is an effective way by which the image information can be preserved for a better-quality image. For this, the textural details should be distinguished, and then effective image processing can be performed. This paper introduces the Gabor filter-based parameter optimization for enhancing the textural and spatial information in the image. Manta ray foraging optimization is adopted for modifying the control parameters in the filter to account for the inadequacy of the algorithm in balancing the local and global search. A self adaptable Manta ray optimization is proposed, which is shown to outperform the traditional enhancement techniques such as Bilateral filter and Gabor filter optimized with Particle Swarm Optimization (PSO), Differential Evolution(DE) and Manta Ray Foraging Optimization(MRFO). The proposed method is compared with the traditional methods in terms of parameters such as Peak Signal to Noise Ratio (PSNR), Feature Similarity Index (FSIM), Entropy and Computation time. The proposed method gave a huge improvement in PSNR by 17.61%, FSIM by 7.47% and entropy values by 6.7% and was seen to give the least CPU time.
TI - A self-adaptable Manta ray optimized Gabor filter for satellite image enhancement
JF - Earth Science Informatics
DO - 10.1007/s12145-023-00963-3
DA - 2023-06-01
UR - https://www.deepdyve.com/lp/springer-journals/a-self-adaptable-manta-ray-optimized-gabor-filter-for-satellite-image-QXyA5Rokq2
SP - 1503
EP - 1517
VL - 16
IS - 2
DP - DeepDyve
ER -