Title

Multispectral Satellite Image Denoising Via Adaptive Cuckoo Search-Based Wiener Filter

Keywords

2-D finite-impulse response (FIR) Wiener filter; adaptive cuckoo search (ACS) algorithm; metaheuristic optimization algorithms; satellite image denoising

Abstract

Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well.

Publication Date

8-1-2018

Publication Title

IEEE Transactions on Geoscience and Remote Sensing

Volume

56

Issue

8

Number of Pages

4334-4345

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TGRS.2018.2815281

Socpus ID

85045341797 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85045341797

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