Editorial to Special Issue “Remote Sensing Image Denoising, Restoration and Reconstruction”

publishedVersionNon peer reviewe

Image Restoration for Remote Sensing: Overview and Toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS

Efficient pre-processing and retrieval of reflectance using calibration modules for Hyperspectral satellite data (Hyperion) and denoising of Hyperspectral reference spectra using wavelet based Adaptive Bilateral Filtering (HABF) -A case study on mangroves forest in Muthupet lagoon, Tamil Nadu

1619-1626The article invoves in the preprocessing of hyperion data, and denoising of hyperspectral reference spectral library, which is generated from the spectro-radiometer. The atmospheric correction module like Fast Line-of-sight of Atmospheric Analysis of Spectral Hypercubes provided the smooth absolute spectral profile. In connection to that, the Hybrid Adaptive Bilateral Filter (HABF) is proposed for denoising of field based spectral library, and laboratory based spectral library. To implement the proposed algorithm, spectral library of mangrove has been exploited, which is collected from Muthupet mangrove forest, and spectra of particular species is generated from Field-spectro-radiometer with wavelength ranges from 350 nm to 2500 nm, and 10 nm band width. This spectral library has been used as an input signal since it contains noise across wavelength ranges from 350 – 450 nm, 1000 – 1200 nm, and 2000 – 2500 nm due to atmospheric conditions. These noises can be removed effectively by the proposed wavelet based HABF techniques and conventional method of denoising. The performance of each method was compared with performance evaluation parameter such as PSNR and MS