Hyperspectral imaging

Remote Sensing

Topics Hyperspectral Image Restoration Hyperspectral Multi-sensor Data Fusion Spatial Information Modelling Image classification in hyperspectral images Hyperspectral Image Restoration Despite advances in sensor technology, hyperspectral (HS) images are inevitably degraded by noise and blur, which can affect information retrieval and content interpretation. Using denoising and deblurring as a preprocessing tool will improve various post-processing tasks, e.g. classification, target detection, unmixing, etc. We propose a novel restoration algorithm for HS images. Our method first uses PCA to decorrelate the HS images and separate the information content from the noise. The first k PCA channels contain most information of the HS image, and the remaining B ? k PCA channels (where B is the number of spectral bands of HS image) mainly contain noise. If deblurring is performed on these noisy and high-dimensional B ? k PCs, then it will amplify the noise of the data cube and cause high computational cost in processing the data, which is undesirable.

Foreword to the special Issue on Hyperspectral remote sensing and imaging spectroscopy

The twenty six papers in this special issue focus on the technologies of hyperspectral remote sensing (HRS)and imaging spectroscopy. HRS has emerged as a powerful tool to understand phenomena at local and global scales by virtue of imaging through a …

Taking Optimal Advantage of Fine Spatial Information: Promoting partial image reconstruction for the morphological analysis of very-high-resolution images

Morphological Attribute Profiles With Partial Reconstruction