Observational data from astronomical imaging surveys contain information about a variety of source populations and environments, its complexity will increase substantially as telescopes become more sensitive. Even for existing observations, measuring the correlations between point-like diffuse emission can be crucial to correctly inferring properties any individual component. For this task is t...

Hyperspectral remote sensing (HRS) and imaging spectroscopy (IS) are the same technologies that provide detailed spectral information for individual pixels of an image. While HRS refers mostly to remote sensing (from a distance), the emerging IS technology covers wide spatial–spectral domains, frommicroscopic to macroscopic HRS/IS. IS is an innovative development of the charge-coupled device (C...

We present an analysis of spectral and spatial data of Mira obtained by the Cassini spacecraft, which not only observed the star’s spectra over a broad range of near-infrared wavelengths, but was also able to obtain high-resolution spatial information by watching the star pass behind Saturn’s rings. The observed spectral range of 1–5microns reveals the stellar atmosphere in the crucial water-ba...

It is an important way that some remote sensing images of different spatial and spectral resolutions are fused to satisfy the requirement of general application. In order to achieve a good fusion result, low spatial spectral images should be resampled. At present, nearest neighbor resampling is often adopted which has some effects on the precision of new image. In this paper, an image fusion me...

Steganography is the art of concealing the presence of a communication, across an open channel, from undesired parties. This is achieved by the encoding of message data in innocuous cover objects, such as JPEG digital images. Many JPEG steganography algorithms exist; most perform the encoding in the spectral domain of the image. A new steganographic method, deemed the Spatial-Spectral Watermark...

Intensity-Hue-Saturation (IHS) and Principal Component Analysis (PCA) are two famous methods to which image fusion algorithms can be reported when merging panchromatic (Pan) and multispectral (MS) images, acquired with different spatial and spectral resolutions. The IHS is a wellknown pan-sharpening approach widely used for its efficiency and high spatial resolution. However, it can distort the...

Remote sensing image fusion (or pan-sharpening) aims at generating high resolution multi-spectral (MS) image from inputs of a high spatial resolution single band panchromatic (PAN) image and a low spatial resolution multi-spectral image. In this paper, a deep convolutional neural network with two-stream inputs respectively for PAN and MS images is proposed for remote sensing image pan-sharpenin...

We compare the benefits of spatial and spectral priors in near-infrared diffuse tomography image reconstruction. Although previous studies that incorporated anatomical spatial priors have shown improvement in algorithm convergence and resolution, our results indicate that functional parameter quantification by this approach can be suboptimal. The incorporation of a priori spectral information s...