A review of applying second-generation wavelets for noise removal from remote sensing data.

The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed based on a method called the lifting scheme, is almost a new version of wavelets, and its application in the remote sensing field is fresh. Although first-generation wavelets have been proven to offer effective techniques for processing remotely sensed data, second-generation wavelets are more efficient in some respects, as will be discussed later. The aim of this review paper is to examine all existing studies in the literature related to applying second-generation wavelets for denoising remote sensing data. However, to make a better understanding of the application of wavelet-based denoising methods for remote sensing data, some studies that apply first-generation wavelets are also presented. In the part of hyperspectral data, there is a focus on noise removal from vegetation spectrum

Simulating multi-directional narrowband reflectance of the earth's surface using ADAM (a surface reflectance database for ESA's earth observation missions)

International audienceThe ADAM (A Surface Reflectance Database for ESA's Earth Observation Missions) product (a climatological database coupled to its companion calculation toolkit) enables users to simulate realistic hyperspectral and directional global Earth surface reflectances (i.e., top-of-canopy/bottom-of-atmosphere) over the 240-4000 nm spectral range (at 1-nm resolution) and in any illumination/observation geometry, at 0.1° x 0.1° spatial resolution for a typical year. ADAM aims to support the preparation of optical Earth observation missions as well as the design of operational processing chains for the retrieval of atmospheric parameters by characterizing the expected surface reflectance, accounting for its anisotropy. Firstly, we describe (1) the methods used in the development of the gridded monthly ADAM climatologies (over land surfaces: monthly means of normalized reflectances derived from MODIS observations in seven spectral bands for the year 2005; over oceans: monthly means over the 1999-2009 period of chlorophyll content from SeaWiFS and of wind speed from SeaWinds), and (2) the underlying modeling approaches of ADAM toolkit to simulate the spectro-directional variations of the reflectance depending on the assigned surface type. Secondly, we evaluate ADAM simulation performances over land surfaces. A comparison against POLDER multi-spectral/multi-directional measurements for year 2008 shows reliable simulation results with root mean square differences below 0.027 and R2 values above 0.9 for most of the 14 land cover IGBP classes investigated, with no significant bias identified. Only for the "Snow and ice" class is the performance lower pointing to a limitation of climatological data to represent actual snow properties. An evaluation of the modeled reflectance in the specific backscatter direction against CALIPSO data reveals that ADAM tends to overestimate (underestimate) the so-called "hot-spot" by a factor of about 1.5 (1.5 to 2) for barren (vegetated) surfaces

Polarization of the Sky

International audienceBased on full-sky imaging polarimetric measurements, in this chapter we demonstrate that the celestial distribution of the angle of polarization (or E-vector direction) of skylight is a very robust pattern being qualitatively always the same under all possible sky conditions. Practically the only qualitative difference among clear, partly cloudy, overcast, foggy, smoky and tree-canopied skies occurs in the degree of linear polarization d: The higher the optical thickness of the non-clear atmosphere, the lower the d of skylight. We review here how well the Rayleigh model describes the E-vector pattern of clear and cloudy skies. We deal with the polarization patterns of foggy, partly cloudy, overcast, twilight, smoky and total-solar-eclipsed skies. We describe the possible influences of the changed polarization pattern of smoky and eclipsed skies on insect orientation. We consider the polarization of ‘water-skies’ above Arctic open waters and the polarization characteristics of fogbows. Finally, we deal with the change of skylight polarization due to the transmission through Snell’s window of the flat water surface