Due to the increasing demand and use of digital spatial information, the interest is being drawn to the generation of digital orthophotos, which is the most accurate and reliable source of spatial information. The subject of this project is the generation of orthoimages as well as the further processing of these orthoimages for the full exploitation of their spectral and spatial information. Th...

The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWEL's novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultane...

Many applications of remote sensing – like, for example, urban monitoring – require high resolution data for a correct determination of object geometry. These spatial high resolution image data contain often limited spectral information (e.g. three band RGB orthophotos). This poor spectral information lead often to classification errors between visible similar classes like water, dark pavements...

Given only a portion of an arbitrary finite-energy [L(2) (-infinity, infinity)] image and a portion of that image's spectrum, we present a closed-form method by which the entire image can be reconstructed. The algorithm is a basic augmentation of a recently proposed iterative restoration algorithm presented by Stark et al. [J. Opt. Soc. Am. 71, 635 (1981); Opt. Lett. 6, 259 (1981)]. Experimenta...

Hyperspectral image contains fine spectral and spatial resolutions for generating accurate land use and land cover maps. Supervised classification is the one of method used to exploit the information from the hyperspectral image. The traditional supervised classification methods could not be able to overcome the limitations of the hyperspectral image. The multiple classifier system (MCS) has th...

Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regula...

The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity ba...

Recent advances in satellite and airborne sensors make spatial and multispectral high-resolution imagery effortlessly available. These advances give the chance to address and solve some old problems related to the poor spatial or spectral resolution; such as the lack of details for certain features or the inability of the traditional classifiers to detect some land cover types due to the missin...