New remote sensing methods based on narrow-band hyperspectral instruments enable the estimation of vegetation biophysical parameters and leaf biochemicals used to detect nutritional and water stress condition. This paper summarizes new advances in hyperspectral and thermal remote sensing for vegetation monitoring through biochemical and biophysical parameter estimation, discussing the potential...

Attempts to estimate photosynthetic rate or gross primary productivity from remotely sensed absorbed solar radiation depend on knowledge of the light use efficiency (LUE). Early models assumed LUE to be constant, but now most researchers try to adjust it for variations in temperature and moisture stress. However, more exact methods are now required. Hyperspectral remote sensing offers the possi...

Traditional ground based monitoring of mining activities are no longer acceptable or sometimes sufficient in terms of assessing sustainable business practices. Remote sensing is increasingly influencing the changing social and management processes of mineral extractions. This study focuses on the application of hyperspectral remote sensing to monitor water and vegetation pollutions at the Dexin...

Two hуperspectral remote sensing techniques, spectral reflectance and chlorophyll fluorescence, were used for the identification of biotic stress (sharka disease) in plum trees at an early stage without visible symptoms on the leaves. The research was focused on cultivars that are widely spread in Bulgaria: ‘Angelina’, ‘Black Diamond’ and ‘Mirabelle’. Hyperspectral reflectance and fluorescence ...

Existing atmospheric correction algorithms for multichannel remote sensing of ocean color from space were designed for retrieving water-leaving radiances in the visible over clear deep ocean areas and cannot easily be modified for retrievals over turbid coastal waters. We have developed an atmospheric correction algorithm for hyperspectral remote sensing of ocean color with the near-future Coas...

Time-lapse hyperspectral imaging technology has been used for various remote sensing applications due to its excellent capability of monitoring regions of interest over a period of time. However, large data volume of fourdimensional hyperspectral imagery demands for massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectra...

The design and use of hyperspectral imaging remote sensing systems involve the selection of a large number of parameters in particular due to the richness of the data. Many of these parameters interrelate in their effect on system performance. The tasks of optimizing parameter values that one has control over and understanding the impact of those that are uncontrollable are important in system ...

In this paper we study the application of bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of bayesian networks as: Naive Bayes, Tree Augmented Naive Bayes, Forest Augmented Naive Bayes and General Bayesian Networks, are applied in the classification of hyperspectral data. In addition, several bayesian multi-net models are applied in th...

Hyperspectral image processing has been a very dynamic area in remote sensing and other applications in recent years. Hyperspectral images provide ample spectral information to identify and distinguish spectrally similar materials for more accurate and detailed information extraction. Wide range of advanced classification techniques are available based on spectral information and spatial inform...

Classification of heterogeneous classes present in the Hyperspectral image is one of the recent research issues in the field of remote sensing. This work presents a novel technique that classifies Hyperspectral images that contain number of classes by making use of the image moments. Recently, researchers have introduced a number of neural network models and structured output based methods for ...