Multispectral and hyperspectral imagery is used in a wide variety of remote sensing applications such as global environmental monitoring, mapping, charting and land use planning (Vaughn et al. 1995). The evolution of sensor technology has dramatically improved the quality of multispectral imagery through increase in spectral, spatial and radiometric resolutions of earth observations. This is ac...

A recently proposed method for automatic radiometric normalization of multiand hyperspectral imagery based on the invariance property of the Multivariate Alteration Detection (MAD) transformation and orthogonal linear regression is extended by using an iterative re-weighting scheme involving no-change probabilities. The procedure is first investigated with partly artificial data and then applie...

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and mu...

Subpixel detection and classification are important in identification and quantification of multicomponent mixtures in remotely sensed data, such as multispectral/hyperspectral images. A recently proposed orthogonal subspace projection (OSP) has shown some success in Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. Ho...

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types an...

Hyperspectral sensors collect hundreds of narrow and contiguously spaced spectral bands of data. Such sensors provide fully registered high resolution spatial and spectral images that are invaluable in discriminating between man-made objects and natural clutter backgrounds. The price paid for this high resolution data is extremely large data sets, several hundred of Mbytes for a single scene, t...

Several ocean color earth observation satellite sensors are presently collecting daily imagery, including the Hyperspectral Imager for the Coastal Ocean (HICO). HICO has been operating aboard the International Space Station since its installation on September 24, 2009. It provides high spatial resolution hyperspectral imagery optimized for the coastal ocean. Atmospheric correction, however, sti...

High spatial resolution multispectral/hyperspectral sensors are being developed by private industry with science/research customers as end users. With an increasingly wide range of sensor choices, it is important for the remote sensing science community and commercial community alike to understand the trade-offs between ground sample distance (GSD), spectral resolution, and signal-to-noise rati...

Our objective is to identify and map individuals of nine tree species in a Hawaiian lowland tropical forest by comparing the performance of a variety of semi-supervised classifiers. A method was adapted to process hyperspectral imagery, LiDAR intensity variables, and LiDAR-derived canopy height and use them to assess the identification accuracy. We found that semi-supervised Support Vector Mach...

Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree spe...