Vegetation indices (VIs) are produced as a combination of different reflectance bands that captured by multispectral images (MSIs). These indices, such normalized difference vegetation index (NDVI), reported to be proxy indicators photosynthetic activity, plant canopy biomass, and leaf area index. To determine the utility using VI derived from MSI model growth, random regression (RR) models wit...

This paper describes the fusion of information extracted from multispectral digital aerial images for land use classification. The proposed approach integrates spectral classification techniques and spatial information. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and NIR channels and form the basis for information ext...

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode ...

In this paper, we present a watermarking technique for hiding an entire image or pattern as a watermark into remotesensing images. In our technique, the Karhunen Loève Transform (KLT) is preliminarily applied on the multispectral image. The most principal component image is used for embedding with a watermark. The embedding process is performed in a middlefrequency subband resulted by a two-dim...

To verify the features of an object or an area, there is a need to merge panchromatic images with high spatial resolution and multispectral images with high spectral resolution. Standard fusion methods like IHS transform are not satisfactory, because they distort spectral characteristics of the multispectral data. In wavelet methods, the aim is to decompose the image into subbands, which have t...

Earth observing satellites usually not only take ordinary red-green-blue images, but provide several images including the near-infrared and infrared spectrum. These images are called multispectral, for about four to seven different bands, or hyperspectral, for higher dimensional images of up to 210 bands. The drawback of the additional spectral information is that each spectral band has rather ...

In this comparative study, the Bayesian and a neural network (the HLVQ) approaches are used to classify multispectral LANDSAT images. The studied area contains several agricultural classes (wheat, flax,....). Some classes are found to be non homogeneous and thus are divided in this study into several subclasses. The Gaussian assumption needed by the Bayesian classifier is thus justified by this...