To enhance the efficiency of machine-learning algorithms of optical remote sensing imagery processing, optimization techniques are evolved of the land surface objects pattern recognition. Different methods of supervised classification are considered for these purposes, including the metrical classifier operating with Euclidean distance between any points of the multi-dimensional feature space given by registered spectra, the K-nearest neighbors classifier based on a majority vote for neighboring pixels of the recognized objects, the Bayesian classifier of statistical decision making, the Support Vector Machine classifier dealing with stable solutions of the mini-max optimization problem and their different modifications. We describe the related techniques applied for selected test regions to compare the listed classifiers.

Optical remote sensing data processing is proposed for the airborne images of high spectral and spatial resolution. Optimization techniques are undertaken to gain information about spatial distribution of the pixels on the hyperspectral images and the texture of the forest stands of different species and ages together with reducing redundancy of the spectral channels used. The category of neighborhood of pixels for particular forest classes and the step up method of selecting optimal spectral channels are employed in the relevant processing procedures. We present examples of pattern recognition for the forests as a result of separating pixels, which characterize the sunlit tops, shaded space and intermediate cases of the Sun illumination conditions on the hyperspectral images.

This paper describes an approach of machine-learning pattern recognition procedures for the land surface objects using their spectral and textural features on remotely sensed hyperspectral images together with the biological parameters retrieval for the recognized classes of forests. Modified Bayesian classifier is used to improve the related procedures in spatial and spectral domains. Direct and inverse problems of atmospheric optics are solved based on modeling results of the projective cover and density of the forest canopy for the selected classes of forests of different species and ages. Applying the proposed techniques to process images of high spectral and spatial resolution, we have detected object classes including forests within their contours on a particular image and can retrieve the phytomass amount of leaves/needles as well as the relevant total biomass amount for the forest canopy.

New approaches to the processing of airborne hyperspectral images are implemented in order to develop emerging applications based on high-performance computing resources. The focus is on solving the problem of recognizing forest vegetation of different species composition and age based on high spectral and spatial resolution airborne sensing data. Examples of the formation of information layers of recorded spectra for “pure species” of pine and birch forests are given with the selection of illuminated and shaded pixels, which increases the accuracy of recognition of objects in the processing of these images.

The application of hyperspectral remote sensing of high spatial resolution is compared to conventional ground-based forest surveys on sample plots and is considered as a possible alternative...

The main stages of the development of technologies for natural and anthropogenic object recognition (cognitive technologies for optical image processing) using remote sensing data are considered together with computational procedures for atmospheric correction of multispectral and hyperspectral air-space images. The main focus is on recognizing forest ecosystems of various species and age, based on inflight testing of domestic hyperspectral equipment for a selected test area, where ground-based forest inventory and other observations were carried out. High accuracies of the recognition of separate gradations of ages for the selected pure birch and pine stands are revealed using elaborated software for airborne hyperspectral image processing.