Comparison Analysis of Recognition Algorithms of Forest-Cover Objects on Hyperspectral Air-Borne and Space-Borne Images

Izvestiya, Atmospheric and Oceanic Physics - The basic model for the recognition of natural and anthropogenic objects using their spectral and textural features is described in the problem of...     

Mapping forest and peat fires using hyperspectral airborne remote-sensing data

The characteristic features of airspace hyperspectral remote sensing (RS) are considered in order to develop classification techniques for relevant images. Currently available approaches to constructing classifiers (computational procedures) are described for recognizing natural and anthropogenic objects in hyperspectral images. We confirm that the methods under development are effective enough with the reduced dimensionality of the feature space of original spectra and the decreased sample volumes in supervising procedures for the selected object classes. Data from joint hyperspectral and aerial photography provide examples of the spectral distributions smoke of different intensities from forest and peat fires in the presence and absence of fire sources, for the smoke coverage of water surfaces, and for the forest vegetation without ignition sources within a selected area. The results obtained in the supervising procedures are used for pattern recognition and scene analysis in airborne images obtained for the test areas during forest-fire season.     

Identification of Forest Vegetation Using Airborne Hyperspectral Data

Izvestiya, Atmospheric and Oceanic Physics - Computational schemes for forest vegetation identification on airborne hyperspectral images are implemented using the feature of the red edge in the...     

Pattern Recognition of Forest Vegetation from Hyperspectral Airborne and Multichannel Satellite Data of High Spatial Resolution: Comparison of Results and Estimation of Their Accuracy

Izvestiya, Atmospheric and Oceanic Physics - The pattern recognition of a forest surface from remote sensing data (both airborne hyperspectral data and WorldView-2 multichannel satellite data of...     

Models of Pattern Recognition and Forest State Estimation Based on Hyperspectral Remote Sensing Data

Izvestiya, Atmospheric and Oceanic Physics - Model applications of airborne hyperspectral remote sensing data for the recognition of forest stand objects and parameterization of the environmental...     

Evaluation of the Species Composition and the Biological Productivity of Forests Based on Remote Sensing Data with High Spatial and Spectral Resolution

Izvestiya, Atmospheric and Oceanic Physics - The application of hyperspectral remote sensing of high spatial resolution is compared to conventional ground-based forest surveys on sample plots and...     

Vegetation pattern recognition using hyperspectral air sounding data

For the problem of pattern recognition of natural and man-made objects using remote hyperspectral imaging data, we propose an approach that is based on both the criterion of a minimal Euclidean distance relative to spectra of some reference objects and specific features of wavelength shift of the area of transition from the chlorophyll absorption band to the spectral reflectivity maximum that is characteristic to vegetation. The database of this pattern-recognition method is constructed on the basis of pixel radiance histograms for particular spectral channels. The histogram in the maximum separability wavelength of object classes characteristic of the chosen test area is divided into a certain number of spectral intervals, which are grouped with respect to the above-mentioned shift. Using computational techniques for separating out these spectral groups, we point to new possibilities in the recognition of different vegetation types with the help of high-resolution spatial and spectral air sounding data.