Remote Sensing Methods for the Retrieval of Inventory and Bioproductivity Parameters of Forests Using High Resolution Satellite Images

Abstract

A method for automated processing high spatial resolution satellite images is proposed to retrieve inventory and bioproductivity parameters of forest stands. The method includes effective learning classifiers, inverse modeling, and regression modeling of the estimated parameters. Spectral and texture features are used to classify forest species. The results of test experiments for the selected area of Savvatievskoe forestry (Russia, Tver region) are presented. Accuracy estimates obtained using ground-based measurements demonstrate the effectiveness of using the proposed techniques to automate the process of updating information for the State Forest Inventory program of Russia.

Probably-Statistical Method for Written Signs Recognition Using the Measure of Proximity

“…If there are no predictions, then the choice of a distribution approximation mode more adequate to the task is carried on the basis of the results of the exam for two types of modes, comparing the number of errors and refusals, the number of modes and their characteristics. In particular, in the observations on the recognition of handwritten figures in the space of natural descriptions, the situation corresponded to the second mode of partitioning, which may be explained by the non-use of neutralizing transformations at the input in these observations [16][17][18][19][20].…”