Повышение эффективности текстурной сегментации лесного полога по изображениям сверхвысокого пространственного разрешения

Abstract

The issue of increasing the accuracy of tree canopy segmentation structures in the processing of very high spatial resolution satellite images by joint use of textural features extracted by different methods is considered. The TTSPCA method (Total Texture Statistics Principal Component Analysis) is proposed and its effectiveness is shown for a number of test cases. Estimates of TTSPCA effectiveness are obtained for the forest stands segmentation and growth types. The panchromatic Worldview-2 image of the test area (Bronnitskoye forestry, Moscow region) obtained in summer was used as remote data. To perform texture segmentation, in addition to TTSPCA, several standard second-order statistical methods and a spectral (energy) method based on the wavelet transform were also used. It is shown that almost all considered statistical and spectral methods provide forest stand segmentation with errors not exceeding 3–4 %. It has been established that the TTSPCA method makes it possible to reduce the probability of errors in the forest inventory zone, as well as to identify sections with a predominance of natural and artificial plantations with an accuracy of over 85 %. The results obtained can be further recommended for use in order to improve the system we are developing for joint spectral-textural processing of satellite images with different spatial resolutions.