Classification of high resolution imagery based on fusion of multiscale texture features

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Liu, Jinxiu ; Liu, Huiping ; Lv, Ying ; Xue, Xiaojuan

关键词: Classification accuracy;    High resolution data;    High resolution imagery;    Image texture analysis;    Land-cover types;    Spectral band;    Textural classification;    Texture features;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012217/pdf
DOI  :  10.1088/1755-1315/17/1/012217

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In high resolution data classification process, combining texture features with spectral bands can effectively improve the classification accuracy. However, the window size which is difficult to choose is regarded as an important factor influencing overall classification accuracy in textural classification and current approaches to image texture analysis only depend on a single moving window which ignores different scale features of various land cover types. In this paper, we propose a new method based on the fusion of multiscale texture features to overcome these problems. The main steps in new method include the classification of fixed window size spectral/textural images from 3×3 to 15×15 and comparison of all the posterior possibility values for every pixel, as a result the biggest probability value is given to the pixel and the pixel belongs to a certain land cover type automatically. The proposed approach is tested on University of Pavia ROSIS data. The results indicate that the new method improve the classification accuracy compared to results of methods based on fixed window size textural classification.