Spectrum Classification of Easily Confused Ground Objects in ALI Remote Sensing Image Based on Texture Features

Abstract

According to the problem that the classification result of shrub and forest land was easy to confuse when used spectrum of Advanced Land Image (ALI) to classify. This paper used the Meijiang River watershed as the study area. Used the Principal Component Analysis (PCA) to reduce dimension, taken the Contrast, Second moment, Mean and Dissimilarity as the texture values, and extracted the texture by Gray level co-occurrence matrix (GLCM). The texture features extracted from different window sizes were used the Maximum likelihood method to classify, and chosen the texture features extracted by the most suitable window size to join the classification. The research result shows that the texture features extracted by window size of 11×11 can distinguish well the two easily ground objects; moreover, the overall accuracy of classification used texture and spectrum features reached to 87.55%, which is 4.4% higher than the classification with spectrum.