Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram |
| |
Authors: | Anzhi Yue Jianyu Yang Wei Su Wenju Yun Dehai Zhu |
| |
Affiliation: | 1. College of Information and Electrical Engineering , China Agricultural University , Beijing , 100083 , China;2. Institute of Remote Sensing Applications, Chinese Academy of Sciences , Beijing , 100101 , China;3. College of Information and Electrical Engineering , China Agricultural University , Beijing , 100083 , China;4. Land Consolidation and Rehabilitation Centre , the Ministry of Land Resources , Beijing , 100035 , China |
| |
Abstract: | A Semivariogram, as defined in geostatistics, is a powerful tool for texture extraction of remotely sensed images. However, the traditional texture features extracted by a semivariogram are generally for pixel-based classification. Moreover, most studies have been based on the original computation mode of semivariogram and discrete semivariance values. This article describes a set of semivariogram texture features (STFs) based on the mean square root pair difference (SRPD) to improve the accuracy of object-oriented classification (OOC) in QuickBird images. The adaptive parameters for the calculation of a semivariogram were first derived from semivariance analysis, including directions, moving window size, and lag distance. Then, 22 STFs were extracted from the discrete and mean/standard deviation semivariance, and 15 features were selected from the extracted STFs based on feature optimization. Then five grey-level co-occurrence matrix (GLCM) texture features (mean, homogeneity, contrast, angular second moment, and entropy) were calculated based on segmented image objects using the panchromatic band. A comparison of classification results demonstrates that the STFs described in this article are useful supplement information for the spectral OOC, and the spectral + STFs classification method can be used to obtain a higher classification accuracy than can the combination of spectral and GLCM features. |
| |
Keywords: | |
|
|