Change detection techniques attempt to be used for remote sensing monitoring of invasive plants. A novel change detection method based on direction feature and RFLICM (an improved fuzzy C-means clustering) is proposed. Firstly, the difference image is acquired from multitemporal images. The pixel values of the difference image are updated using the template of directional neighborhood based on direction feature. The final change detection map is achieved by clustering the pixel values of the difference image using RFLICM algorithm into two disjoint classes: changed and unchanged. The results obtained by experiment are compared with some other existing state of the art methods. It is observed that the proposed method outperforms the other methods. Finally, the proposed change detection technique is applied to remote sensing monitoring of invasive plants.
['Qingsong Li', 'Xizhong Qin', 'Zhenhong Jia', 'Jie Yang', 'Raphael Hu']
A Novel Change Detection Method Based on Direction Feature and Fuzzy Clustering for Remote Sensing Images and Its Application in Biological Invasions