Landcover classification of satellite images based on an adaptive interval fuzzy c-means algorithm coupled with spatial information
Landcover classification of satellite images based on an adaptive interval fuzzy c-means algorithm coupled with spatial information
复制标题
基于自适应区间模糊 C 均值算法与空间信息相结合的卫星图像土地覆盖分类
DOI：
10.1080/01431161.2019.1685718
复制
发表时间：
2020
影响因子：
3.4
通讯作者：
Zhu Meng
中科院分区：
文献类型：
--
作者：
Xu Jindong;Feng Guozheng;Fan Baode;Yan Weiqing;Zhao Tianyu;Sun Xiao;Zhu Meng
ABSTRACT Landcover classifications have large uncertainty related to the heterogeneity of similar objects and complex spatial correlations in satellite images, making it difficult to obtain ideal classification results using traditional classification methods. Therefore, to address the uncertainty in landcover classifications based on remotely sensed information, we propose a novel fuzzy c-means algorithm, which integrates adaptive interval-valued modelling and spatial information. It dynamically adjusts the interval width according to the fuzzy degree of the target membership without pre-setting any parameters, controls the fuzziness of the target, and mines the inherent distribution of the data. Furthermore, reliability-based spatial correlation modelling is used to describe the spatial relationship of the target and to improve both robustness and accuracy of the algorithm. Experimental data consisting of SPOT5 (10-m spatial resolution) or Thematic Mapper (30-m spatial resolution) satellite data for three case study areas in China are used to test this algorithm. Compared with other state-of-the-art fuzzy classification methods, our algorithm markedly improved the ground-object separability. Moreover, it balanced improvement of pixel separability and suppression of heterogeneity of intra-class objects, producing more compact landcover areas and clearer boundaries between classes.
