摘要： 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.