Reliable cloud detection plays an important role in the manufacture of remote sensing and the alarm of natural calamities. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of clouds with different concentration, color and shapes. Related works mostly used gray, shape and texture features to detect clouds, which obtain results with poor robustness and efficiency. To detect cloud more automatically and robustly, we propose a novel could detection method based on the fusion of local optimum by adaptive simple linear iterative clustering (ASLIC) and the whole optimum by bilateral filtering with an improved saliency detection method. After this step, we trained a multi-feature fusion model based support vector machine(SVM) used geometric feature: fractal dimension index (FRAC) and independence index (IDD) which is proposed by us to describe the piece of region’s spatial distribution, texture feature: we use four angles to calculate the gray-level co-occurrence matrix (GLXM) about entropy, energy, contrast, homogeneity, spectral feature(SF): after principal component analysis(PCA) we choose the first bond, the second bond and the near infrared bond(NIR). Besides, in view of the disturbance of water, ice, we also use NDVI and HOT index to estimate the model. Compared to the traditional methods of SLIC, our new method for cloud detection is accurate, and robust when dealing with clouds of different types and sizes over various land satellite images.