High resolution image possesses abundant information of ground objects. The hand-crafted features cannot meet the demand of complex scene classification due to complex scene distribution

while the unsupervised feature learning method can exploit the intrinsic structure of image patches to obtain effective discriminating features. However

single feature with a scale is difficult to represent the characteristics of complex scenes in practical applications

which restricts classification performance. To solve this problem

this paper proposed a new method based on multi-scale and multi-feature fusion (MMF) for remote sensing scene classification. At first

an improved unsupervised feature learning via spectral clustering (iUFL-SC) is designed to effectively reveal the intrinsic structure of image patches

and then the iUFL-SC

LBP

and SIFT features of image patches are extracted by dense sampling in each image. After that

the middle-level features of each scene are obtained through bag of visual words (BoVW) model for effective feature description. Finally

the fused features are classified by histogram intersection kernel SVM. Experimental results on two public data sets indicate that MMF can extract discriminant features of remote sensing image and subsequently improve the classification performance.