Scene Classification of High-Resolution Remote Sensing Image by Multi-scale and Multi-feature Fusion

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. © 2020, Chinese Institute of Electronics. All right reserved.