Abstract
An effective remote sensing image scene classification approach using patch-based multi-scale completed local binary pattern (MS-CLBP) features and a Fisher vector (FV) is proposed. The approach extracts a set of local patch descriptors by partitioning an image and its multi-scale versions into dense patches and using the CLBP descriptor to characterize local rotation invariant texture information. Then, Fisher vector encoding is used to encode the local patch descriptors (i.e., patch-based CLBP features) into a discriminative representation. To improve the discriminative power of feature representation, multiple sets of parameters are used for CLBP to generate multiple FVs that are concatenated as the final representation for an image. A kernel-based extreme learning machine (KELM) is then employed for classification. The proposed method is extensively evaluated on two public benchmark remote sensing image datasets (i.e., the 21-class land-use dataset and the 19-class satellite scene dataset) and leads to superior classification performance (93.00% for the 21-class dataset with an improvement of approximately 3% when compared with the state-of-the-art MS-CLBP and 94.32% for the 19-class dataset with an improvement of approximately 1%).
Highlights
Remote sensing is an effective tool for Earth observation, which has been widely applied in surveying land-use and land-cover classifications and monitoring their dynamic changes
We propose a local feature representation method based on patch-based multi-scale completed local binary pattern (MS-CLBP), which can be used to extract complementary features to global features
Inspired by the success of CLBP and Fisher vector (FV) in computer vision applications, we propose an effective image representation approach for remote sensing image scene classification based on patch-based
Summary
Remote sensing is an effective tool for Earth observation, which has been widely applied in surveying land-use and land-cover classifications and monitoring their dynamic changes. With the improvement of spatial resolution, remote-sensing images present more detailed information such as spatial arrangement information and textural structures, which are of great help in recognizing different land-use and land-cover scene categories. In [11], global features extracted using the enhanced Gabor texture descriptor (EGTD) and local features extracted using the SIFT descriptor were fused in a hierarchical approach to improve the performance of remote sensing image scene classification. The CLBP descriptor is applied to partition dense image patches and extract a set of local patch descriptors, which characterize the detailed local structure and texture information in high-resolution remote sensing images. The MS-CLBP operator is applied to the partitioned dense regions to extract a set of local patch descriptors, and the Fisher kernel representation is used to encode the local descriptors into a discriminative representation of remote sensing images.
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