Abstract
Remote sensing image scene classification is a hot research area for its wide applications. More recently, fusion-based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion-based method for remote sensing image scene classification from another viewpoint. First, it is categorized as front side fusion mode, middle side fusion mode, and back side fusion mode. For each fusion mode, the related methods are introduced and described. Then, classification performances of the single side fusion mode and hybrid side fusion mode (combinations of single side fusion) are evaluated. Comprehensive experiments on UC Merced, WHU-RS19, and NWPU-RESISC45 datasets give the comparison result among various fusion methods. The performance comparisons of various modes, and interactions among different fusion modes are also discussed. It is concluded that (1) fusion is an effective way to improve model performance, (2) back side fusion is the most powerful fusion mode, and (3) method with random crop+multiple backbone+average achieves the best performance.
Highlights
With explosive increasing of remote sensing data, analysis and processing remote sensing image effectively and efficiently becomes of great importance
Remote sensing image scene classification, which aims to classify remote sensing image into different types based on image content, has been attracted more and more attentions for its comprehensive application in fields of geography, ecology, city plan, forest monitor, military, etc [1]
Remote sensing image scene classification essentially belongs to domains of machine learning and computer vision
Summary
With explosive increasing of remote sensing data, analysis and processing remote sensing image effectively and efficiently becomes of great importance. Remote sensing image scene classification, which aims to classify remote sensing image into different types based on image content, has been attracted more and more attentions for its comprehensive application in fields of geography, ecology, city plan, forest monitor, military, etc [1]. Remote sensing image scene classification essentially belongs to domains of machine learning and computer vision. With well-organized training dataset, models can be learned through minimizing loss functions between model output and ground-truth label. According to feature extraction and representation techniques, existing methods can be categorized into three types: method based on low-level feature, method based on mid-level feature, and method based on deep feature
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