Abstract
Abstract. Remote Sensing scene classification aims to identify semantic objects with similar characteristics from high resolution images. Even though existing methods have achieved satisfactory performance, the features used for classification modeling are still limited to some kinds of vector representation within a Euclidean space. As a result, their models are not robust to reflect the essential scene characteristics, hardly to promote classification accuracy higher. In this study, we propose a novel scene classification method based on the intrinsic mean on a Lie Group manifold. By introducing Lie Group machine learning into scene classification, the new method uses the geodesic distance on the Lie Group manifold, instead of Euclidean distance, solving the problem that non-euclidean space samples could not be calculated by Euclidean distance directly. The experiments show that our method produces satisfactory performance on two public and challenging remote sensing scene datasets, UC Merced and SIRI-WHU, respectively.
Highlights
Remote sensing scene classification refers to distinguishing semantic objects with similar scene characteristics from multiple image categories and classifying them into scene types
Different remote sensing images in the database are classified according to certain dominating features, which make the extraction of image features a key to scene classification
We propose a strategy to classify the samples of Lie Group in the space of Lie Group manifold, and implement the intrinsic mean classification algorithm within the Lie Group according to this strategy
Summary
Remote sensing scene classification refers to distinguishing semantic objects with similar scene characteristics from multiple image categories and classifying them into scene types . Different remote sensing images in the database are classified according to certain dominating features, which make the extraction of image features a key to scene classification. The third method is developed in recent years, which does not need to extract feature descriptors manually, and the classification effect on scenes is very good if a learning network is well trained. A deep network model needs a large amount of data for training, which usually takes a long time for computing and relies on highly configured device supports
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