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Supervised High-Level Feature Learning With Label Consistencies for Object Recognition
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DOI：10.1109/TGRS.2019.2955557.png)
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Abstract
En 中文
Due to the large intraclass variances and complicated object distribution, recognizing objects with complex appearances and arbitrary orientations has been an active research topic and a challenging task in remote sensing fields. In this article, we formulate object recognition as a high-level feature-learning problem, and a novel supervised method is proposed to learn high-level feature representations from high-resolution remote sensing images for object recognition. Our method simultaneously and coherently achieves high-level feature learning and classifier training, which improves the recognition performance. Two constraints that enforce the label consistencies of group images and label consistencies of single images are introduced in a deep learning framework to obtain the high-level feature space. The high-level feature and a multiclass linear classifier are finally learned by an effective optimization algorithm. Experimental results demonstrate the superior performance of the proposed method over many state-of-the-art techniques in object recognition.
Keywords:
Feature extraction
Object recognition
Remote sensing
Machine learning
Object detection
Training
Linear programming
Deep learning
high-level feature
label consistency
object recognition
objective function
optimization
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