Joint Learning of the Center Points and Deep Metrics for Land-Use Classification in Remote Sensing
Deep learning methods, especially convolutional neural networks (CNNs), have shown remarkable ability for remote sensing scene classification. However, the traditional training process of standard CNNs only takes the point-wise penalization of the training samples into consideration, which usually m...
Main Authors: | Zhiqiang Gong, Ping Zhong, Weidong Hu, Yuming Hua |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | http://www.mdpi.com/2072-4292/11/1/76 |
Similar Items
-
Deep metric learning method for high resolution remote sensing image scene classification
by: YE Lihua, et al.
Published: (2019-06-01) -
Attention Consistent Network for Remote Sensing Scene Classification
by: Xu Tang, et al.
Published: (2021-01-01) -
Deep Object-Centric Pooling in Convolutional Neural Network for Remote Sensing Scene Classification
by: Kunlun Qi, et al.
Published: (2021-01-01) -
A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification
by: Cuiping Shi, et al.
Published: (2021-05-01) -
Improved Metric Learning With the CNN for Very-High-Resolution Remote Sensing Image Classification
by: Cheng Shi, et al.
Published: (2021-01-01)