MSPPF-NETS: A DEEP LEARNING ARCHITECTURE FOR REMOTE SENSING IMAGE CLASSIFICATION

被引：0

作者：

Yang, Rui ^{[1
]}

Zhang, Yun ^{[1
]}

Zhao, Pengfei ^{[1
]}

Ji, Zhenyuan ^{[1
]}

Deng, Weibo ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin, Peoples R China

来源：

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年

关键词：

Local Climate Zones (LCZs); MSPPF-nets; Classification; Remote sensing image; Feature fusion;

D O I：

10.1109/igarss.2019.8899068

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Nowadays, deep learning has got a major success in computer vision, especially in image recognition. In this paper, a new architecture based on DenseNets which is referred to as Multi-Scale Input Spatial Pyramid Pooling Fusion Networks (MSPPF-nets) is proposed for the work of classification of local climate zones (LCZs). Multi-scale remote sensing images can be inputs of the networks by the benefit of Spatial Pyramid Pooling (SPP) layer, multi-scale features from different channels were extracted and fused by our multi-branch-input framework. The final classification results have illustrated the feasibility of this presented classification method.

引用

页码：3045 / 3048

页数：4

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