Random Topology and Random Multiscale Mapping: An Automated Design of Multiscale and Lightweight Neural Network for Remote-Sensing Image Recognition

计算机科学 卷积神经网络 拓扑(电路) 合成孔径雷达 上下文图像分类 网络拓扑 特征提取 人工智能 人工神经网络 特征(语言学) 模式识别(心理学) 算法 图像(数学) 操作系统 哲学 组合数学 语言学 数学
作者
Jihao Li,Martin Weinmann,Xian Sun,Wenhui Diao,Yingchao Feng,Kun Fu
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:60: 1-17 被引量:2
标识
DOI:10.1109/tgrs.2021.3102988
摘要

With the proposal of neural architecture search (NAS), automated network architecture design gradually becomes a new way in deep learning research. Due to its high capability regarding automated design, some pioneers have made an attempt to apply NAS in remote sensing and made some achievements, like 1-D/3-D Auto-convolutional neural network (CNN) and polarimetric synthetic aperture radar (PolSAR)-tailored Differentiable Architecture Search (PDAS). However, there are still some areas to be improved for existing NAS in remote-sensing field. In this article, we propose a random topology and random multiscale mapping (RTRMM) method to generate a multiscale and lightweight architecture for remote-sensing image recognition. First, a random topology generator generates the topology through random graph. Second, during the experiment, we find remote-sensing image features extracted by a multiscale network are more appropriate, compared with features extracted by a single-scale model. Nevertheless, the complexity inevitably increases with the introduction of a multiscale concept. Consequently, we design a variable search space consisting of decomposition convolution units under the guidance of mathematical analysis. The mapping of each neuron is then determined by a random multiscale mapping sampler. After that, we assemble the topology and mappings into blocks and construct three RTRMM models. Experiments on four scene classification datasets confirm the feature extraction capability and lightweight performance of RTRMM models. Moreover, we also observe that our approach achieves a better tradeoff between floating-point operations (FLOPs) and accuracy than some current well-behaved methods. Furthermore, the results on Vaihingen dataset verify the high feature-transfer capability.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
阿纯完成签到,获得积分10
刚刚
wanci应助charles采纳,获得10
刚刚
快乐的紫寒完成签到,获得积分10
刚刚
V_I_G完成签到,获得积分10
刚刚
阿东c完成签到 ,获得积分10
2秒前
情怀应助别先生采纳,获得10
2秒前
mzp完成签到,获得积分10
2秒前
XXXX完成签到,获得积分10
3秒前
烟酒生完成签到,获得积分10
4秒前
FashionBoy应助jixuchance采纳,获得10
6秒前
派大星完成签到,获得积分10
6秒前
54完成签到,获得积分10
8秒前
ECT完成签到,获得积分10
9秒前
10秒前
Zzz完成签到,获得积分10
10秒前
磕盐民工完成签到,获得积分10
10秒前
xiaowang完成签到,获得积分10
10秒前
vermouth完成签到 ,获得积分10
10秒前
勤奋新晴完成签到,获得积分10
12秒前
老肖应助飞飞飞采纳,获得10
13秒前
温馨完成签到 ,获得积分10
13秒前
舒心小海豚完成签到 ,获得积分10
14秒前
15秒前
15秒前
心愿发布了新的文献求助10
15秒前
打工人完成签到,获得积分10
15秒前
笑点低的如凡完成签到,获得积分10
15秒前
dhjic完成签到 ,获得积分10
15秒前
Wa完成签到,获得积分10
15秒前
浑映之完成签到 ,获得积分10
16秒前
Xccccc完成签到 ,获得积分10
16秒前
赵亚南完成签到,获得积分10
16秒前
vermouth关注了科研通微信公众号
17秒前
wangsenyu完成签到 ,获得积分10
17秒前
英俊亦巧完成签到,获得积分10
18秒前
khurram完成签到,获得积分10
18秒前
18秒前
ljh完成签到 ,获得积分10
18秒前
111完成签到,获得积分10
18秒前
李九妹发布了新的文献求助10
19秒前
高分求助中
Evolution 10000
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
叶剑英与华南分局档案史料 500
Foreign Policy of the French Second Empire: A Bibliography 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3146969
求助须知:如何正确求助?哪些是违规求助? 2798221
关于积分的说明 7827159
捐赠科研通 2454808
什么是DOI,文献DOI怎么找? 1306480
科研通“疑难数据库(出版商)”最低求助积分说明 627788
版权声明 601565