Hyperspectral image classification based on deep separable residual attention network

高光谱成像 残余物 计算机科学 可分离空间 人工智能 图像(数学) 模式识别(心理学) 遥感 计算机视觉 地质学 算法 数学 数学分析
作者
Chao Tu,Wanjun Liu,Linlin Zhao,Tinghao Yan
出处
期刊:Infrared Physics & Technology [Elsevier BV]
卷期号:140: 105401-105401 被引量:1
标识
DOI:10.1016/j.infrared.2024.105401
摘要

Hyperspectral image have rich spatial and spectral information, and how to fully extract and utilize the features of these two dimensions is a research hotspot in hyperspectral classification methods. At present, the unique convolutional operation and deep feature extraction structure of convolutional neural network enable them to have stronger feature representation capabilities and achieve good results in hyperspectral image classification. However, CNN methods do not assign different weights based on the importance of features in the feature extraction process, making it difficult to effectively utilize key features, and most importantly, using fixed shaped convolution kernel can easily overlook the differences between hyperspectral image features. A hyperspectral image classification method based on deep separable residual attention network is proposed to address the above issues. Firstly, to reduce the correlation between hyperspectral image data and minimize the interference of redundant information, principal component analysis is used to reduce the dimensionality of hyperspectral image. Secondly, a shallow feature extraction module is constructed, which can dynamically adjust the size of the receptive field according to the actual situation of the image, adaptively extract shallow features, and reduce the loss of original image features. Then, a depthwise separable residual attention mechanism module is proposed, based on which features are extracted. Starting from global and local features, contextual information on image features in channel and spatial domains is extracted. Finally, use a multi-scale feature fusion module to fully integrate feature maps at different scales. Using Indian Pines, Pavia University and Botswana as experimental datasets, the overall classification accuracy of this paper's method is 98.47 %, 98.70 %, 98.83 % with only 50, 50, 30 training samples per class. The Kappa coefficient is 98.25 %, 98.27 %, and 98.73 %, respectively. Compared with advanced methods, this method not only has higher classification accuracy, but also fully utilizes key features at various network levels.
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
PDF的下载单位、IP信息已删除 (2025-6-4)

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
ZZICU完成签到,获得积分10
刚刚
hbpu230701完成签到,获得积分0
1秒前
Lenacici完成签到,获得积分10
2秒前
long发布了新的文献求助10
2秒前
量子星尘发布了新的文献求助10
3秒前
3秒前
3秒前
3秒前
4秒前
YY完成签到 ,获得积分10
5秒前
5秒前
徐进发布了新的文献求助10
8秒前
jiaolulu发布了新的文献求助10
8秒前
乐观银耳汤完成签到,获得积分10
9秒前
WJing完成签到,获得积分10
9秒前
lenetivy发布了新的文献求助20
9秒前
11秒前
linhanwenzhou发布了新的文献求助10
13秒前
yyy完成签到 ,获得积分10
13秒前
幽默的煎饼完成签到,获得积分10
13秒前
14秒前
搞怪不斜完成签到,获得积分10
14秒前
14秒前
xinxiangshicheng完成签到 ,获得积分10
15秒前
愤怒的小鸟完成签到,获得积分10
15秒前
MY完成签到,获得积分10
15秒前
顾矜应助lenetivy采纳,获得10
16秒前
自觉寒梦发布了新的文献求助10
16秒前
美好斓发布了新的文献求助10
16秒前
郑文涛完成签到,获得积分10
17秒前
JamesPei应助专注的白柏采纳,获得10
18秒前
YHY发布了新的文献求助10
20秒前
好吃发布了新的文献求助10
20秒前
拾光完成签到,获得积分10
21秒前
long完成签到 ,获得积分10
21秒前
天天向上发布了新的文献求助10
22秒前
6260完成签到,获得积分10
22秒前
pcr163应助linhanwenzhou采纳,获得50
23秒前
23秒前
酷酷元风完成签到,获得积分10
24秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 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 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038368
求助须知:如何正确求助?哪些是违规求助? 3576068
关于积分的说明 11374313
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029