A Deep Learning Hybrid CNN Framework Approach for Vegetation Cover Mapping using Deep Features

被引:31
|
作者
Nijhawan, Rahul [1 ]
Sharma, Himanshu [2 ]
Sahni, Harshita [3 ]
Batra, Ashita [4 ]
机构
[1] IIT Roorkee, Dept Earthquake Engn, Roorkee, Uttar Pradesh, India
[2] Quantum Sch Tech, CSE, Roorkee, Uttar Pradesh, India
[3] COER, CSE, Roorkee, Uttar Pradesh, India
[4] Quantum Sch Tech, CSE, Roorkee, Uttar Pradesh, India
关键词
Deep Learning; CNN; Hybrid; Vegetation Cover; Sentinel; 2; IMAGERY; FOREST;
D O I
10.1109/SITIS.2017.41
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Vegetation cover mapping is an imperative task of monitoring the change in vegetation as it can help us meet sustenance requirements. In this study, we explore the future potential of multilayer Deep learning framework (DL) that comprises of hybrid of CNN's, for mapping vegetation cover area as DL is a congenial state-of-art algorithm for implementing image processing. This study proposes a novel DL framework exploiting hybrids of CNN's with Local binary pattern and GIST features. Every CNN is fed with disparate combination of multi-spectral Sentinel 2 satellite imagery bands (spatial resolution of 10m), texture and topographic parameters of Uttarakhand (30 degrees 15' N, 79 degrees 15' E) region, India. Our proposed DL framework outperformed the state-of-art algorithms with a classification accuracy of 88.43%.
引用
收藏
页码:192 / 196
页数:5
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