Mangrove classification using airborne hyperspectral AVIRIS-NG and comparing with other spaceborne hyperspectral and multispectral data

被引：0

作者：

Prakash Hati J. ^{[1
]}

Samanta S. ^{[1
]}

Rani Chaube N. ^{[2
]}

Misra A. ^{[2
]}

Giri S. ^{[1
]}

Pramanick N. ^{[1
]}

Gupta K. ^{[1
,3
]}

Datta Majumdar S. ^{[1
]}

Chanda A. ^{[1
]}

Mukhopadhyay A. ^{[1
,3
]}

Hazra S. ^{[1
]}

机构：

[1] School of Oceanographic Studies, Jadavpur University, Kolkata

[2] Space Application Centre, Indian Space Research Organisation, Ahmedabad

[3] Centre for Earth Observation Science, University of Manitoba, Winnipeg

来源：

Egyptian Journal of Remote Sensing and Space Science | 2021年 / 24卷 / 02期

基金：

美国国家航空航天局;

关键词：

AVIRIS-NG; Hyperion; Mangroves; Sentinel-2; Support Vector Machine;

D O I：

10.1016/j.ejrs.2020.10.002

中图分类号：

学科分类号：

摘要：

Application of remote sensing makes the assessment and monitoring of mangroves both time and cost-effective. In this study, the capacity of AVIRIS-NG data in discriminating different mangrove species of Lothian Island of Indian Sundarbans has been evaluated and compared with hyperspectral (Hyperion) and multispectral dataset (Landsat 8 OLI and Sentinel-2). Spectral signatures of mangrove species were retrieved, and spectral libraries were created. With the corrected images and spectral libraries, mangroves were classified using appropriate classification techniques. For multispectral datasets (Landsat 8 OLI and Sentinel-2) and hyperspectral coarser-resolution Hyperion datasets, K-means classification followed by knowledge-based classification was adopted. For fine resolution hyperspectral AVIRIS-NG dataset, classification was accomplished using Support Vector Machine (SVM). The overall accuracy for the classification is significantly high in case of AVIRIS-NG data (87.61%) compared to the Landsat 8 OLI (76.42%), Sentinel-2 (79.81%), and Hyperion data (81.98%). The results showed that AVIRIS-NG hyperspectral dataset has the potential to classify not only the genus level but also species-level with satisfactory accuracy in a complex mangrove forest. © 2020 National Authority for Remote Sensing and Space Sciences

引用

页码：273 / 281

页数：8

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