Sentinel-2 Images at 2.5m Spatial Resolution via Deep- Learning: A Case Study in Zakythnos

被引：2

作者：

Panagiotopoulou, Antigoni ^{[1
,2
]}

Bratsolis, Emmanuel ^{[1
]}

Grammatikopoulos, Lazaros ^{[1
]}

Petsa, Eleni ^{[1
]}

Charou, Eleni ^{[2
]}

Poirazidis, Konstantinos ^{[3
]}

Martinis, Aristotelis ^{[3
]}

Madamopoulos, Nicholas ^{[4
,5
]}

机构：

[1] Univ West Attica, Dept Surveying & Geoinformat Engn, Athens, Greece

[2] NCSR Demokritos, Inst Informat & Telecommun, Athens, Greece

[3] Ionian Univ, Dept Environm, Zakynthos, Greece

[4] Hellen Air Force Acad, Dept Aeronaut Sci, Dekeleia, Greece

[5] CUNY City Coll, Dept Elect Engn, New York, NY USA

来源：

2022 IEEE 14TH IMAGE, VIDEO, AND MULTIDIMENSIONAL SIGNAL PROCESSING WORKSHOP (IVMSP) | 2022年

关键词：

Sentinel-2; deep-learning; super-resolution; normalized carotenoid reflectance index; olive tree;

D O I：

10.1109/IVMSP54334.2022.9816272

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

High-resolution (HR) satellite images can provide detailed information about land usage/land cover. Often, it is necessary that the satellite sensor inherent spatial resolution is increased through algorithmic processing of the image data acquired. Machine-learning and in particular deep-learning based super-resolution (SR) techniques are an effective tool for increasing the spatial resolution of images. In the current work, Sentinel-2 images are super-resolved to spatial resolution equal to 2.5 m/pixel by means of deep-learning based SR techniques. The area of study is Zakynthos island in Greece. A novel index called Normalized Carotenoid Reflectance Index (NCRI) is proposed for the assessment of land cover by olive trees.

引用

页数：5

共 14 条

[1]

[Anonymous], US

[2] Densely Residual Laplacian Super-Resolution [J].