Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

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Riparian Vegetation Mapping for Hydraulic Roughness Estimation Using Very High Resolution Remote Sensing Data Fusion

Journal of Hydraulic Engineering ◽

10.1061/(asce)hy.1943-7900.0000254 ◽

2010 ◽

Vol 136 (11) ◽

pp. 855-867 ◽

Cited By ~ 35

Author(s):

Giovanni Forzieri ◽

Gabriele Moser ◽

Enrique R. Vivoni ◽

Fabio Castelli ◽

Francesco Canovaro

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Data Fusion ◽

Riparian Vegetation ◽

Remote Sensing Data ◽

Vegetation Mapping ◽

Hydraulic Roughness ◽

Sensing Data ◽

Very High

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Feature-location analyses for identification of urban tree species from very high resolution remote sensing data

Ecological Informatics ◽

10.1016/j.ecoinf.2015.06.002 ◽

2015 ◽

Vol 29 ◽

pp. 16-24 ◽

Cited By ~ 2

Author(s):

Jianhua Zhou ◽

Jun Qin ◽

kai Gao ◽

Si Xu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Tree Species ◽

Remote Sensing Data ◽

Feature Location ◽

Urban Tree ◽

Sensing Data ◽

Very High

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An Object Based Analysis Applied to Very High Resolution Remote Sensing Data for the Change Detection of Soil Sealing at Urban Scale

Recent Advances of Neural Network Models and Applications - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-319-04129-2_16 ◽

2014 ◽

pp. 155-162 ◽

Cited By ~ 1

Author(s):

Luca Pugliese ◽

Silvia Scarpetta

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Change Detection ◽

Remote Sensing Data ◽

Sensing Data ◽

Soil Sealing ◽

Object Based ◽

Very High

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BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-7-83-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 83-88 ◽

Cited By ~ 3

Author(s):

M. Papadomanolaki ◽

M. Vakalopoulou ◽

S. Zagoruyko ◽

K. Karantzalos

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Remote Sensing Data ◽

Multispectral Data ◽

Sensing Data ◽

Multispectral Remote Sensing ◽

High Level ◽

Learning Frameworks

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

Download Full-text