A knowledge-based system for the computation of land cover mixing and the classification of multi-spectral satellite imagery

Summary Objectives: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. Methods: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. Results: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. Conclusions: The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

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Methods of performance evaluation for the supervised classification of satellite imagery in determining land cover classes

Ciencia e investigación agraria ◽

10.4067/s0718-16202013000200016 ◽

2013 ◽

Vol 40 (2) ◽

pp. 419-428 ◽

Cited By ~ 4

Author(s):

Carlos H. Wachholz de Souza ◽

Erivelto Mercante ◽

Victor H. R. Prudente ◽

Diego D.D. Justina

Keyword(s):

Performance Evaluation ◽

Land Cover ◽

Satellite Imagery ◽

Supervised Classification

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A new Bayesian ensemble of trees approach for land cover classification of satellite imagery

Canadian Journal of Remote Sensing ◽

10.5589/m14-003 ◽

2014 ◽

Vol 39 (6) ◽

pp. 507-520 ◽

Cited By ~ 8

Author(s):

Reshu Agarwal ◽

Pritam Ranjan ◽

Hugh Chipman

Keyword(s):

Land Cover ◽

Satellite Imagery ◽

Land Cover Classification

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A development of the Enhanced Built-up and Bareness Index (EBBI) based on combination of multi-resolution Landsat 8 and Sentinel 2 MSI images

Journal of Mining and Earth Sciences ◽

10.46326/jmes.2021.62(1).01 ◽

2021 ◽

Vol 62 (1) ◽

pp. 1-9

Author(s):

Hung Le Trinh ◽

Ha Thu Thi Le ◽

Loc Duc Le ◽

Long Thanh Nguyen ◽

Keyword(s):

Land Cover ◽

Satellite Imagery ◽

Near Infrared ◽

Urban Land ◽

Urban Land Use ◽

Landsat 8 ◽

Infrared Band ◽

Bare Land ◽

Sentinel 2

Classification of built-up land and bare land on remote sensing images is a very difficult problem due to the complexity of the urban land cover. Several urban indices have been proposed to improve the accuracy in classifying urban land use/land cover from optical satellite imagery. This paper presents an development of the EBBI (Enhanced Built-up and Bareness Index) index based on the combination of Landsat 8 and Sentinel 2 multi-resolution satellite imagery. Near infrared band (band 8a), short wave infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) Landsat 8 image were used to calculate EBBI index. The results obtained show that the combination of Landsat 8 and Sentinel 2 satellite images improves the spatial resolution of EBBI index image, thereby improving the accuracy of classification of bare land and built-up land by about 5% compared with the case using only Landsat 8 images.

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