TY - JOUR
T1 - Gabor features extraction and land-cover classification of urban hyperspectral images for remote sensing applications
AU - Cruz-Ramos, Clara
AU - Garcia-Salgado, Beatriz P.
AU - Reyes-Reyes, Rogelio
AU - Ponomaryov, Volodymyr
AU - Sadovnychiy, Sergiy
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/8
Y1 - 2021/8
N2 - The principles of the transform stage of the extract, transform and load (ETL) process can be applied to index the data in functional structures for the decision-making inherent in an urban remote sensing application. This work proposes a method that can be utilised as an organisation stage by reducing the data dimension with Gabor texture features extracted from grey-scale representations of the Hue, Saturation and Value (HSV) colour space and the Normalised Difference Vegetation Index (NDVI). Additionally, the texture features are reduced using the Linear Discriminant Analysis (LDA) method. Afterwards, an Artificial Neural Network (ANN) is employed to classify the data and build a tick data matrix indexed by the belonging class of the observations, which could be retrieved for further analysis according to the class selected to explore. The proposed method is compared in terms of classification rates, reduction efficiency and training time against the utilisation of other grey-scale representations and classifiers. This method compresses up to 87% of the original features and achieves similar classification results to non-reduced features but at a higher training time.
AB - The principles of the transform stage of the extract, transform and load (ETL) process can be applied to index the data in functional structures for the decision-making inherent in an urban remote sensing application. This work proposes a method that can be utilised as an organisation stage by reducing the data dimension with Gabor texture features extracted from grey-scale representations of the Hue, Saturation and Value (HSV) colour space and the Normalised Difference Vegetation Index (NDVI). Additionally, the texture features are reduced using the Linear Discriminant Analysis (LDA) method. Afterwards, an Artificial Neural Network (ANN) is employed to classify the data and build a tick data matrix indexed by the belonging class of the observations, which could be retrieved for further analysis according to the class selected to explore. The proposed method is compared in terms of classification rates, reduction efficiency and training time against the utilisation of other grey-scale representations and classifiers. This method compresses up to 87% of the original features and achieves similar classification results to non-reduced features but at a higher training time.
KW - Artificial neural network
KW - Dimension reduction
KW - Feature extraction
KW - Gabor features
KW - Urban hyperspectral images
UR - http://www.scopus.com/inward/record.url?scp=85111428563&partnerID=8YFLogxK
U2 - 10.3390/rs13152914
DO - 10.3390/rs13152914
M3 - Artículo
AN - SCOPUS:85111428563
SN - 2072-4292
VL - 13
JO - Remote Sensing
JF - Remote Sensing
IS - 15
M1 - 2914
ER -