Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

Algorithm 1

The data processing and the training phase of the proposed neural computing model used for impervious surface detection.

	//Retrieve remote sensing data and data preprocessing
	Obtain spectral bands of Sentinel-2 for the study area
	Open spectral bands in Sentinel Application Platform (SNAP)
	Image enhancement via histogram equalization
	Normalized difference vegetation index (NDVI) computation
	Removal of large water bodies using the computed NDVI
	//Generate dataset for the two classes of impervious and pervious surfaces
	//Image data sampling
	Create image dataset PosSet for impervious class
	Create image dataset NegSet for pervious class
	//Image texture computation
	For each image in PosSet
	Image texture computation using the following:
	(i) Statistical measurements of bands
	(ii) BGC
	End for
	For each image in NegSet
	Image texture computation using the following:
	(i) Statistical measurements of bands
	(ii) BGC
	End for
	Construct numerical dataset D containing texture features and class labels
	//Neural computing model training and evaluation
	Set the total number of model evaluation times RN = 20
	Establish a set of optimizers
	For r = 0 to RN-1
	Randomly extract 70% of the dataset to form a training dataset
	Select an optimizer from SO
	Perform model training
	Evaluating the model training performance
	End for