Abstract
Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.
Highlights
As one of the most biologically productive ecosystems on earth, wetlands are of significant importance for hydrological and ecological processes [1]
The performance of SCAA_MLP and other comparative classifiers was tested on two coastal wetland study sites
We proposed a novel spectral–spatial classification framework for coastal wetland using Sentinel-2 remotely sensed imagery, in which morphological attribute profiles (APs) were extracted and a multilayer perceptron (MLP) classifier was optimized by SCAA
Summary
As one of the most biologically productive ecosystems on earth, wetlands are of significant importance for hydrological and ecological processes [1]. The land covers from the same wetland type present strong spectral heterogeneity because of the variances in water volume, salt content, vegetation density, and illumination conditions [6,13,14,15]. Characteristics such as high within-class variability and low between-class disparity make the classification of coastal wetlands a challenging task
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