Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

Zhang, Aizhu; Sun, Guifan; Ma, Ping; Jia, Xiuping; Ren, Jinchang; Huang, Hui Ling; Zhang, Xuming

doi:10.3390/rs11080952

Cited by 21 publications

(11 citation statements)

References 78 publications

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“…MSI data are captured by multispectral imager carried by Sentinel-2. Table I list the information of satellite remote sensing data [17], [18], [68]. To keep the time consistency and reduce the negative effects from the changing land cover types in classification results, multi-source remote sensing images are chosen with the acquisition time as close as possible.…”

Section: A Study Area and Datasets Descriptionmentioning

confidence: 99%

Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands

Liu

Tao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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It is significant for restoration and protection of natural resources and ecological services in coastal wetlands to map different land cover types with satellite remote sensing data. Considering difficulties of wetland species classification, hyperspectral images (HSI) with high spectral resolution and multispectral images (MSI) with high spatial resolution are considered to achieve complementary advantages of multi-source data. An effective approach, named as multi-stream convolutional neural network (CNN), is proposed to achieve fine classification of coastal wetlands. Firstly, regression processing is adopted to make chaotically scattered coastal wetland data more compact and different. Secondly, through appropriate feature extraction and feature fusion strategies, high-level information of multi-source data in regression domain is fused to distinguish different land cover. Experiments on GF-5 hyperspectral images and Sentinel-2 multispectral images are carried out in order to validate the classification performance of the proposed approach in two coastal wetlands of research value in China, i.e., Yellow River Estuary and Yancheng coastal wetland. Experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods in the field, especially when the number of sample size is extremely small. Index Terms-Coastal wetlands; data fusion; hyperspectral imagery (HSI); multispectral imagery (MSI); convolutional neural network (CNN); least squares regression (LSR).

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Section: A Study Area and Datasets Descriptionmentioning

confidence: 99%

Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands

Liu

Tao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The ultimate land-cover map is obtained via the fusion of the N classification results using the majority vote scheme following Equation (8). The classification results are quantitatively evaluated using two widely used precision evaluation criteria calculated from the confusion matrix, i.e., the Overall Accuracy (OA) and kappa coefficient (kappa) [54].…”

Section: Multi-scale Superpixel-based Classificationmentioning

confidence: 99%

Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification

Zhang

Sun

et al. 2019

Remote Sensing

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The high interior heterogeneity of land surface covers in high-resolution image of coastal cities makes classification challenging. To meet this challenge, a Multi-Scale Superpixels-based Classification method using Optimized Spectral–Spatial features, denoted as OSS-MSSC, is proposed in this paper. In the proposed method, the multi-scale superpixels are firstly generated to capture the local spatial structures of the ground objects with various sizes. Then, the normalized difference vegetation index and extend multi-attribute profiles are introduced to extract the spectral–spatial features from the multi-spectral bands of the image. To reduce the redundancy of the spectral–spatial features, the crossover-based search algorithm is utilized for feature optimization. The pre-classification results at each single scale are, therefore, obtained based on the optimized spectral–spatial features and random forest classifier. Finally, the ultimate classification is generated via the majority voting of those pre-classification results in each scale. Experimental results on the Gaofen-2 image of Qingdao and WorldView-2 image of Hong Kong, China confirmed the effectiveness of the proposed method. The experiments verify that the OSS-MSSC method not only works effectively on the homogeneous regions, but also is able to preserve the small local spatial structures in the high-resolution remote sensing images of coastal cities.

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“…It is fast and simple to implement without any label information on the input data [14]. However, these methods perform worse when deal with the HSI scene with high intra-class disparity and interclass similarity [15]. As presented in [16], the HSI data are supposed to be processed as an image rather than a collection of pixels.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, various spatial-spectral feature extraction methods are developed to consider both spectral and contextual information [17]. The Gabor filter [18], wavelets [19], the extended morphological profile [15], Markov random field [20] and sparse representation [21] have been applied to the HSI classification. In [3], a 2-D extension to SSA method (denoted as 2D-SSA) is developed for effective spatial features extraction of HSI, in which each band image is decomposed into various components and reconstructed using trend and selected oscillation information.…”

Section: Introductionmentioning

confidence: 99%

Multiscale 2-D Singular Spectrum Analysis and Principal Component Analysis for Spatial–Spectral Noise-Robust Feature Extraction and Classification of Hyperspectral Images

Ren

Zhao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss from removed bands. To tackle these issues, in this paper, we propose a novel spatial-spectral feature extraction framework, Multiscale 2D singular spectrum analysis (2D-SSA) with principal component analysis (2D-MSSP), for noise-robust feature extraction and data classification of HSI. First, multiscale 2D-SSA is applied to exploit the multiscale spatial features in each spectral band of HSI via extracting the varying trends within defined windows. Taking the extracted trend signals at each scale level as the input features, the principal component analysis (PCA) is employed to the spectral domain for dimensionality reduction and spatial-spectral feature extraction. The derived spatialspectral features in each scale are separately classified and then fused at decision-level for efficacy. As our 2D-MSSP method can extract features and simultaneously remove noise in both spatial and spectral domains, which ensures it to be noise-robust for classification of HSI, even the uncorrected dataset. Experiments on three publicly available datasets have fully validated the efficacy and robustness of the proposed approach, when benchmarked with ten state-of-the-art classifiers, including six spatial-spectral methods and four deep learning classifiers. In addition, both quantitative and qualitative assessment has validated the efficacy of our approach in noise-robust classification of HSI even with limited training samples, especially in classifying uncorrected data without filtering noisy bands.

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Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

Cited by 21 publications

References 78 publications

Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands

Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands

Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification

Multiscale 2-D Singular Spectrum Analysis and Principal Component Analysis for Spatial–Spectral Noise-Robust Feature Extraction and Classification of Hyperspectral Images

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