The main objective of this paper was to assess the capability of multisource remote sensing imagery fusion for coastal zone classification. Five scenes of Gaofen- (GF-) 1 optic imagery and four scenes of synthetic aperture radar (SAR) (C-band Sentinel-1 and L-band ALOS-2) imagery were collected and matched. Note that GF-1 is the first satellite of the China high-resolution earth observation system, which acquires multispectral data with decametric spatial resolution, high temporal resolution, and wide coverage. The results showed that based on the comparison of C- and L-band SAR for coastal coverage, it is verified that C band is superior to L band and parameter subsets of
Coastal zones, typical land-sea ecosystems, play a key role in the sustainable development and environmental protection of shorelines, where around 50% of the world’s population lives within 60 up to 200 km of the coast [
Remote sensing, both passive and active sensors, has been proven to be a valuable tool for coastal zone classification. Optical sensors, such as the Thematic Mapper (TM) (Landsat 5), Enhanced Thematic Mapper Plus (ETM+) (Landsat 7), High Resolution Visible (HRV) (SPOT 3), High Resolution Visible and Infrared (HRVIR) (SPOT 4), and High Resolution Geometric (HRG) (Spot 5) sensors; the new Chinese Gaofen (GF-1, -2, and -4) sensors; and synthetic aperture radar (SAR) sensors like RADARSAT-1/2, ENVISAT ASAR, ALOS-1/2, Terra, COSMO-SkyMed, and GF-3 SAR sensors, have been used to map and monitor coastal zones for more than 20 years [
In this paper, optical imagery from Landsat TM, Gaofen- (GF-) 1, and C-band Sentinel-1 and L-band ALOS-2 SAR was collected and matched. Simultaneously, the spectral and polarimetric features of sampled coastal types were analyzed for classification. A wavelet transform (WT) was also proposed for multisource remote sensing imagery fusion to acquire optimum classification results.
The sections of this paper are as follows: the study area and remote sensing data set are introduced in Section
Hangzhou Bay is a representative wetland area located south of the Yangtze River Delta, with a winding shoreline and numerous islands. In addition, the north-south transition of climate and the east-west transition of landforms result in coastal wetland diversity. There are five types of wetland distributed in Zhejiang Province (Table
Specifications of remote sensing datasets.
Sensor | Type | Polarization/bands | Time (UTC) | Resolution (m) | Swath width (km) |
---|---|---|---|---|---|
ALOS-2 | SAR | HH/HV/VH/VV | 2016-5-16 (15:32) | 6 | 40 × 70 |
ALOS-2 | SAR | HH/HV/VH/VV | 2016-12-26 (15:32) | 6 | 40 × 70 |
ALOS-2 | SAR | HH/HV | 2016-7-17 (15:59) | 3 | 50 × 70 |
Sentinel-1 | SAR | VH/VV | 2016-7-26 (09:54) | 5 × 20 | 240 |
Sentinel-1 | SAR | VH/VV | 2016-7-26 (09:54) | 5 × 20 | 240 |
Landsat 8 | Optic | OLI | 2016-07-21 (19:19) | 30 | 185 |
Landsat 8 | Optic | OLI | 2016-07-21 (19:19) | 30 | 185 |
GF-1 | Optic | PMS | 2016-07-07 (11:10) | 8 | 60 |
GF-1 | Optic | PMS | 2016-07-07 (11:10) | 8 | 60 |
GF-1 | Optic | PMS | 2016-07-07 (11:10) | 8 | 60 |
GF-1 | Optic | PMS | 2016-07-07 (11:10) | 8 | 60 |
GF-1 | Optic | WFV | 2016-07-15 (11:06) | 16 | 800 |
Three scenes of ALOS-2 SAR imagery were collected, including two scenes from quad-polarized imagery and one scene from dual-polarized imagery. ALOS-2 stands for Advanced Land Observing Satellite 2, launched by the Japan Aerospace Exploration Agency (JAXA) in May 2014. As the name indicates, the ALOS-2 is the successor of the ALOS, but specialized for the L-band (1.2 GHz) SAR.
Two Sentinel-1 SAR imagery scenes were collected for comparison. Sentinel-1 is the first of the Copernicus Programme satellite constellation, launched by the European Space Agency in April 2010. The Sentinel-1 pairs are composed of two satellites, Sentinel-1A and Sentinel-1B, that carry a C-band (5.405 GHz) SAR.
Five GF-1 optical imagery scenes were also collected for analysis. The Chinese GF-1 is the first satellite of the Major National Science and Technology Project of China, known as the China high-resolution earth observation system, launched in April 2013. The GF-1 panchromatic multispectral (PMS) sensor and wide-field view (WFV) cameras acquire data with high spatial resolution, wide coverage, and high revisit frequency, which are highly valuable data sources for coastal zone dynamic monitoring and classification.
The shapefile of survey results is also shown in Figure
Data coverage of remote sensing imagery and survey shapefile results. Red rectangles represent the coverage area of Landsat optics imagery, yellow rectangles represent the coverage area of GF-1 optics imagery, blue rectangles represent the coverage area of ALOS-2 SAR imagery, green rectangles represent the coverage area of Sentinel-1 SAR imagery, and the grey area represents the coverage area of survey shapefile results.
With the exception of the normalized radar cross section (NRCS) of 4 polarization channels, the multipolarization features considered in this study are shown in Table
Polarized parameters used in this study.
Parameters | Definition |
---|---|
Copolarized backscattering coefficient | |
Cross-polarized backscattering coefficient | |
Cross-polarized backscattering coefficient difference | |
Cross-polarized polarization ratio |
WT is widely used for imagery fusion through the multiresolution analysis of the spatial-frequency domain. The main idea of WT fusion involves retrieving multiresolution signals from WT and then fusing the images at different scales. It is noteworthy that the WT method was derived by performing an inverse WT using a low-resolution multispectral approximation image and the details from a high-resolution panchromatic image [
The computation of WT from a 2-D image involves recursive filtering and subsampling. At each level, there are four detailed images: low-low (LL), low-high (LH), high-low (HL), and high-high (HH). An
Schematic of image fusion based on WT.
In this study, the MLC method was adopted to achieve high classification accuracy, which relies on the Bayesian maximum likelihood approach that discriminates different classes with the same a priori occurrence probability [
The schematic for our study is shown in Figure
Flow chart of our study.
The multipolarization parameter assessment consisted of the mean and standard deviation for both C-band Sentinel-1 and L-band ALOS-2 SAR. For each type of polarization parameter listed in Table
Mean and standard deviation of
Four polarimetric parameters: (a)
From Figure
Prior to imagery fusion, the hazen-intensity-saturation (HIS) transforms were respectively applied for GF-1 and Sentinel-1 SAR imagery to decompose the imagery into H, I, S spaces. We selected individual components from GF-1 optical and Sentinel-1 SAR imagery for fusion; then, they were fused into a new single component using WT. The WT method was used to eliminate distortion issues for spectral features in the transform. Finally, revised HIS transforms were performed to restore the fusion results to the RGB space. The fusion imagery is shown in Figure
Pseudo RGB composite imagery: (a) GF-1, (b) HIS fusion, (c) our proposed fusion method.
Compared with pseudo RGB composite imagery from the GF-1 image (Figure
It can be seen in Table
Statistics for imagery features.
Imagery | Mean | Std | Entropy | Gradient | Cor |
---|---|---|---|---|---|
GF-1 | 64.323 | 63.118 | 6.669 | 4.713 | |
HIS fusion | 62.932 | 75.029 | 6.670 | 6.182 | 0.914 |
Our proposed fusion method | 67.526 | 76.184 | 7.321 | 6.440 | 0.955 |
A ML classification was then applied to the fusion results. The procedure was as follows:
In accordance with an expert interpretation diagram (Figure All the training samples were used as inputs for the MLC method. After completing the training, the validation samples were then applied to generate the type of identification accuracy and kappa coefficient. The five test areas, which corresponded to the five regions defined by the reference map, were manually identified in the classification outputs. Finally, the coastal classification map is shown in Figure
Coastal classification results. Reprojected outputs obtained using MLC.
The classification outputs (Figure
Kappa coefficient and classification accuracy results.
Class | Product accuracy (%) | User accuracy (%) | Product accuracy (pixels) | User accuracy (pixels) |
---|---|---|---|---|
Sea | 100.00 | 96.60 | 2387/2387 | 2387/2471 |
Intertidal zone | 99.69 | 81.82 | 2574/2582 | 2574/3146 |
Aquiculture zone | 50.11 | 72.16 | 1112/2219 | 1112/1541 |
Buildings | 84.95 | 80.20 | 2439/2871 | 2439/3041 |
Plant cover | 92.63 | 99.42 | 1899/2050 | 1899/1910 |
Overall accuracy = (10411/12109) = 85.9774%, kappa coefficient = 0.8236.
The criteria of the expert interpretation diagram are according to the criteria shown in Table
Classification criterion and interpretation sign for coastal visual interpretation.
Type | Classification criteria | Interpretation sign | ||
---|---|---|---|---|
Geometry | Color | Texture | ||
Shallow waters | Planar | Dark cyan | Smooth | |
Rocky coast | Planar or stripped | White or turquoise | Smooth | |
Sand beach | Stripped | White in the middle, turquoise in offshore, brownish in nearshore | Quite smooth | |
Silt beach | Schistose or stripped | Indigo | Quite smooth | |
Intertidal marshes | Irregular shape | Seashell rose | Smooth, irregular tidal bore marks | |
Irregular shape | Camel hair | Quite rough | ||
Mangrove forest | Irregular shape | Rough | ||
Estuarine waters | Naturally curve or obviously flat, apparent boundary | Smooth | ||
Sand island | Banding or irregular shape | Quite rough, obvious layering | ||
Coastal salt lake | Regular shape | Smooth | ||
Coastal fresh lake | Irregular, natural shape | From deep red to red | Quite smooth | |
Reservoirs | Regular geometrical shape | Mazarine | Smooth | |
Aquafarm | Regular banding | Dark cyan or aqua | Clear boundaries, chequered with black and white | |
Rice field | Regular shape, ridges, ditches, and other agricultural facilities | Seashell rose | Smooth | |
Salt pan | Regular rectangle and continuous distribution | Dark green, white | Clear boundaries, square shape gray and white, coarse | |
Others |
This paper investigated the utility of multisource remote sensing imagery based on WT for coastal coverage classification. Five scenes of GF-1 optical imagery and four scenes of SAR (C-band Sentinel-1 and L-band ALOS-2) imagery were collected and used to identify the optimal combination of SAR band and polarimetric parameters. A fusion method based on WT was proposed and performed for imagery fusion. Finally, the classification output was provided, along with a classification accuracy assessment and the kappa coefficient. The conclusions are as follows:
In terms of response of C- and L-band SAR to coastal coverage, the C-band Sentinel-1 is superior to the L-band ALOS-2 SAR. Moreover, compared with the other three parameters, the In terms of fusion performance for our proposed method, although the Std of our proposed fusion results was much bigger, the other features (mean, entropy, and gradient) were superior to the GF-1 and HIS fusion results. In addition, the Cor statistics showed that the results of our proposed method was much better than the HIS fusion results. In terms of classification assessment of our proposed fusion method, the kappa coefficient and overall accuracy were 0.8236 and 85.9774%, respectively, which had a satisfying performance for coastal coverage mapping.
The data used to support the findings of this study are available from the corresponding author upon request. The ALOS-2 data used to support the findings of this study have been deposited in the JAXA repository (
The authors declare that they have no conflicts of interest.
The authors gratefully acknowledge the financial supports from the National Natural Science Foundation of China (61601213) and the project funded by China Postdoctoral Science Foundation (2017M611252).