Shoreline-mapping tasks using remotely sensed image sources were carried out using the machine learning techniques or using water indices derived from image sources. This research compared two different methods for mapping accurate shorelines using the high-resolution satellite image acquired in Hwado Island, South Korea. The first shoreline was generated using a water-index-based method proposed in previous research, and the second shoreline was generated using a machine-learning-based method proposed in this research. The statistical results showed that both shorelines had high accuracies in the well-identified coastal zones while the second shoreline had better accuracy than the first shoreline in the coastal zones with irregular shapes and the shaded areas not identified by the water-index-based method. Both shorelines, however, had low accuracies in the coastal zones with the shaded areas not identified by both methods.
A coastal zone is defined as “the coastal waters (including the lands therein and thereunder) and the adjacent shorelands (including the waters therein and thereunder) strongly influenced by each and in proximity to the shorelines of several coastal states, and include islands, transitional and inter-tidal areas, salt marshes, wetlands, and beaches” [
Research on shoreline mapping using the remote sensing datasets has been carried out because the utilization of such datasets is efficient for acquiring the surface and geometric information of wide coastal zones with high accuracy and without human access [
A recent research on mapping shorelines using various remote sensing data was carried out using two different approaches: (1) mapping shorelines by the supervised approach such as the machine learning techniques and (2) mapping shorelines by the unsupervised approach based on the water index derived from multispectral image sources. A comparison of these two approaches for mapping shorelines using the high-resolution image sources, however, has been limited. This research proposed a machine-learning-based method and compared the proposed method with the previous water-index-based method proposed by Choung and Jo (2015) for mapping accurate shorelines using a high-resolution satellite image.
Hwado Island, South Korea, was selected as the study area in this research due to the data availability (see Figure
Hwado Island, South Korea, selected as the study area.
The orthorectified high-resolution satellite image was acquired by the WorldView-2 satellite on October 11, 2011. The given WorldView-2 image consists of the four available spectral bands (blue: 450–510 nm; green: 510–580 nm; red: 630–690 nm; and NIR (near infrared): 770–895 nm), and the ground resolution of the WorldView-2 image is 50 cm [
This section illustrates the water-index-based method, the previous method, and the machine-learning-based method, the proposed method, for mapping shorelines using the given WorldView-2 image. Figure
Flowchart showing the procedure for mapping shorelines using the two different methods.
NDWI is a remote-sensing-derived index for detecting water features such as oceans, rivers, lakes, and reservoirs from multispectral image sources by using their spectral bands [
NDWI image generated from the given WorldView-2 image.
The next step was to convert the NDWI image into the first binary image separating the land and water features. In the water-index-based method, the adaptive threshold derived from the adaptive thresholding method is used to separate the land and water features in the NDWI image because it chooses an adaptive intensity threshold in the NDWI image for minimizing the intraclass variance of the white and black pixel groups in the converted binary image [
First binary image converted from the NDWI image through the adaptive thresholding method.
Finally, the first shoreline was extracted from the first binary image by selecting the boundary between the land and water features. Figure
First shoreline extracted from the first binary image by selecting the boundary between the land and water features.
Machine learning is defined as “a branch of artificial intelligence in which a computer generates rules underlying or based on the raw data that have been fed into it,” and the machine learning technique is defined as “the ability of a machine to improve its performance based on previous results” [
Machine learning is classified into several methods according to the type of learning algorithm used, such as the supervised learning technique operated with training samples and the unsupervised learning technique not requiring training samples [
One section of the generated coastal-surface classification map: (a) one section of the given high-resolution satellite image; (b) one section of the generated coastal-surface classification map.
The next step was to convert the coastal-surface classification map into the second binary image. As the rock and vegetation features represent the land features, these features were grouped into the land features in the converted binary image. Figure
Second binary image converted from the coastal-surface classification map.
In the second binary image, the land features (the white pixels) often include small holes and gaps (the black pixels) due to the irregular shapes of the coastal zones, the continuous wave actions, or the misclassification errors of the SVM classifier, and they generally cause errors in mapping accurate shorelines. Hence, in this research, morphological filtering was applied on the second binary image to remove the small holes and gaps existing in the land features and to preserve the shapes of the land features. Morphological filtering is an image-processing technique for modifying the shapes of the input objects by running the structure elements with specific shapes over the input objects [
Process showing morphological filtering applied to the second binary image: (a) one section of the original binary image; (b) one section of the dilated image; and (c) one section of the eroded image.
After the eroded image was generated through the morphological filtering process, the second shoreline was extracted from the eroded image by selecting the boundary between the land and water features (see Figure
Second shoreline extracted from the eroded image by selecting the boundary between the land and water features.
In this section, the accuracy of the coastal-surface classification map is assessed using the 100 checkpoints, defined as the first checkpoint group, generated by manual digitization located around the second shoreline. Table
Accuracy of the coastal surfaces classified by the SVM classifier.
Overall accuracy | 89% | ||
---|---|---|---|
Producer’s accuracy | User’s accuracy | ||
(Error of omission) | (Error of commission) | ||
Water | 70% | Water | 74% |
Rock | 96% | Rock | 93% |
Vegetation | 75% | Vegetation | 86% |
In the generated coastal-surface classification map, there were some misclassification errors owing to the following. First, some water features were misclassified into rock features due to the coastal materials located under the shallow water surfaces. Second, some rock features were misclassified into water features due to their similar reflectance characteristics caused by the shadows on the rock surfaces. Third, some vegetation features were misclassified into water features or vice versa, also due to their similar reflectance characteristics caused by the shades on their surfaces and so forth. Figure
Examples of the misclassification errors that occurred in the coastal-surface classification map: (a) example of the misclassification in the region where the water features were misclassified into rock features; (b) example of the misclassification in the region where the rock features were misclassified into water features; and (c) example of the misclassification in the region where the vegetation features were misclassified into water features.
In this section, the accuracy of the two shorelines generated by the water-index- and machine-learning-based methods, respectively, was measured through the following steps. First, 100 checkpoints, defined as the second checkpoint group, were generated by manual digitization, and the average distance of these checkpoints was 70 m (see Figure
Locations of the second checkpoint group for the measurement of the accuracy of both shorelines.
Then the accuracy of both shorelines was assessed by measuring the shortest distance from the checkpoints of the second checkpoint group to the first and second shorelines, respectively. Table
Accuracy of the first shoreline generated by the water-index-based method and the second shoreline generated by the machine-learning-based method.
Accuracies of both shorelines | First shoreline (m) | Second shoreline (m) |
---|---|---|
Mean | 2.62 | 0.79 |
Standard deviation | 5.27 | 2.99 |
Maximum | 28.60 | 25.02 |
Line graphs of the shortest distance from each checkpoint to the shorelines generated by the different methods.
As can be seen in Table
Detailed examination of comparison results: (a) locations of the selected Regions 1, 2, 3, and 4 in the entire study area; (b) Region 1, where both shorelines had high accuracy; (c) Region 2, where the second shoreline had better accuracy for the first reason; (d) Region 3, where the second shoreline had better accuracy for the second reason; and (e) Region 4, where both shorelines had low accuracy due to the shaded areas that were not identified in the NDWI image or the coastal-surface classification map.
In conclusion, both shorelines had high accuracy in the well-identified coastal zones while the second shoreline generated by the machine-learning-based method had better accuracy than the first shoreline generated by the water-index-based method in the coastal zones with irregular shapes, light shades, and so forth. Both methods, however, showed inefficient performance for mapping the shorelines in the coastal zones with significant shades that were not identified in the NDWI image or the coastal-surface classification map. In general, the pixels representing the shaded areas have the intensity values lower than other pixels in all multispectral bands [
The shoreline-mapping task using remotely sensed image sources is efficient for the estimation of the shoreline positions without human access. This research compared different methods (the water-index-based method and the machine-learning-based method) for mapping accurate shorelines using a high-resolution satellite image. The water-index-based method is useful for separating the land and water features from multispectral image sources, but it is limited for identifying the various land covers that constitute the coastal zones. The machine-learning-based method is useful for identifying these various coastal features with different spectral-reflectance characteristics, which means that using the machine-learning-based method is better than using the water-index-based method for mapping shorelines using multispectral image sources. There are significant improvements required, however, in future research for the development of an automatic shoreline-mapping process and the estimation of shoreline positions in various coastal zones. First, different machine learning algorithms or any other technique should be applied to generate a more accurate coastal-surface classification map for mapping accurate shorelines in various coastal zones. Second, additional datasets not influenced by the shadows should be integrated into the image sources for mapping accurate shorelines not only in the well-identified coastal zones but also in the shaded coastal zones. Third, the ground truths acquired by the ground-surveying method would be used for measuring accuracies of the generated shorelines and the coastal-surface classification map.
The authors declare that there are no conflicts of interest regarding the publication of this article.
This research was supported by Development of Space Core Technology through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, Information and Communication Technology (ICT) and Future Planning (Grant no. NRF-2014M1A3A3A03067384).