Affiliation:
1. Green & Landscape Architecture, Dankook University, Cheonan 31116, Republic of Korea
Abstract
Wetlands possess significant ecological value and play a crucial role in the environment. Recent advancements in remote exploration technology have enabled a quantitative analysis of wetlands through surveys on the type of cover present. However, the classification of complex cover types as land cover types in wetlands remains challenging, leading to ongoing studies aimed at addressing this issue. With the advent of high-resolution sensors in unmanned aerial vehicles (UAVs), researchers can now obtain detailed data and utilize them for their investigations. In this paper, we sought to establish an effective method for classifying centimeter-scale images using multispectral and hyperspectral techniques. Since there are numerous classes of land cover types, it is important to build and extract effective training data for each type. In addition, computer vision-based methods, especially those that combine deep learning and machine learning, are attracting considerable attention as high-accuracy methods. Collecting training data before classifying by cover type is an important factor that which requires effective data sampling. To obtain accurate detection results, a few data sampling techniques must be tested. In this study, we employed two data sampling methods (endmember and pixel sampling) to acquire data, after which their accuracy and detection outcomes were compared through classification using spectral angle mapper (SAM), support vector machine (SVM), and artificial neural network (ANN) approaches. Our findings confirmed the effectiveness of the pixel-based sampling method, demonstrating a notable difference of 38.62% compared to the endmember sampling method. Moreover, among the classification methods employed, the SAM technique exhibited the highest effectiveness, with approximately 10% disparity observed in multispectral data and 7.15% in hyperspectral data compared to the other models. Our findings provide insights into the accuracy and classification outcomes of different models based on the sampling method employed in spectral imagery.
Funder
Korea Environment Industry & Technology Institute
Korea Ministry of Environmen
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
Reference79 articles.
1. Freshwater biodiversity: Importance, threats, status and conservation challenges;Dudgeon;Biol. Rev. Camb. Philos. Soc.,2006
2. Emerging threats and persistent conservation challenges for freshwater biodiversity;Reid;Biol. Rev.,2019
3. Tidal wetland stability in the face of human impacts and sea-level rise;Kirwan;Nature,2013
4. Comparison of Multi-Class and Fusion of Multiple Single-Class SegNet Model for Mapping Karst Wetland Vegetation Using UAV Images;Deng;Sci. Rep.,2022
5. Guo, M., Li, J., Sheng, C., Xu, J., and Wu, L. (2017). A review of wetland remote sensing. Sensors, 17.