Abstract
This study proposes a new water body classification method using top-of-atmosphere (TOA) reflectance and water indices (WIs) of the Landsat 8 Operational Land Imager (OLI) sensor and its corresponding random forest classifiers. In this study, multispectral images from the OLI sensor are represented as TOA reflectance and WI values because a classification result using two measures is better than raw spectral images. Two types of boosted random forest (BRF) classifiers are learned using TOA reflectance and WI values, respectively, instead of the heuristic threshold or unsupervised methods. The final probability is summed linearly using the probabilities of two different BRFs to classify image pixels to water class. This study first demonstrates that the Landsat 8 OLI sensor has higher classification rate because it provides improved signal-to-ratio radiometric by using 12-bit quantization of the data instead of 8-bit as available from other sensors. In addition, we prove that the performance of the proposed combination of two BRF classifiers shows robust water body classification results, regardless of topology, river properties, and background environment.
Highlights
IntroductionMaintaining clean rivers and lakes is a prerequisite for supplying stable and safe water for humans
Maintaining clean rivers and lakes is a prerequisite for supplying stable and safe water for humans.Conventional water quality assessments are limited to in situ collection and measurement of water samples from several spots of a long river or a wide lake for subsequent laboratory analyses [1].Even though this method is accurate, it requires substantial time and effort for continuous observation; satellite remote sensing has been used because of its cost-effectiveness and ability to overcome the constraints of conventional methods
To solve the problem of supervised learning, our study proposes a new water body classification algorithm that uses a combination of two boosted random forest (BRF) classifiers based on top-of-atmosphere (TOA) reflectance values and spectral water indices (WIs), which were estimated only from the Landsat 8 Operational Land Imager (OLI) sensors without using Thermal InfraRed Sensor (TIRS)
Summary
Maintaining clean rivers and lakes is a prerequisite for supplying stable and safe water for humans. Conventional water body classification methods apply one or more heuristic thresholds to spectral images. These methods are simple and obtain good classification results from limited terrain. Classifier-based methods deliver better water body classification performance than threshold-based methods because these methods do not need to set heuristic thresholds In these types of methods, supervised and non-supervised learning techniques are used for water body classification with multispectral images. To solve the problem of supervised learning, our study proposes a new water body classification algorithm that uses a combination of two boosted random forest (BRF) classifiers based on top-of-atmosphere (TOA) reflectance values and spectral WIs, which were estimated only from the Landsat 8 OLI sensors without using TIRS.
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