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Classification of Potential Water Bodies Using Landsat 8 OLI and a Combination of Two Boosted Random Forest Classifiers.

Ko BC, Kim HH, Nam JY - Sensors (Basel) (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Keimyung University, Sindang-dong, Dalseo-gu, Daegu 704-701, Korea. niceko@kmu.ac.kr.

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.

No MeSH data available.


Three areas of Korea with different water types and topologies for performance evaluation. These images include many types of water features such as rivers, lakes and reservoirs.
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sensors-15-13763-f003: Three areas of Korea with different water types and topologies for performance evaluation. These images include many types of water features such as rivers, lakes and reservoirs.

Mentions: Figure 3 shows the test images of the three areas.


Classification of Potential Water Bodies Using Landsat 8 OLI and a Combination of Two Boosted Random Forest Classifiers.

Ko BC, Kim HH, Nam JY - Sensors (Basel) (2015)

Three areas of Korea with different water types and topologies for performance evaluation. These images include many types of water features such as rivers, lakes and reservoirs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507615&req=5

sensors-15-13763-f003: Three areas of Korea with different water types and topologies for performance evaluation. These images include many types of water features such as rivers, lakes and reservoirs.
Mentions: Figure 3 shows the test images of the three areas.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Keimyung University, Sindang-dong, Dalseo-gu, Daegu 704-701, Korea. niceko@kmu.ac.kr.

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.

No MeSH data available.