Limits...
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.


Comparison of OA performance according to the changes of weight for Equation (13).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4507615&req=5

sensors-15-13763-f004: Comparison of OA performance according to the changes of weight for Equation (13).

Mentions: The best classification performance was obtained for Area 3, which had an average OA of 99.92% and average Kappa of 0.9949. In contrast, Area 1 had an average OA of 99.88% and average Kappa of 0.9935. Even though the performance of SVM is similar to the proposed method, the processing speed of the proposed method is approximately 12.91 s, which is about 6 times faster than the SVM method (82.4 s) using the same testing images as shown in Figure 4. In case of RF-based method, it has somewhat lower performance than BRF-based method. From this result, we know that the segmentation accuracy can be improved by simple boosting of RF. The main reason for higher classification rate of our proposed method is that our algorithm found many potential water body pixels through individual BRF using TOA reflectance and WIs features in the first step. Our method also eliminated a large amount of false water body pixels in the second step by averaging the output probabilities of two different BRFs.


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)

Comparison of OA performance according to the changes of weight for Equation (13).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-13763-f004: Comparison of OA performance according to the changes of weight for Equation (13).
Mentions: The best classification performance was obtained for Area 3, which had an average OA of 99.92% and average Kappa of 0.9949. In contrast, Area 1 had an average OA of 99.88% and average Kappa of 0.9935. Even though the performance of SVM is similar to the proposed method, the processing speed of the proposed method is approximately 12.91 s, which is about 6 times faster than the SVM method (82.4 s) using the same testing images as shown in Figure 4. In case of RF-based method, it has somewhat lower performance than BRF-based method. From this result, we know that the segmentation accuracy can be improved by simple boosting of RF. The main reason for higher classification rate of our proposed method is that our algorithm found many potential water body pixels through individual BRF using TOA reflectance and WIs features in the first step. Our method also eliminated a large amount of false water body pixels in the second step by averaging the output probabilities of two different BRFs.

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.