High-resolution landcover classification using Random Forest

Hayes, Matthew M.; Miller, Scott N.; Murphy, Melanie A.

doi:10.1080/2150704x.2014.882526

Cited by 110 publications

(91 citation statements)

References 17 publications

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“…In this paper, we adopted a relatively novel classifier, Random Forest (RF) [18], for urban flood mapping. According to current research [18][19][20][21][22][23], the performance of Random Forest in flood mapping has not been well documented, thus, we are highly motivated to justify its performance. Meanwhile, texture analysis [24][25][26][27][28][29] was also used to provide enough shape and contextual information for the RF classifier to improve its accuracy.…”

Section: Introductionmentioning

confidence: 99%

Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

Feng

Liu

Gong

2015

Water

246

169

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Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing applications, an unmanned aerial vehicle (UAV) can provide high-resolution data with a great potential for fast and accurate detection of inundated areas under complex urban landscapes. In this research, optical imagery was acquired by a mini-UAV to monitor the serious urban waterlogging in Yuyao, China. Texture features derived from gray-level co-occurrence matrix were included to increase the separability of different ground objects. A Random Forest classifier, consisting of 200 decision trees, was used to extract flooded areas in the spectral-textural feature space. Confusion matrix was used to assess the accuracy of the proposed method. Results indicated the following: (1) Random Forest showed good performance in urban flood mapping with an overall accuracy of 87.3% and a Kappa coefficient of 0.746; (2) the inclusion of texture features improved classification accuracy significantly; (3) Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine. The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas.

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Section: Introductionmentioning

confidence: 99%

Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

Feng

Liu

Gong

2015

Water

246

169

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“…The random forest (RF) algorithm, an integrative classifier, has shown to be able to achieve high classification accuracy even when applied to analyze data with stronger noise [27,28]. Currently, the random forest classifier has been widely employed in the landcover classification of mesophyte environments, but is rarely used in the wetland classification for arid and semiarid areas [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

et al. 2016

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Abstract:The wetland classification from remotely sensed data is usually difficult due to the extensive seasonal vegetation dynamics and hydrological fluctuation. This study presents a random forest classification approach for the retrieval of the wetland landcover in the arid regions by fusing the Pléiade-1B data with multi-date Landsat-8 data. The segmentation of the Pléiade-1B multispectral image data was performed based on an object-oriented approach, and the geometric and spectral features were extracted for the segmented image objects. The normalized difference vegetation index (NDVI) series data were also calculated from the multi-date Landsat-8 data, reflecting vegetation phenological changes in its growth cycle. The feature set extracted from the two sensors data was optimized and employed to create the random forest model for the classification of the wetland landcovers in the Ertix River in northern Xinjiang, China. Comparison with other classification methods such as support vector machine and artificial neural network classifiers indicates that the random forest classifier can achieve accurate classification with an overall accuracy of 93% and the Kappa coefficient of 0.92. The classification accuracy of the farming lands and water bodies that have distinct boundaries with the surrounding land covers was improved 5%-10% by making use of the property of geometric shapes. To remove the difficulty in the classification that was caused by the similar spectral features of the vegetation covers, the phenological difference and the textural information of co-occurrence gray matrix were incorporated into the classification, and the main wetland vegetation covers in the study area were derived from the two sensors data. The inclusion of phenological information in the classification enables the classification errors being reduced down, and the overall accuracy was improved approximately 10%. The results show that the proposed random forest classification by fusing multi-sensor data can retrieve better wetland landcover information than the other classifiers, which is significant for the monitoring and management of the wetland ecological resources in arid areas.

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“…Random forests is a powerful algorithm, which is increasingly used in the classification of remote sensing images [46,47]. Random forests can handle highly non-linear interactions and classification boundaries of the multi-temporal spectral data.…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

Sharma

Hara

Tateishi

2017

Land

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This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-validation approach was applied for evaluating the performance of different datasets. Cross-validation with the rich-feature datasets-with a sample size of 390-showed that the MODIS datasets provided highest classification accuracy (Overall accuracy = 0.80, Kappa coefficient = 0.77) compared with Landsat 8 (Overall accuracy = 0.77, Kappa coefficient = 0.74) and Sentinel-2 (Overall accuracy = 0.66, Kappa coefficient = 0.61) datasets. As a result, temporally rich datasets were found to be crucial for the vegetation physiognomic classification. However, in the case of Landsat 8 or Sentinel-2 datasets, sample size could be increased excessively as around 9800 ground truth points could be prepared within 390 MODIS pixel-sized polygons. The increase in the sample size significantly enhanced the classification using Landsat-8 datasets (Overall accuracy = 0.86, Kappa coefficient = 0.84). However, Sentinel-2 datasets (Overall accuracy = 0.77, Kappa coefficient = 0.74) could not perform as much as the Landsat-8 datasets, possibly because of temporally limited datasets covered by the Sentinel-2 satellites so far. A combination of the Landsat-8 and Sentinel-2 datasets slightly improved the classification (Overall accuracy = 0.89, Kappa coefficient = 0.87) than using the Landsat 8 datasets separately. Regardless of the fact that Landsat 8 and Sentinel-2 datasets have lower temporal resolutions than MODIS datasets, they could enhance the classification of otherwise challenging vegetation physiognomic types due to possibility of training a wider variation of physiognomic types at 30 m resolution. Based on these findings, an up-to-date 30 m resolution vegetation map was generated by using Landsat 8 and Sentinel-2 datasets, which showed better accuracy than the existing map in Japan.

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High-resolution landcover classification using Random Forest

Cited by 110 publications

References 17 publications

Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China

Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

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