SVM-based soft classification of urban tree species using very high-spatial resolution remote-sensing imagery

Zhou, Jianhua; Qin, Jun; Gao, Xinbo; Leng, Hanbing

doi:10.1080/01431161.2016.1178867

Cited by 14 publications

(8 citation statements)

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“…In addition to the RF classifier, the other two machine learning algorithms, SVM and ANN, have also been implemented to perform a comparative study because they have been widely used in remote sensing image classification. The SVM is to search the hyperplane with maximum interval in a linear space, and can achieve good classification for the data with small signatures and high dimensions [46], while the ANN performs the training process by constant modification of the weights through weighted accumulation of the input layers and inverse propagation of errors [47]. The SVM classifier is trained with the kernel of radial basis function.…”

Section: Experiments and Resultsmentioning

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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Section: Experiments and Resultsmentioning

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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“…Ke et al [9] used three segmentation schemes to evaluate the synergistic use of high spatial resolution multispectral imagery and low-posting-density LiDAR data for forest species classification using an object-based approach and synergistic use improved the forest classification. However, these methods require manual feature selection, which is subjective and therefore complicates the extraction of high-quality features [12][13][14]. With the development of deep learning [15], increasing numbers of researchers are using neural networks to automatically extract features, thereby eliminating the need for manual feature selection [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

2020

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Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and generally low accuracy, whereas deep learning technology can automatically extract image features to achieve end-to-end classification. Therefore, a tree classification method based on deep learning is proposed in this study. This method combines the semantic segmentation network U-Net and the feature extraction network ResNet into an improved Res-UNet network, where the convolutional layer of the U-Net network is represented by the residual unit of ResNet, and linear interpolation is used instead of deconvolution in each upsampling layer. At the output of the network, conditional random fields are used for post-processing. This network model is used to perform classification experiments on airborne orthophotos of Nanning Gaofeng Forest Farm in Guangxi, China. The results are then compared with those of U-Net and ResNet networks. The proposed method exhibits higher classification accuracy with an overall classification accuracy of 87%. Thus, the proposed model can effectively implement forest tree species classification and provide new opportunities for tree species classification in southern China.

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“…For the classification of tree species, the use of very high-resolution imagery has been shown to offer unique benefits. The small size of individual pixels allows one to capture the variation within a tree crown at a more detailed level, therefore increasing the potential of defining meaningful textural features [20,60,72,88]. Puttonen et al [72] made an explicit distinction between the illuminated and shaded part of a tree crown, using the mean value of each part and the ratio between the two parts to train their classifier.…”

Section: Imagery With a Very High Spatial Resolution (≤1 M)mentioning

confidence: 99%

“…Distance to the nearest objects can also be used to weigh class probabilities and derive measures of density. Zhou et al [88] included density-related features to capture the spatial structure of neighboring tree species and found it to be beneficial for defuzzifying an initial fuzzy classification based on high-resolution aerial imagery. Contextual features can also be used for the semantic mapping of functional vegetation types, where the plant configuration or the specific embedding of a vegetated area in the urban context plays an important role [30,87,110].…”

Section: Contextual Featuresmentioning

confidence: 99%

Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review

Neyns

Canters

2022

Remote Sensing

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Green space is increasingly recognized as an important component of the urban environment. Adequate management and planning of urban green space is crucial to maximize its benefits for urban inhabitants and for the urban ecosystem in general. Inventorying urban vegetation is a costly and time-consuming process. The development of new remote sensing techniques to map and monitor vegetation has therefore become an important topic of interest to many scholars. Based on a comprehensive survey of the literature, this review article provides an overview of the main approaches proposed to map urban vegetation from high-resolution remotely sensed data. Studies are reviewed from three perspectives: (a) the vegetation typology, (b) the remote sensing data used and (c) the mapping approach applied. With regard to vegetation typology, a distinction is made between studies focusing on the mapping of functional vegetation types and studies performing mapping of lower-level taxonomic ranks, with the latter mainly focusing on urban trees. A wide variety of high-resolution imagery has been used by researchers for both types of mapping. The fusion of various types of remote sensing data, as well as the inclusion of phenological information through the use of multi-temporal imagery, prove to be the most promising avenues to improve mapping accuracy. With regard to mapping approaches, the use of deep learning is becoming more established, mostly for the mapping of tree species. Through this survey, several research gaps could be identified. Interest in the mapping of non-tree species in urban environments is still limited. The same holds for the mapping of understory species. Most studies focus on the mapping of public green spaces, while interest in the mapping of private green space is less common. The use of imagery with a high spatial and temporal resolution, enabling the retrieval of phenological information for mapping and monitoring vegetation at the species level, still proves to be limited in urban contexts. Hence, mapping approaches specifically tailored towards time-series analysis and the use of new data sources seem to hold great promise for advancing the field. Finally, unsupervised learning techniques and active learning, so far rarely applied in urban vegetation mapping, are also areas where significant progress can be expected.

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SVM-based soft classification of urban tree species using very high-spatial resolution remote-sensing imagery

Cited by 14 publications

References 50 publications

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

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

An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review

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