Abstract
Land-use information provides a direct representation of the effect of human activities on the environment, and an accurate and efficient land-use classification of remote sensing images is an important element of land-use and land-cover change research. To solve the problems associated with traditional land-use classification methods (e.g., rapid increase in dimensionality of data, inadequate feature extraction, and low running efficiency), a method that combines object-oriented approach with deep convolutional neural network (COCNN) is presented. First, a multi-scale segmentation algorithm is used to segment images to generate image segmentation regions with high homogeneity. Second, a typical rule set of feature objects is constructed on the basis of the object-oriented segmentation results, and the segmentation objects are classified and extracted to form a training sample set. Third, a convolutional neural network (CNN) model structure is modified to improve classification performance, and the training algorithm is optimized to avoid the overfitting phenomenon that occurs during training using small datasets. Ten land-use types are classified by using the remote sensing images covering the area around Fuxian Lake as an example. By comparing the COCNN method with the method based solely on CNN, precision and kappa index were selected to evaluate the classification accuracy of the two methods. For the COCNN method, on the basis of the classification statistics, precision and kappa index coefficients are 96.2% and 0.96, respectively, which are 8.98% and 0.1 higher than those of the method based solely on CNN. Experimental results show that the COCNN method reasonably and efficiently combines object-oriented and deep learning approaches, thereby effectively solving the problem of the inaccurate classification of typical features with better classification accuracy than the simple use of CNN.
Highlights
Land-use and land-cover change (LUCC), which is closely related to global climate change and changes in ecosystems and biodiversity, reflects the effects of human activities and climate change on the ecological environment of the Earth’s surface (Blasi et al 2008; Yang et al 2014)
To solve the problems associated with traditional land-use classification methods, a method that combines object-oriented approach with deep convolutional neural network (COCNN) is presented
A convolutional neural network (CNN) model structure is modified to improve classification performance, and the training algorithm is optimized to avoid the overfitting phenomenon that occurs during training using small datasets
Summary
Land-use and land-cover change (LUCC), which is closely related to global climate change and changes in ecosystems and biodiversity, reflects the effects of human activities and climate change on the ecological environment of the Earth’s surface (Blasi et al 2008; Yang et al 2014). The diversity of objects within a given class increases as does the similarity of objects in different classes due to spectral confusion in HRRS images These properties reduce the effectiveness of traditional classification methods based on low-level features (Paisitkriangkrai et al 2016). Object-oriented classification strategies classify objects on the basis of homogeneous multi-pixels and use spectral, spatial, shape, and other features of images to perform type judgments together, thereby breaking through the limitations of pixel-based classification. A landuse-type classification method (COCNN) based on the technical characteristics of object-oriented and deep learning approaches is proposed on the basis of the analysis of the advantages of the two methods.
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