Abstract
An increasing number of applications require land cover information from remote sensing images, thereby resulting in an urgent demand for automatic land use and land cover classification. Therefore, effectively improving the accuracy of land cover classification is a main objective in remote sensing image processing. We propose a land cover classification postprocessing framework based on iterative self-adaptive superpixel segmentation (LCPP-ISSS) for remote sensing image data. This framework can further optimize the land cover classification results obtained by neural networks without changing the network structure. First, we propose the iterative self-adaptive superpixel segmentation algorithm for high-resolution remote sensing images to extract the boundary information of different land cover classes. Then, we propose a land cover classification result optimization method based on patch complexity to optimize the classification result by combining the boundary information with the semantic information. In an experiment, we compare the classification accuracy before and after using LCPP-ISSS and with other common methods. The results show that LCPP-ISSS outperforms the dense conditional random field and provides a 4% increase in the mean intersection over union and a 10% increase in overall accuracy.
Highlights
With continuous improvements in data resolution in recent years, the details and boundaries of various land cover in images have become clearer
LCPP-iterative self-adaptive superpixel segmentation (ISSS) includes mainly two parts: segmentation and optimization, which are completed by ISSS and LCOM-PC, respectively
A postprocessing framework named LCPP-ISSS is proposed to optimize the results of land cover classification
Summary
With continuous improvements in data resolution in recent years, the details and boundaries of various land cover in images have become clearer. The demand for an automatic land cover/land use classification method that can better handle the detailed information of high-resolution remote sensing images has become increasingly urgent. Many researchers have performed land cover classification based on traditional machine learning[1,2] and deep learning methods.[3,4] The full convolutional network (FCN)[5] and its improved network have been applied for land cover classification tasks in high-resolution remote sensing images, which have achieved a certain effect.[6,7] the distortion caused by the convolution structure of upsampling and downsampling will inevitably lead to errors, such as edge blurring and holes. Changing the network structure can improve classification accuracy to a certain extent, neural networks have poor interpretability and operability due to the “black box” effect, and some specific problems may appear in actual
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