Abstract
Land use classification is a fundamental task of information extraction from remote sensing imagery. Semantic segmentation based on deep convolutional neural networks (DCNNs) has shown outstanding performance in this task. However, these methods are still affected by the loss of spatial features. In this study, we proposed a new network, called the dense-coordconv network (DCCN), to reduce the loss of spatial features and strengthen object boundaries. In this network, the coordconv module is introduced into the improved DenseNet architecture to improve spatial information by putting coordinate information into feature maps. The proposed DCCN achieved an obvious performance in terms of the public ISPRS (International Society for Photogrammetry and Remote Sensing) 2D semantic labeling benchmark dataset. Compared with the results of other deep convolutional neural networks (U-net, SegNet, Deeplab-V3), the results of the DCCN method improved a lot and the OA (overall accuracy) and mean F1 score reached 89.48% and 86.89%, respectively. This indicates that the DCCN method can effectively reduce the loss of spatial features and improve the accuracy of semantic segmentation in high resolution remote sensing imagery.
Highlights
Land use classification information offers a significant indication of human activities in an urban environment [1]
To further reduce the loss of high-frequency details and object boundaries in high resolution remote sensing images, inspired by coordinate convolution, we extended the coordconv module into the Fully Connected (FC)-DenseNet and designed a novel encoder–decoder architecture called the dense-coordconv network (DCCN) to solve the complex urban land use classification tasks by using high-resolution remote sensing images
Similar to other encoder–decoder architectures, this paper employed an encoder–decoder architecture based on the architecture of the FC-DenseNet
Summary
Land use classification information offers a significant indication of human activities in an urban environment [1]. This information can provide the basic datasets for change detection [2], landscape pattern [3], and urban heat island effects [4]. With the rapid development of remote sensing technology, this method has become a major way to obtain land use information, and innumerable high resolution remote sensing images are used to extract spatial information regarding urban land use. Improvements in spatial resolution increase the internal variability of homogenous land cover units and decrease the statistical separability of land cover classes in the spectral space, not necessarily achieving better classification [5]. The internal variability of the high-resolution images makes the land use classification more challenging [6,7].
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