Abstract
Moderate spatial resolution (MSR) satellite images, which hold a trade-off among radiometric, spectral, spatial and temporal characteristics, are extremely popular data for acquiring land cover information. However, the low accuracy of existing classification methods for MSR images is still a fundamental issue restricting their capability in urban land cover mapping. In this study, we proposed a hybrid convolutional neural network (H-ConvNet) for improving urban land cover mapping with MSR Sentinel-2 images. The H-ConvNet was structured with two streams: one lightweight 1D ConvNet for deep spectral feature extraction and one lightweight 2D ConvNet for deep context feature extraction. To obtain a well-trained 2D ConvNet, a training sample expansion strategy was introduced to assist context feature learning. The H-ConvNet was tested in six highly heterogeneous urban regions around the world, and it was compared with support vector machine (SVM), object-based image analysis (OBIA), Markov random field model (MRF) and a newly proposed patch-based ConvNet system. The results showed that the H-ConvNet performed best. We hope that the proposed H-ConvNet would benefit for the land cover mapping with MSR images in highly heterogeneous urban regions.
Highlights
Accurate and timely urban land cover mapping plays a major role in many urban applications, such as ecosystem monitoring, land management, planning and landscape analysis [1,2,3]
With the implementation of H-convolutional neural network (ConvNet), an urban land cover map, which consists of four biophysical land cover categories of water, vegetation, soil and impervious surfaces, was produced (Figure 6)
(1) the remote sensing images are acquired from a satellite view and with significantly changeable spatial resolutions, whereas the images in computer vision are always acquired from a human view and with high spatial resolution
Summary
Accurate and timely urban land cover mapping plays a major role in many urban applications, such as ecosystem monitoring, land management, planning and landscape analysis [1,2,3]. Sensed satellite images can quickly capture land cover changes at a large-scale and have been widely used for land cover mapping for decades [4,5]. Compared with rural regions mainly covering natural land surfaces (e.g., grass and water), urban regions have undergone a nearly complete reconstruction, with a highly heterogeneous land surface. There is generally a confusion among land cover categories in mapping urban land cover with remote sensing techniques, and mapping efficiency and accuracy are hampered. With decades of optical imaging technique development, we can access vast amounts of high-quality moderate spatial resolution (MSR) satellite images (e.g., Landsat-8 and Sentinel-2 images), which allows the broad applications of land cover mapping over large-scale areas. Many algorithms have been successfully widely used in remotely sensed image classification for different application requirements, including ISODATA and K-means algorithms [6,7], maximum likelihood classifier (MLC) [8], neural network (NN) [9], random forest (RF) [10] and support vector machine (SVM) algorithms [11]
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