Application of a parallel spectral-spatial convolution neural network in object-oriented remote sensing land use classification

作者：Cui, Wei^*; Zheng, Zhendong; Zhou, Qi; Huang, Jiejun; Yuan, Yanbin

来源：Remote Sensing Letters, 2018, 9(4): 334-342.

DOI：10.1080/2150704X.2017.1420265

• SCENE CLASSIFICATION

Due to the abundance of spatial information and relative lack of spectral information in high spatial resolution remote sensing images, a land use classification method for high-resolution remote sensing images is proposed based on a parallel spectral-spatial convolutional neural network (CNN) and object-oriented remote sensing technology. The contour of a remote sensing object is taken as the boundary and the set of pixels that comprises the object are extracted to form the input data set for the deep neural network. The proposed network considers both the features of the object and the pixels which forms the object. The spatial and spectral features of remote sensing image objects are extracted independently in the parallel network using panchromatic and multispectral remote sensing techniques. Then, through a fully connected layer, both spectral and spatial information are integrated to produce remote sensing object class coding. The experimental results demonstrate that the parallel spectral-spatial CNN, which combines spatial and spectral features, achieves better classification performance than the individual CNN. Therefore, the proposed method provides a novel approach to land use classification based on high spatial resolution remote sensing images.

• 出版日期2018
• 单位武汉理工大学