Abstract
Over the past few years, deep learning algorithms have held immense promise for better multi-spectral (MS) optical remote sensing image (RSI) analysis. Most of the proposed models, based on convolutional neural network (CNN) and fully convolutional network (FCN), have been applied successfully on computer vision images (CVIs). However, there is still a lack of exploration of spectra correlation in MS RSIs. In this study, a deep neural network with a spectrum separable module (DSSM) is proposed for semantic segmentation, which enables the utilization of MS characteristics of RSIs. The experimental results obtained on Zurich and Potsdam datasets prove that the spectrum-separable module (SSM) extracts more informative spectral features, and the proposed approach improves the segmentation accuracy without increasing GPU consumption.
Highlights
In order to evaluate the effectiveness of the proposed framework, we adopted intersection over union (IoU) as an evaluation indicator for a single category of ground objects
The number of spectra of the input image only affects the thickness of the convolution kernel, and we already know from the previous analysis that the thickness of the convolution kernel in the convolutional neural network (CNN) does not affect the parameters of the model; their consumption cost remained the same
The framework is mainly composed of an spectrumseparable module (SSM) module and a deep neural network
Summary
The DSSM with RGB-NIR continued to demonstrate a better segmentation capability than the other three strategies, which increased by 1.92% in terms of FW IoU. At this point, the number of spectra of the input image only affects the thickness of the convolution kernel, and we already know from the previous analysis that the thickness of the convolution kernel in the CNN does not affect the parameters of the model; their consumption cost remained the same. As compared with the aforementioned three strategies, the consumption cost of the DSSM with RGB-NIR increased by about 20%.
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