A Dual-Branch Deep Learning Architecture for Multisensor and Multitemporal Remote Sensing Semantic Segmentation

Abstract

Multisensor data analysis allows exploiting heterogeneous data regularly acquired by the many available remote sensing (RS) systems. Machine- and deep-learning methods use the information of heterogeneous sources to improve the results obtained by using single-source data. However, the state-of-the-art methods analyze either the multiscale information of multisensor multiresolution images or the time component of image time series. We propose a supervised deep-learning classification method that jointly performs a multiscale and multitemporal analysis of RS multitemporal images acquired by different sensors. The proposed method processes very-high-resolution (VHR) images using a residual network with a wide receptive field that handles geometrical details and multitemporal high-resolution (HR) image using a 3-D convolutional neural network that analyzes both the spatial and temporal information. The multiscale and multitemporal features are processed together in a decoder to retrieve a land-cover map. We tested the proposed method on two multisensor and multitemporal datasets. One is composed of VHR orthophotos and Sentinel-2 multitemporal images for pasture classification, and another is composed of VHR orthophotos and Sentinel-1 multitemporal images. Results proved the effectiveness of the proposed classification method.

Keywords

• Deep learning (DL) classification
• multiresolution
• multisensor data
• multitemporal images
• remote sensing (RS)
• very-high-resolution (VHR) images