Residual quadratic encoder–decoder architecture for semantic segmentation of satellite images.

Semantic segmentation is used for identification of buildings, roads, vegetation cover, and water body detection in satellite images. Several state-of-the-art deep learning models investigated have a large number of parameters and are difficult to train on low-configuration machines. To resolve this issue, quadratic encoder–decoder (QuadED), and residual quadratic encoder–decoder (ResQuadED) enabled with quadratic convolutional (QuadConv2D) layer are proposed as novel architectures in this manuscript. In the experiments, it is observed that QuadED performs better for binary semantic segmentation and ResQuadED performs better for both binary and multi-class semantic segmentation. As a result, the QuadED achieves an IoU of 76.82%, and 87.57% for the architectures with 5,51,913 and 4,90,088 parameters on the MBRSC and Massachusetts datasets, respectively. The ResQuadED model achieves an IoU of 77.87%, and 89.27% for the architectures with 5,37,585 and 4,54,682 parameters on the MBRSC and Massachusetts datasets, respectively. These results reveal that ResQuadED architecture performs significantly better for the evaluation metrics with fewer parameters than QuadED and existing architectures. [ABSTRACT FROM AUTHOR]