  360{\deg} spherical images have advantages of wide view field, and are
typically projected on a planar plane for processing, which is known as
equirectangular image. The object shape in equirectangular images can be
distorted and lack translation invariance. In addition, there are few publicly
dataset of equirectangular images with labels, which presents a challenge for
standard CNNs models to process equirectangular images effectively. To tackle
this problem, we propose a methodology for converting a perspective image into
equirectangular image. The inverse transformation of the spherical center
projection and the equidistant cylindrical projection are employed. This
enables the standard CNNs to learn the distortion features at different
positions in the equirectangular image and thereby gain the ability to
semantically the equirectangular image. The parameter, {\phi}, which determines
the projection position of the perspective image, has been analyzed using
various datasets and models, such as UNet, UNet++, SegNet, PSPNet, and DeepLab
v3+. The experiments demonstrate that an optimal value of {\phi} for effective
semantic segmentation of equirectangular images is 6{\pi}/16 for standard CNNs.
Compared with the other three types of methods (supervised learning,
unsupervised learning and data augmentation), the method proposed in this paper
has the best average IoU value of 43.76%. This value is 23.85%, 10.7% and
17.23% higher than those of other three methods, respectively.
