Semantic Segmentation of Multispectral Satellite Images Using Residual Convolutional Networks

Satellite imagery is always used to study spatial geographies to find water, residential, farmland, and forest lands; which can be further used for township development and planning, landscape detection etc. Semantic segmentation and image classification are the two crucial procedures in determining the spatial geographies. In order to improve the generalization ability of semantic segmentation algorithms, a combined model of UNet_ResNet is used in this paper. The engineered model is a type of Convolutional Neural Networks using GeoGANs which detects semantic patches in neural networks with smaller sizes and regional characteristics within a certain spatial and pixel scale. However, it faces a semantic segmentation challenge of identifying roadways in metropolitan areas. The model shows an accuracy score from 93% to 97.3% for image classification and segmentation purposes which fares better than the implementation of various existing architectures.