Semantic Segmentation of Satellite Images using a Modified CNN with Hard-Swish Activation Function

Remote sensing is a key strategy used to obtain information related to the Earth's resources and its usage patterns. Semantic segmentation of a remotely sensed image in the spectral, spatial and temporal domain is an important preprocessing step where different classes of objects like crops, water bodies, roads, buildings are localized by a boundary. The paper proposes to use the Convolutional Neural Network (CNN) called U-HardNet with a new and novel activation function called the Hard-Swish for segmenting remotely sensed images. Along with the CNN, for a precise localization, the paper proposes to use IHS transformed images with binary cross entropy loss minimization. Experiments are done with publicly available images provided by DSTL (Defence Science and Technology Laboratory) for object recognition and a comparison is drawn with some recent relevant techniques.