Authors:
R. Avenash
and
P. Viswanath
Affiliation:
Computer Science and Engineering, Indian Institute of Information Technology, Chittoor, Sri City, A.P. and India
Keyword(s):
Semantic Segmentation, Activation Function, Remote Sensing Images, Convolutional Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
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.