Abstract
Satellite images find various applications today, of which segmenting the objects in a satellite image for map generation is widely used in disaster mitigation planning and recovery. Existing works mostly segment one class of object from satellite images, whereas actual images contain multiple classes of objects. To overcome this shortcoming, this paper describes a system which generates the base map from a satellite image for four classes of objects - Buildings, Roads, Greenery and Water Bodies. Binary and multi-class U-Net architectures are designed and trained with suitable datasets to segment the four classes of objects individually. The trained models can take an input satellite image (RGB, JPG format) of pixel resolution 30–50 cm, individually segment the four classes and generate the base map by combining segmented regions in different colours. The performance is analysed by comparing the Intersection Over Union (IOU) score and cross entropy loss with validation set and existing models. A real time image acquired from Google Earth Pro is tested and the results are subjectively evaluated to infer that 90% of the regions are segmented correctly.
Source Code - https://github.com/sudhamsugurijala/Sat_Image_Seg.
Supported by Sri Sivasubramaniya Nadar College of Engineering.
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Srinivasan, K., Gurijala, S., Sai Chitti Subrahmanyam, V., Swetha, B. (2022). Generating the Base Map of Regions Using an Efficient Object Segmentation Technique in Satellite Images. In: Patel, K.K., Doctor, G., Patel, A., Lingras, P. (eds) Soft Computing and its Engineering Applications. icSoftComp 2021. Communications in Computer and Information Science, vol 1572. Springer, Cham. https://doi.org/10.1007/978-3-031-05767-0_27
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