Integrating UAV in IoT for RoI Classification in Remote Images

Abstract

The paper presents a cheap and efficient solution for remote processing of images taken by a team of UAVs (Unmanned Aerial Vehicles). The work objective was to implement an integrated system for detection and classification of regions of interest (RoIs), in the case of flood events. The UAVs are considered as objects of the internet. This means the integration of UAVs in IoT (Internet of Things) as intelligent objects. The investigated RoIs are: water, grass, forests, buildings, and roads. A multi UAV – multi GCS (Ground Control Station) solution is proposed. Due to this integration, land segmentation by image processing can be efficiently made in real time. For RoI detection and evaluation a multi CNN structure is used as a multi classifier structure. Particularly, a CNN classifier is implemented for each type of RoI and all the CNNs work in parallel. The orthophotoplan obtained from remote acquired images are successively decomposed in adjacent images of size 6000 × 4000 and next in overlapping patches of size 65 × 65 pixels for classifier learning or for testing. Finally, the images are segmented in RoIs by a multi-mask technique and the percentage of each RoI is calculated. The accuracy of segmentation and the processing time, evaluated from 10 real images, was better than in other reported cases.