Bigdata Based Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification Algorithm
Keywords:
Intermittent Image Clustering Segmentation, Progressive Image Classification Algorithm, Satellite Image, Disaster DetectionAbstract
The analysis of remote sensing image areas is a need for climate detection and management to monitor flood disasters in key environments and applications. The satellite image is most used to detect the disaster analysis in the earth, and it has advantages to capture an earth image, this earth image has more information to give the control technique. Since the acquisition of satellite and aerial imagery, this system detects the flood disaster, and flood detection has become more desirable in recent years with increasing convenience. In this research work, Progressive Image Classification Algorithm (PICA) is proposed to detect the earth disaster and classify the result more effectively. PICA is most essential to overcome robust shadows, proper access to the characteristics of the disaster, false positives by operators, or false that affect the impact of the disaster. It creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches. The proposed PICA detects the earth disaster earlier and the accuracy (95.96%).
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