SVM-Based Detection of Miniature Area of LCLU: A False-Damage Assessment Index for Disaster Management Application

Abstract

In this paper, we reflect on changing the detection environment for addressing the difficulty of detecting miniature area of Land Cover Land Use (LCLU) with a technique using Support Vector Machines (SVMs).We then become accustomed and sophisticatedly changing the Support Vector Machine for designing a supervised learning basis detection that enfolds the False Damage Assessment Index(FDAI). Primarily our proposed detection technique is controls easily the FDAI by simply adjusting two parameters() where it can be facilitate to control sensitivity of detection to the binary classifier and numerical supervised learning algorithm. The experimental results demonstrating about ours proposing detector noticeably improving the detection probability on many existing classifiers in both DAI and FDAI cases