A Decision Fusion Framework For High-Resolution Remote-Sensing Image Classification

Publish Year: 1394
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:

ICMVIP09_085

تاریخ نمایه سازی: 6 اسفند 1395

Abstract:

Classification of high-resolution remote-sensing images is a challenging research area. In this paper we proposed a novel decision fusion framework to combine bag of features (BOF) based classifiers. The proposed framework, can also be used in multi category image classification applications. A singlevoting algorithm is used for decision fusion and an ambiguity detection module is used to determine the ambiguous situations. An ambiguous situation will occur during multi-category voting, where more than one class got the maximum votes, and also when the number of the same votes doesn’t exceeds a desired threshold. To resolve this situation we proposed to use the earth mover's distance (EMD) which is a metric for histogram matching. Indeed, we used the EMD to compare the BOF based histogram of images with the centroid classes. Finally, to evaluate the proposed framework, we used a multi-category remote-sensing image dataset and compared the proposed approach with several other similar approaches with BOF based classifiers. The experimental results demonstrate the effectiveness of the proposed framework.

Authors

Ali Jafari

Malek-Ashtar University of Technology Tehran, Iran

Mostafa Heidarpour

Malek-Ashtar University of Technology Tehran, Iran