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.
Keywords:
Authors
Ali Jafari
Malek-Ashtar University of Technology Tehran, Iran
Mostafa Heidarpour
Malek-Ashtar University of Technology Tehran, Iran