Automatic martian dust storm detection via decision level fusion basedondeep extreme learning machine
Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · 2017
This paper presents an automatic Martian dust storm detection via decision level fusion (DLF) based on deep extreme learning machine (DELM). Since Martian images are taken in multi-wavelength bands, DLF techniques which output a final classification result by integrating multiple classification results are necessary. Furthermore, since the number of Martian images taken by satellites is different for each region, the number of the classification results to be integrated is different. Thus, we present a new DLF framework based on confidence values of the classification results. Specifically, we generate multiple extreme learning machines with kernel classifiers to obtain their classification results. Moreover, we monitor the classification results as confidence values and select the same number of the classification results with high confidence for each region. Finally, these selected results can be integrated by using a DLF based on DELM, which is a multilayered ELM. This integration framework is the biggest contribution of our method. Experimental results show the effectiveness of the DLF based on DELM.