2 research outputs found
Discovering and Generating Hard Examples for Training a Red Tide Detector
Currently, accurate detection of natural phenomena, such as red tide, that
adversely affect wildlife and human, using satellite images has been
increasingly utilized. However, red tide detection on satellite images still
remains a very hard task due to unpredictable nature of red tide occurrence,
extreme sparsity of red tide samples, difficulties in accurate groundtruthing,
etc. In this paper, we aim to tackle both the data sparsity and groundtruthing
issues by primarily addressing two challenges: i) significant lack of hard
examples of non-red tide that can enhance detection performance and ii) extreme
data imbalance between red tide and non-red tide examples. In the proposed
work, we devise a 9-layer fully convolutional network jointly optimized with
two plug-in modules tailored to overcoming the two challenges: i) a hard
negative example generator (HNG) to supplement the hard negative (non-red tide)
examples and ii) cascaded online hard example mining (cOHEM) to ease the data
imbalance. Our proposed network jointly trained with HNG and cOHEM provides
state-of-the-art red tide detection accuracy on GOCI satellite images.Comment: 10 page