  Most publicly available datasets for image classification are with single
labels, while images are inherently multi-labeled in our daily life. Such an
annotation gap makes many pre-trained single-label classification models fail
in practical scenarios. This annotation issue is more concerned for aerial
images: Aerial data collected from sensors naturally cover a relatively large
land area with multiple labels, while annotated aerial datasets, which are
publicly available (e.g., UCM, AID), are single-labeled. As manually annotating
multi-label aerial images would be time/labor-consuming, we propose a novel
self-correction integrated domain adaptation (SCIDA) method for automatic
multi-label learning. SCIDA is weakly supervised, i.e., automatically learning
the multi-label image classification model from using massive, publicly
available single-label images. To achieve this goal, we propose a novel
Label-Wise self-Correction (LWC) module to better explore underlying label
correlations. This module also makes the unsupervised domain adaptation (UDA)
from single- to multi-label data possible. For model training, the proposed
model only uses single-label information yet requires no prior knowledge of
multi-labeled data; and it predicts labels for multi-label aerial images. In
our experiments, trained with single-labeled MAI-AID-s and MAI-UCM-s datasets,
the proposed model is tested directly on our collected Multi-scene Aerial Image
(MAI) dataset.
