  Aerial image classification is of great significance in remote sensing
community, and many researches have been conducted over the past few years.
Among these studies, most of them focus on categorizing an image into one
semantic label, while in the real world, an aerial image is often associated
with multiple labels, e.g., multiple object-level labels in our case. Besides,
a comprehensive picture of present objects in a given high resolution aerial
image can provide more in-depth understanding of the studied region. For these
reasons, aerial image multi-label classification has been attracting increasing
attention. However, one common limitation shared by existing methods in the
community is that the co-occurrence relationship of various classes, so called
class dependency, is underexplored and leads to an inconsiderate decision. In
this paper, we propose a novel end-to-end network, namely class-wise
attention-based convolutional and bidirectional LSTM network (CA-Conv-BiLSTM),
for this task. The proposed network consists of three indispensable components:
1) a feature extraction module, 2) a class attention learning layer, and 3) a
bidirectional LSTM-based sub-network. Particularly, the feature extraction
module is designed for extracting fine-grained semantic feature maps, while the
class attention learning layer aims at capturing discriminative class-specific
features. As the most important part, the bidirectional LSTM-based sub-network
models the underlying class dependency in both directions and produce
structured multiple object labels. Experimental results on UCM multi-label
dataset and DFC15 multi-label dataset validate the effectiveness of our model
quantitatively and qualitatively.
