  Pixel-wise classification in remote sensing identifies entities in
large-scale satellite-based images at the pixel level. Few fully annotated
large-scale datasets for pixel-wise classification exist due to the challenges
of annotating individual pixels. Training data scarcity inevitably ensues from
the annotation challenge, leading to overfitting classifiers and degraded
classification performance. The lack of annotated pixels also necessarily
results in few hard examples of various entities critical for generating a
robust classification hyperplane. To overcome the problem of the data scarcity
and lack of hard examples in training, we introduce a two-step hard example
generation (HEG) approach that first generates hard example candidates and then
mines actual hard examples. In the first step, a generator that creates hard
example candidates is learned via the adversarial learning framework by fooling
a discriminator and a pixel-wise classification model at the same time. In the
second step, mining is performed to build a fixed number of hard examples from
a large pool of real and artificially generated examples. To evaluate the
effectiveness of the proposed HEG approach, we design a 9-layer fully
convolutional network suitable for pixel-wise classification. Experiments show
that using generated hard examples from the proposed HEG approach improves the
pixel-wise classification model's accuracy on red tide detection and
hyperspectral image classification tasks.
