  Object detection and semantic segmentation are two main themes in object
retrieval from high-resolution remote sensing images, which have recently
achieved remarkable performance by surfing the wave of deep learning and, more
notably, convolutional neural networks (CNNs). In this paper, we are interested
in a novel, more challenging problem of vehicle instance segmentation, which
entails identifying, at a pixel-level, where the vehicles appear as well as
associating each pixel with a physical instance of a vehicle. In contrast,
vehicle detection and semantic segmentation each only concern one of the two.
We propose to tackle this problem with a semantic boundary-aware multi-task
learning network. More specifically, we utilize the philosophy of residual
learning (ResNet) to construct a fully convolutional network that is capable of
harnessing multi-level contextual feature representations learned from
different residual blocks. We theoretically analyze and discuss why residual
networks can produce better probability maps for pixel-wise segmentation tasks.
Then, based on this network architecture, we propose a unified multi-task
learning network that can simultaneously learn two complementary tasks, namely,
segmenting vehicle regions and detecting semantic boundaries. The latter
subproblem is helpful for differentiating closely spaced vehicles, which are
usually not correctly separated into instances. Currently, datasets with
pixel-wise annotation for vehicle extraction are ISPRS dataset and IEEE GRSS
DFC2015 dataset over Zeebrugge, which specializes in semantic segmentation.
Therefore, we built a new, more challenging dataset for vehicle instance
segmentation, called the Busy Parking Lot UAV Video dataset, and we make our
dataset available at http://www.sipeo.bgu.tum.de/download so that it can be
used to benchmark future vehicle instance segmentation algorithms.
