We present a texture network called Deep Encoding Pooling Network (DEP) for
the task of ground terrain recognition. Recognition of ground terrain is an
important task in establishing robot or vehicular control parameters, as well
as for localization within an outdoor environment. The architecture of DEP
integrates orderless texture details and local spatial information and the
performance of DEP surpasses state-of-the-art methods for this task. The GTOS
database (comprised of over 30,000 images of 40 classes of ground terrain in
outdoor scenes) enables supervised recognition. For evaluation under realistic
conditions, we use test images that are not from the existing GTOS dataset, but
are instead from hand-held mobile phone videos of similar terrain. This new
evaluation dataset, GTOS-mobile, consists of 81 videos of 31 classes of ground
terrain such as grass, gravel, asphalt and sand. The resultant network shows
excellent performance not only for GTOS-mobile, but also for more general
databases (MINC and DTD). Leveraging the discriminant features learned from
this network, we build a new texture manifold called DEP-manifold. We learn a
parametric distribution in feature space in a fully supervised manner, which
gives the distance relationship among classes and provides a means to
implicitly represent ambiguous class boundaries. The source code and database
are publicly available.

用 Deep Encoding Pooling Network (DEP) 进行地面地形识别，将无序纹理细节和局部空间信息相结合，超越了现有的先进方法，结果表明该网络在 GTOS-mobile 以及更一般的数据库上都表现出色。