  Remote sensing scene classification deals with the problem of classifying
land use/cover of a region from images. To predict the development and
socioeconomic structures of cities, the status of land use in regions are
tracked by the national mapping agencies of countries. Many of these agencies
use land use types that are arranged in multiple levels. In this paper, we
examined the efficiency of a hierarchically designed CNN based framework that
is suitable for such arrangements. We use NWPU-RESISC45 dataset for our
experiments and arranged this data set in a two level nested hierarchy. We have
two cascaded deep CNN models initiated using DenseNet-121 architectures. We
provide detailed empirical analysis to compare the performances of this
hierarchical scheme and its non hierarchical counterpart, together with the
individual model performances. We also evaluated the performance of the
hierarchical structure statistically to validate the presented empirical
results. The results of our experiments show that although individual
classifiers for different sub-categories in the hierarchical scheme perform
well, the accumulation of classification errors in the cascaded structure
prevents its classification performance from exceeding that of the non
hierarchical deep model.
