  Satellite image classification is a challenging problem that lies at the
crossroads of remote sensing, computer vision, and machine learning. Due to the
high variability inherent in satellite data, most of the current object
classification approaches are not suitable for handling satellite datasets. The
progress of satellite image analytics has also been inhibited by the lack of a
single labeled high-resolution dataset with multiple class labels. In a
preliminary version of this work, we introduced two new high resolution
satellite imagery datasets (SAT-4 and SAT-6) and proposed DeepSat framework for
classification based on "handcrafted" features and a deep belief network (DBN).
The present paper is an extended version, we present an end-to-end framework
leveraging an improved architecture that augments a convolutional neural
network (CNN) with handcrafted features (instead of using DBN-based
architecture) for classification. Our framework, having access to fused spatial
information obtained from handcrafted features as well as CNN feature maps,
have achieved accuracies of 99.90% and 99.84% respectively, on SAT-4 and SAT-6,
surpassing all the other state-of-the-art results. A statistical analysis based
on Distribution Separability Criterion substantiates the robustness of our
approach in learning better representations for satellite imagery.
