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 highresolution dataset with multiple class labels. The contributions of this paper are twofold -(1) first, we present two new satellite datasets called SAT-4 and SAT-6, and (2) then, we propose a classification framework that extracts features from an input image, normalizes them and feeds the normalized feature vectors to a Deep Belief Network for classification. On the SAT-4 dataset, our best network produces a classification accuracy of 97.95% and outperforms three state-of-the-art object recognition algorithms, namely -Deep Belief Networks, Convolutional Neural Networks and Stacked Denoising Autoencoders by ∼11%. On SAT-6, it produces a classification accuracy of 93.9% and outperforms the other algorithms by ∼15%. Comparative studies with a Random Forest classifier show the advantage of an unsupervised learning approach over traditional supervised learning techniques. A statistical analysis based on Distribution Separability Criterion and Intrinsic Dimensionality Estimation substantiates the effectiveness of our approach in learning better representations for satellite imagery.
In REF ), we presented a classification framework that feeds handcrafted features extracted from an image to a DBN for classifying high resolution satellite imagery.
DeepSat: a learning framework for satellite imagery
GIS '15
Computer Science