A Distributed System Architecture for High-resolution Remote Sensing Image Retrieval by Combining Deep and Traditional Features

The recent advance of satellite technology has led to explosive growth of high-resolution remote sensing images in both quantity and quality. To address the challenges of high-resolution remote sensing images retrieval in both efficiency and accuracy, a distributed system architecture for satellite images retrieval by combining deep and traditional hand-crafted features is proposed in this paper. On one hand, to solve the problem of higher computational complexity and storage capacity, Hadoop framework is applied to manage satellite image data and to extract image features in parallel environment. On the other hand, deep features based on convolutional neural networks (CNNs) are extracted and combined with traditional features to overcome the limitations of hand-crafted features. Besides, object detection are integrated in the proposed system to realize accurate object locating at the time of retrieval. Experiments are carried on several challenging datasets to evaluate the performance of the proposed distributed system. Standard metrics like retrieval precision, recall and computing time under different configurations are compared and analyzed. Experimental results demonstrate that our system architecture is practical and feasible, both efficiency and accuracy can meet realistic demands.