Open AccessHigh‐efficiency scene classification based on deep compressed‐domain feature
Author(s) -
Li Cheng,
Zhao Baojun,
Zhao Boya,
Wang Wenzheng,
Duan Chenhui
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0266
Subject(s) - header , computer science , feature (linguistics) , convolutional neural network , domain (mathematical analysis) , representation (politics) , artificial intelligence , geospatial analysis , pyramid (geometry) , computation , volume (thermodynamics) , pattern recognition (psychology) , remote sensing , algorithm , geology , mathematical analysis , philosophy , linguistics , mathematics , computer network , physics , optics , quantum mechanics , politics , political science , law
Remote sensing image (RSI) scene classification has become a more and more fundamental issue in satellite and UAV time‐sensitive applications. However, as the volume and velocity of RSIs are undergoing an explosive growth, traditional effective technologies claim a huge amount of computing resources, on a scale that already goes beyond in‐orbit processing capacity. Here, we attempt to design a deep feature representation framework based on onboard compressed data to solve the aforementioned problem. Firstly, we extract header and body information in raw JPEG2000 codestream for representing geometrical and geospatial property of RSIs. Moreover, a novel compressed‐domain convolutional neural network (CNN) is proposed to obtain high‐level representation for effective classification. Extensive experiments demonstrate that, in comparison with the existing relevant state‐of‐the‐art approaches, the proposed method achieves high classification accuracy with faster computation and lower consumption.