| Summary: | Recent advances in satellite imaging technologies have paved its way to the RS big data era. Efficient storage, management and utilization of massive amounts of data is one of the major challenges faced by the remote sensing (RS) community. To minimize the storage requirements and speed up the transmission rate, RS images are compressed before archiving. Accordingly, developing efficient Content Based Image Retrieval (CBIR) and scene classification techniques to effectively utilize these huge volume of data is one among the most researched areas in RS. With the continual growth in the volume of compressed RS data, the dominant aspect that plays a key role in the development of
these techniques is the decompression time required by these images. Existing CBIR and scene classification methods in RS require fully decompressed RS images as input, which is a computationally complex and time consuming task to perform. Among several compression algorithms introduced to RS, JPEG 2000 is the most widely used in operational satellites due to its multiresolution paradigm, scalability and high compression ratio. In light of this, the goal of this thesis is to develop novel methods to achieve image retrieval and scene classification for JPEG 2000 compressed RS image archives. The first contribution of the thesis addresses the possibility of performing CBIR directly on compressed RS images. The aim of the proposed method is to achieve efficient image characterization and retrieval within the JPEG 2000 compressed domain. The proposed progressive image retrieval approach achieves a coarse to fine image description
and retrieval in the partially decoded JPEG 2000 compressed domain. Its aims to reduce the computational time required by the CBIR system for compressed RS image archives. The second contribution of the thesis concerns the possibility of achieving scene classification for JPEG 2000 compressed RS image archives. Recently, deep learning methods have demonstrated a cutting edge improvement in scene classification performance in large-scale RS image archives. In view of this, the proposed method is based on deep learning and aims to achieve maximum scene classification accuracy with minimal decoding. The proposed approximation approach learns the high-level hierarchical image description in a partially decoded domain thereby avoiding the requirement to fully decode the images from the archive before any scene classification is performed. Quantitative as well as qualitative experimental results demonstrate the efficiency of the proposed methods, which show significant improvements over state-of-the-art methods.
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