Towards Simultaneous Image Compression and Indexing for Scalable Content-Based Retrieval in Remote Sensing

Abstract

Due to the rapidly growing remote-sensing (RS) 1 image archives, images are usually stored in a compressed format 2 for reducing their storage sizes. Thus, most of the existing 3 content-based RS image retrieval systems require fully decoding 4 images (i.e., decompression) that is computationally demanding 5 for large-scale archives. To address this issue, we introduce a 6 novel approach devoted to simultaneous RS image compres-7 sion and indexing for scalable content-based image retrieval 8 (denoted as SCI-CBIR). The proposed SCI-CBIR prevents the 9 requirement of decoding RS images before image search and 10 retrieval. To this end, it includes two main steps: 1) deep-11 learning-based compression and 2) deep-hashing-based indexing. 12 The first step effectively compresses RS images by employing 13 a pair of deep encoder and decoder neural networks and an 14 entropy model. The second step produces hash codes with a high 15 discrimination capability for RS images by employing pairwise, 16 bit-balancing, and classification loss functions. For the training 17 of the SCI-CBIR approach, we also introduce a novel multistage 18 learning procedure with automatic loss weighting techniques 19 to characterize RS image representations that are appropriate 20 for both RS image indexing and compression. The proposed 21 learning procedure enables automatically weighting different 22 loss functions considered for the proposed approach instead of 23 computationally demanding grid search. Experimental results 24 show the effectiveness of the proposed approach when compared 25 to widely used approaches in RS. The code of the proposed 26 approach is available at https://git.tu-berlin.de/rsim/SCI-CBIR. 27 Index Terms-Deep-learning-based compression, hashing-28 based indexing, image retrieval, remote sensing (RS). 29 I. INTRODUCTION 30 R ECENT advances in satellite technologies lead to a 31 significantly increased volume of remote-sensing (RS) 32 image archives. Thus, in recent years, increasing attention 33 has been devoted to the development of accurate and scal-34 able content-based image retrieval (CBIR) methods for such 35 archives [1], [2], [3]. For large-scale CBIR, fast and accurate 36 indexing methods that allow approximate nearest neigh-37 bor search are fundamental. In this perspective, hashing-38 based indexing has recently attracted attention to solving 39

Query by Example in Remote Sensing Image Archive Using Enhanced Deep Support Vector Data Description

“…For instance, Shao et al [17] proposed multilabel RS image retrieval based on a fully convolutional network to address the poor retrieval performance for complex RS images. 2) Hashing-based RS image retrieval [18], [19]: This category is more common for large-scale RS image retrieval. To achieve this, feature reduction techniques have been adopted.…”