Alike Scene Retrieval from Land-Cover Products Based on the Label Co-Occurrence Matrix (LCM) †

Jun Liu,Qianqing Qin,Guopeng Yang,Bin Luo

doi:10.3390/rs9090912

Abstract

The management and application of remotely sensed data has become much more difficult due to the dramatically growing volume of remotely sensed imagery. To address this issue, content-based image retrieval (CBIR) has been applied to remote sensing image retrieval for information mining. As a consequence of the growing volume of remotely sensed imagery, the number of different types of image-derived products (such as land use/land cover (LULC) databases) is also increasing rapidly. Nevertheless, only a few studies have addressed the exploration and information mining of these products. In this letter, for the sake of making the most use of the LULC map, we propose an approach for the retrieval of alike scenes from it. Based on the proposed approach, we design a content-based map retrieval (CBMR) system for LULC. The main contributions of our work are listed below. Firstly, the proposed system can allow the user to select a region of interest as the reference scene with variable shape and size. In contrast, in the traditional CBIR/CBMR systems, the region of interest is usually of a fixed size, which is equal to the size of the analysis window for extracting features. In addition, the user can acquire various retrieval results by specifying the corresponding parameters. Finally, by combining the signatures in the base signature library, the user can acquire the retrieval result faster.

Highlights

Alongside the rapid development of remote sensing platforms and sensors, the volume of remotely sensed imagery has tremendously increased
Content-based image retrieval (CBIR) is widely used in remote sensing image retrieval, as it does not require the presence of semantic tags, which are rarely available and expensive to assign
To consider the spatial relationships of the labels in a scene, we propose a scene signature extraction method motivated by the grey level co-occurrence matrix (GLCM), which is utilized to compute the texture feature of the image

Summary

Introduction

Alongside the rapid development of remote sensing platforms and sensors, the volume of remotely sensed imagery has tremendously increased. Because of the large data volumes, the exploration and information mining from remote sensing archives is becoming increasingly difficult. CBIR is widely used in remote sensing image retrieval, as it does not require the presence of semantic tags, which are rarely available and expensive to assign. Demir and Bruzzone [8,9] introduced the hashing methods for large-scale remote sensing (RS) retrieval problems to provide highly time-efficient and accurate search capability within huge data archives. Aptoula [10,11] applied global morphological texture descriptors to the problem of content-based remote sensing image retrieval. Local description strategies and visual vocabularies were widely adopted by remote sensing content-based retrieval and scene classification [12,13,14,15,16,17,18,19,20]

Methods

Discussion

Conclusion