Abstract
With the rapid development of satellite remote sensing technology, the size of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection, and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval from a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature, integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing and also deal with problems related to seasonal changes, as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval. The experiment results obtained using a Landsat data set show that the use of the new model can produce promising results. A coverage rate and mean average precision of 71% and 89%, respectively, were achieved for the top 20 returned pairs of images.
Highlights
Land-Use (LU) and Land-Cover (LC) change can be associated with varying rates of change of one of the Earth’s surface components
A total of 14 scenes acquired by Landsat 5 TM (11 images) and Landsat 8 (3 images) between 1996 and 2015 were used. 14 images with Worldwide Reference System (WRS) Path 123 and Row 032 and cloud cover of less than 80% were downloaded from the U.S Geological Survey
The remote sensing image preprocessing and data archiving consisted of four parts: image preprocessing, image decomposition based on Quin+-tree [25], feature extraction from pairs of remote sensing images, and content-based remote sensing image change information retrieval
Summary
Land-Use (LU) and Land-Cover (LC) ( referred to as LULC) change can be associated with varying rates of change of one of the Earth’s surface components. In contrast to the keyword-to-find-image approach, content-based image retrieval (CBIR) is a major advance that aims to search images using visual features that are similar to those of the query image submitted by the user This technique uses a description of the image consisting of automatically extracted visual features such as color, texture, and shape. To overcome the problems described above, content-based remote sensing image retrieval can be applied to accessing and detecting change information in remote sensing imagery. As accessing and detection of remote images’ change information can be seen the similar pairs (different rates with the same geographical location) of images retrieval, in this paper, a new content-based remote sensing image change information retrieval (CBRSICIR) model. MMaappsssshhoowwininggaaddmmininisistrtraatitviveeaarreeaassaannddtteerrrraainin..(a(a))TThheeggeeooggrraapphhicicaal lloloccaatitoionnooffBBeeijiijningg, , CChhiinnaa;;((bb))PPoolliittiiccaallmmaappooffBBeeiijijinngg;;aanndd,,((cc))TTooppooggrraapphhiicc mmaapp ooff BBeeiijjiinngg ddeerriivveedd ffrroomm LLaannddssaatt 55 ddaattaa
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