An Automatic Accurate High-Resolution Satellite Image Retrieval Method

Zhiwei Fan,Wen Zhang,Lingkui Meng,Dongying Zhang

doi:10.3390/rs9111092

Zhiwei Fan, Wen Zhang + Show 2 more

Open Access

https://doi.org/10.3390/rs9111092

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Journal: Remote Sensing	Publication Date: Oct 26, 2017
Citations: 5	License type: CC BY 4.0

Affiliation: Wuhan University, Zhengzhou University

Abstract

With the growing number of high-resolution satellite images, the traditional image retrieval method has become a bottleneck in the massive application of high-resolution satellite images because of the low degree of automation. However, there are few studies on the automation of satellite image retrieval. This paper presents an automatic high-resolution satellite image accurate retrieval method based on effective coverage (EC) information, which is used to replace the artificial screening stage in traditional satellite image retrieval tasks. In this method, first, we use a convolutional neural network to extract the EC of each satellite image; then, we use an effective coverage grid set (ECGS) to represent the ECs of all satellite images in the library; finally, the satellite image accurate retrieval algorithm is proposed to complete the process of screening images. The performance evaluation of the method is implemented in three regions: Wuhan, Yanling, and Tangjiashan Lake. The large number of experiments shows that our proposed method can automatically retrieve high-resolution satellite images and significantly improve efficiency.

Highlights

The information in satellite images plays an important role in environmental monitoring, disaster forecasting, geological surveying, and other applications
In the summary of the artificial screening process, we have found several rules that guide the screening process: (1) Images that cover the edge of the Region of Interest (ROI) are preferred to avoid small pieces scattered in an uncovered area; (2) Images with a large effective coverage area for the ROI are preferred to control the number of screened images; and (3) Images that properly overlap with other screened images are preferred to facilitate the image mosaic work
Rating Images in the Pre-selected Image Set” (PIS) Because each image in the PIS has an intersection with the ROI, but only one image can be selected for each round of screening, we develop a rating method to evaluate the opportunity to be selected of each image in PIS, so that the images with larger effective coverage areas for the ROI are preferred, which is consistent with manual-screening rule 2

Summary

Introduction

The information in satellite images plays an important role in environmental monitoring, disaster forecasting, geological surveying, and other applications. With the steadily expanding demand for remotely sensed images, many satellites have been launched, and thousands of images are acquired every day. Many researchers and organizations use satellite images to study the surface evolution process of a Region of Interest (ROI), and the entrance of the remote sensing application is satellite image retrieval. Conventional satellite image retrieval in remote sensing applications is performed using a compound search composed of spatial search and attribute search. We need some high-resolution satellite images as first-hand information for a land use survey of Wuhan in 2016; the ROI is Wuhan in the retrieval task, and the conditions of the attribute search are imaging in 2016 and a spatial resolution above 10 m per pixel. Some well-known satellite imagery portals use this form of retrieval [1,2,3]

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