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28 pages, 9174 KiB

Open AccessArticle

RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System

by Xiaohua Zhou, Xuezhi Wang, Yuanchun Zhou, Qinghui Lin, Jianghua Zhao and Xianghai Meng

Remote Sens. 2021, 13(9), 1815; https://doi.org/10.3390/rs13091815 - 6 May 2021

Cited by 19 | Viewed by 3996

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use. Full article

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18 pages, 13912 KiB

Open AccessArticle

A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features

by Tianyou Chu, Yumin Chen, Liheng Huang, Zhiqiang Xu and Huangyuan Tan

Remote Sens. 2020, 12(23), 3978; https://doi.org/10.3390/rs12233978 - 4 Dec 2020

Cited by 11 | Viewed by 3717

Abstract

Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods. Full article

(This article belongs to the Special Issue Feature Paper Special Issue: Large Spatial Scale Analysis and Intensive Data Use in Urban Social, Ecological, Economic, or Environmental System)

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28 pages, 3620 KiB

Open AccessArticle

Efficient Geometric Pruning Strategies for Continuous Skyline Queries

by Jiping Zheng, Jialiang Chen and Haixiang Wang

ISPRS Int. J. Geo-Inf. 2017, 6(3), 91; https://doi.org/10.3390/ijgi6030091 - 22 Mar 2017

Cited by 7 | Viewed by 4284

Abstract

The skyline query processing problem has been well studied for many years. The literature on skyline algorithms so far mainly considers static query points on static attributes. With the popular usage of mobile devices along with the increasing number of mobile applications and users, continuous skyline query processing on both static and dynamic attributes has become more pressing. Existing efforts on supporting moving query points assume that the query point moves with only one direction and constant speed. In this paper, we propose continuous skyline computation over an incremental motion model. The query point moves incrementally in discrete time steps with no restrictions and predictability. Geometric properties over incremental motion denoted by a kinetic data structure are utilized to prune the portion of data points not included in final skyline query results. Various geometric strategies are asymptotically proposed to prune the querying dataset, and event-driven mechanisms are adopted to process continuous skyline queries. Extensive experiments under different data sets and parameters demonstrate that the proposed method is robust and more efficient than multiple snapshots of I/O optimal branch-and-bound skyline (BBS) skyline queries. Full article

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