CN111159310A - Extensible image generation space-time fusion algorithm with information gain strategy - Google Patents
Extensible image generation space-time fusion algorithm with information gain strategy Download PDFInfo
- Publication number
- CN111159310A CN111159310A CN201911280551.7A CN201911280551A CN111159310A CN 111159310 A CN111159310 A CN 111159310A CN 201911280551 A CN201911280551 A CN 201911280551A CN 111159310 A CN111159310 A CN 111159310A
- Authority
- CN
- China
- Prior art keywords
- image
- information
- time
- low
- resolution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Image Processing (AREA)
Abstract
The invention discloses an extensible image generation space-time fusion algorithm with an information gain strategy, which comprises the following steps: by usingCycle‑GANSimulating the time sequence of the image, obtaining a plurality of available data sets, and generating a multi-level iterative image; selecting the multi-level iteration image with the image similar to the image in prediction according to the reference information; obtaining gain information of the multi-stage iteration image, and sending the gain information to a prediction moment with spatial informationkA low resolution image of (a); and obtaining image information selected by wavelet transformation, and predicting spatial information in space-time fusion.Cycle‑GANThe idea of simulating the time sequence process and the method of counterstudy are helpful to the time sequenceThe high-resolution image makes reasonable prediction and the generated image is beneficial to generating the image containing more gain information which is not contained in the low-resolution image, thereby providing help for introducing new gain information for space-time fusion.
Description
Technical Field
The invention relates to the technical field of image generation space-time fusion algorithms, in particular to an extensible image generation space-time fusion algorithm with an information gain strategy.
Background
Due to the cost, technical limitation and different tasks, most of the current satellite sensors obtain single remote sensing images with higher resolution. Which cannot meet the requirements of practical applications. The time-space fusion can utilize the complementarity of information among different remote sensing data to the maximum extent, so that the remote sensing data with different spatial resolutions and time resolutions can finally obtain a fusion image with higher time and spatial resolutions by a fusion technical means. However, in practical applications, because of a great difference in spatial resolution between high-resolution images and low-resolution images, for example, one MODIS pixel corresponds to hundreds of pixels of the Landsat image, which means that the MODIS image contains less than 1% of spatial information of the Landsat image in the same scene, and the conventional methods such as interpolation cannot embody enough spatial information, but too little known information is not enough to represent the correspondence between the high-resolution images and the low-resolution images, which results in lower fusion accuracy.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides an extensible image generation space-time fusion algorithm with an information gain strategy, which can overcome the defects in the prior art.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows:
an extensible image generation spatio-temporal fusion algorithm with an information gain strategy, the method comprising the steps of:
s1: acquiring a plurality of available data sets by adopting a Cycle-GAN analog image time sequence to generate a multi-level iterative image;
s2: selecting the multi-level iteration image with the image similar to the image in prediction according to the reference information;
s3: acquiring gain information of the multistage iteration image, and sending the gain information to a low-resolution image with spatial information at a prediction time k;
s4: and obtaining image information selected by wavelet transformation, and predicting spatial information in space-time fusion.
Further, the step S1 includes the following steps:
s11: setting iteration times, and acquiring image information of two moments k-1 and k +1 before and after a predicted moment k;
s12: setting images at the moments k-1 and k +1 as training samples of Cycle-GAN, and selecting one moment as a generated sample to generate an image;
s13: and iteratively obtaining a plurality of remote sensing images.
Further, the step S2 is a step of:
s21: calculating and predicting a low-resolution image at the moment k;
s22: calculating and predicting mutual information of low-resolution images at the time k and low-resolution images at the time k-1
S23: calculating and predicting mutual information of the low-resolution image at the time k and the low-resolution image at the time k +1
S24: calculating and predicting a high-resolution image at the time k;
s25: calculating and predicting mutual information of high-resolution image at time k and high-resolution image at time k-1
S26: calculating and predicting mutual information of the high-resolution image at the moment k and the high-resolution image at the moment k +1
S27: obtaining mutual information in rangeAndwhere λ represents the correspondence of mutual information between high and low resolutions, and may be an empirical value;
s28: acquiring the image with the maximum information entropy in the selected images, and setting the image as the selected generated image
Further, the step S3 includes the following steps:
s31: acquiring an imageInformation and low resolution image information, for imagesPerforming wavelet transform two-level decomposition on the low-resolution image;
s32: obtaining and reconstructing imagesSecond-order decomposition information and low-resolution image second-order decomposition information.
The invention has the beneficial effects that: by using the method, on one hand, a new thought is provided for space-time fusion for predicting the spatial information, and a new solution is provided for the problem that the corresponding relation is difficult to process due to overlarge information difference between high and low spatial resolutions;
on the other hand, the idea of the Cycle-GAN simulation time sequence process and the method for counterstudy are beneficial to making reasonable prediction on the time sequence high-resolution images and generating images, the generated images contain more gain information which is not contained in the low-resolution images, and help is provided for introducing new gain information for space-time fusion.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of steps of an extensible image generation spatiotemporal fusion algorithm with information gain strategy according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
As shown in FIG. 1, the scalable image generation spatiotemporal fusion algorithm with information gain strategy according to the embodiment of the present invention comprises the following steps:
step S1, acquiring a plurality of available data sets by adopting the time sequence of the Cycle-GAN simulation image, and generating a multi-stage iterative image;
step S2, selecting the multi-stage iteration image with the image similar to the image in the prediction process according to the reference information;
step S3, obtaining gain information of the multi-stage iteration image, and sending the gain information to a low-resolution image with spatial information at a prediction time k;
and step S4, acquiring image information selected by wavelet transformation, and predicting spatial information in space-time fusion.
Step S1 includes the following steps:
step S11, setting iteration times, and acquiring image information of two moments k-1 and k +1 before and after a predicted moment k;
step S12, setting the images at the time k-1 and k +1 as Cycle-GAN training samples, and selecting one of the time as a generated sample generation image;
and step S13, iteratively acquiring a plurality of remote sensing images.
Step S2 includes the following steps:
step S21, calculating and predicting a low-resolution image at time k;
step S22, calculating and predicting mutual information between the low resolution image at time k and the low resolution image at time k-1
Step S23, mutual information between the low resolution image at time k and the low resolution image at time k +1 is calculated and predicted
Step S24, calculating and predicting a high-resolution image at time k;
step S25, calculating and predicting mutual information between the high-resolution image at time k and the high-resolution image at time k-1
Step S26, calculating and predicting mutual information between the high-resolution image at time k and the high-resolution image at time k +1
Step S27, obtaining mutual information in rangeAndwhere λ represents the correspondence of mutual information between high and low resolutions, and may be an empirical value;
acquiring the image with the maximum information entropy in the selected images, and setting the image as the selected generated image
Step S3 includes the following steps:
step S31, acquiring imageInformation and low resolution image information, for imagesPerforming wavelet transform two-level decomposition on the low-resolution image;
step S32, acquiring and recombining the imageSecond-order decomposition information and low-resolution image second-order decomposition information.
In order to facilitate understanding of the above-described technical aspects of the present invention, the above-described technical aspects of the present invention will be described in detail below in terms of specific usage.
The overall architecture process of the multi-source remote sensing image data multi-dimensional organization management system based on cloud storage comprises the following modules and processes:
1) the multi-center remote sensing data retrieval module comprises a plurality of parts such as a distributed data center ftp service, a sub-center crawler, metadata mapping and a solr created index. Metadata is integrated into a main center through a sub-center crawler, and a metadata index is created through solr to provide a query retrieval service for multi-center remote sensing metadata for a user.
2) The data is subscribed by a multi-dimensional subscription module, a user subscribes data required by the user through a multi-center remote sensing data retrieval module, the file is transmitted to the shared cloud storage through ftp service provided by the sub-center, the reliability of data storage is guaranteed, and then the view of the user is defined by selecting a view generation rule. Therefore, multi-dimensional organization of the multi-center remote sensing data is realized.
3) The data view sharing module is used for enabling a user to generate a view and simultaneously store the view structure of the user in the user virtual directory database, and then calling the user virtual directory database to share the virtual directory structure of the user to other users, so that movement of real data is avoided, and multi-dimensional organization and management of other multiple users on remote sensing data are facilitated.
When a specific user organizes and manages data in cloud storage, on the basis of OpenStack Swift object storage, the user constructs a sharing code for view data, and shares a virtual directory structure to other users. The remote sensing data resource can be accessed by a user through the HTTP protocol through the Restful API of Swift.
The cloud storage multidimensional data organization module is mainly divided into a front-end visualization layer and provides data view generation service, view sharing service and view data downloading service for users. The intermediate service logic module is mainly responsible for processing basic functions of data import and export cloud storage and generating functions of a data view logic structure, and the bottom layer is mainly provided with persistent storage support by OpenStack object storage and Mysql deployed by a cluster.
When the method is used specifically, a user searches the archived data of each sub-center, selects data subscription, simultaneously leads the subscribed data from the sub-centers into the main center for cloud storage, can select view construction rules to generate a custom view, persists the view to the Mysql database, and can share the data view and download links with other users through the view sharing function and the data downloading function of the cloud storage to finish data preparation.
According to the introduction, the multi-source remote sensing image data multi-dimensional organization method based on cloud storage is designed and completed, and the method can be suitable for remote sensing application needing multi-dimensional representation and analysis of large-scale remote sensing data. The method utilizes a unified management flow, user management based on a cloud platform, automatic multi-dimensional data view construction and virtual data view sharing and downloading based on cloud storage, greatly improves the data storage efficiency and simplifies the data preparation flow required by subsequent data analysis.
In summary, by means of the above technical solution of the present invention, through the use of the method, on one hand, a new idea is provided for the prediction of spatial information by space-time fusion, and a new solution is provided for the problem of processing difficulty of correspondence caused by too large information difference between high and low spatial resolutions; on the other hand, the idea of the Cycle-GAN simulation time sequence process and the method for counterstudy are beneficial to making reasonable prediction on the time sequence high-resolution images and generating images, the generated images contain more gain information which is not contained in the low-resolution images, and help is provided for introducing new gain information for space-time fusion.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. An extensible image generation spatio-temporal fusion algorithm with an information gain strategy, characterized by comprising the following steps:
s1: acquiring a plurality of available data sets by adopting a Cycle-GAN analog image time sequence to generate a multi-level iterative image;
s2: selecting the multi-level iteration image with the image similar to the image in prediction according to the reference information;
s3: acquiring gain information of the multistage iteration image, and sending the gain information to a low-resolution image with spatial information at a prediction time k;
s4: and obtaining image information selected by wavelet transformation, and predicting spatial information in space-time fusion.
2. The scalable, information-gain-strategic image-generation spatio-temporal fusion algorithm of claim 1, wherein said step S1 comprises the steps of:
s11: setting iteration times, and acquiring image information of two moments k-1 and k +1 before and after a predicted moment k;
s12: setting images at the moments k-1 and k +1 as training samples of Cycle-GAN, and selecting one moment as a generated sample to generate an image;
s13: and iteratively obtaining a plurality of remote sensing images.
3. The scalable, information-gain-strategic image-generation spatio-temporal fusion algorithm of claim 1, wherein said step S2 comprises the steps of:
s21: calculating and predicting a low-resolution image at the moment k;
s22: calculating and predicting mutual information of low-resolution images at the time k and low-resolution images at the time k-1
S23: calculating and predicting mutual information of the low-resolution image at the time k and the low-resolution image at the time k +1
S24: calculating and predicting a high-resolution image at the time k;
s25: calculating and predicting mutual information of high-resolution image at time k and high-resolution image at time k-1
S26: calculating and predicting mutual information of the high-resolution image at the moment k and the high-resolution image at the moment k +1
S27: obtaining mutual interactionInformation is in rangeAndwhere λ represents the correspondence of mutual information between high and low resolutions, and may be an empirical value;
s28: acquiring the image with the maximum information entropy in the selected images, and setting the image as a selected generated image Fk GAN。
4. The scalable, information-gain-strategic image-generation spatio-temporal fusion algorithm of claim 1, wherein said step S3 comprises the steps of:
s31: acquiring an image Fk GANInformation and low resolution image information, for image Fk GANPerforming wavelet transform two-level decomposition on the low-resolution image;
s32: obtaining and reconstructing an image Fk GANSecond-order decomposition information and low-resolution image second-order decomposition information.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911280551.7A CN111159310B (en) | 2019-12-13 | 2019-12-13 | Extensible image generation space-time fusion method with information gain strategy |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911280551.7A CN111159310B (en) | 2019-12-13 | 2019-12-13 | Extensible image generation space-time fusion method with information gain strategy |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111159310A true CN111159310A (en) | 2020-05-15 |
CN111159310B CN111159310B (en) | 2023-09-29 |
Family
ID=70557063
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201911280551.7A Active CN111159310B (en) | 2019-12-13 | 2019-12-13 | Extensible image generation space-time fusion method with information gain strategy |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111159310B (en) |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090226114A1 (en) * | 2008-03-07 | 2009-09-10 | Korea Aerospace Research Institute | Satellite image fusion method and system |
CN102915529A (en) * | 2012-10-15 | 2013-02-06 | 黄波 | Integrated fusion technique and system based on remote sensing of time, space, spectrum and angle |
CN105184076A (en) * | 2015-09-02 | 2015-12-23 | 安徽大学 | Space-time integrated fusion method for remote sensing earth surface temperature data |
WO2017219263A1 (en) * | 2016-06-22 | 2017-12-28 | 中国科学院自动化研究所 | Image super-resolution enhancement method based on bidirectional recursion convolution neural network |
US20180137603A1 (en) * | 2016-11-07 | 2018-05-17 | Umbo Cv Inc. | Method and system for providing high resolution image through super-resolution reconstruction |
US20190004533A1 (en) * | 2017-07-03 | 2019-01-03 | Baidu Usa Llc | High resolution 3d point clouds generation from downsampled low resolution lidar 3d point clouds and camera images |
EP3564903A1 (en) * | 2018-05-01 | 2019-11-06 | Koninklijke Philips N.V. | Lower to higher resolution image fusion |
CN110532897A (en) * | 2019-08-07 | 2019-12-03 | 北京科技大学 | The method and apparatus of components image recognition |
-
2019
- 2019-12-13 CN CN201911280551.7A patent/CN111159310B/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090226114A1 (en) * | 2008-03-07 | 2009-09-10 | Korea Aerospace Research Institute | Satellite image fusion method and system |
CN102915529A (en) * | 2012-10-15 | 2013-02-06 | 黄波 | Integrated fusion technique and system based on remote sensing of time, space, spectrum and angle |
CN105184076A (en) * | 2015-09-02 | 2015-12-23 | 安徽大学 | Space-time integrated fusion method for remote sensing earth surface temperature data |
WO2017219263A1 (en) * | 2016-06-22 | 2017-12-28 | 中国科学院自动化研究所 | Image super-resolution enhancement method based on bidirectional recursion convolution neural network |
US20180137603A1 (en) * | 2016-11-07 | 2018-05-17 | Umbo Cv Inc. | Method and system for providing high resolution image through super-resolution reconstruction |
US20190004533A1 (en) * | 2017-07-03 | 2019-01-03 | Baidu Usa Llc | High resolution 3d point clouds generation from downsampled low resolution lidar 3d point clouds and camera images |
EP3564903A1 (en) * | 2018-05-01 | 2019-11-06 | Koninklijke Philips N.V. | Lower to higher resolution image fusion |
CN110532897A (en) * | 2019-08-07 | 2019-12-03 | 北京科技大学 | The method and apparatus of components image recognition |
Non-Patent Citations (2)
Title |
---|
CHEN B等: "Comparison of Spatiotemporal Fusion Models:A Review", vol. 7, no. 2, pages 1798 - 1835 * |
黄波;赵涌泉;: "多源卫星遥感影像时空融合研究的现状及展望", no. 10 * |
Also Published As
Publication number | Publication date |
---|---|
CN111159310B (en) | 2023-09-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20230252724A1 (en) | Point cloud data hierarchy | |
CN106909644B (en) | A kind of multistage tissue and indexing means towards mass remote sensing image | |
Planthaber et al. | EarthDB: scalable analysis of MODIS data using SciDB | |
US10169455B2 (en) | Systems and methods for addressing a media database using distance associative hashing | |
WO2019069304A1 (en) | System and method for compact and efficient sparse neural networks | |
CN102509022B (en) | Method for quickly constructing raster database facing to Virtual Earth | |
CN105786942A (en) | Geographic information storage system based on cloud platform | |
WO2015141463A1 (en) | Method for processing input low-resolution (lr) image to output high-resolution (hr) image | |
US20160299910A1 (en) | Method and system for querying and visualizing satellite data | |
US11860674B1 (en) | Query system | |
CN109657080B (en) | Distributed processing method/system and medium for high-resolution satellite remote sensing data | |
CN107480720B (en) | Human body posture model training method and device | |
CN110569972A (en) | search space construction method and device of hyper network and electronic equipment | |
Guo et al. | A spatially adaptive decomposition approach for parallel vector data visualization of polylines and polygons | |
Pokorný et al. | Big data movement: a challenge in data processing | |
US20220156291A1 (en) | Geospatial data analytics and visualization platform | |
US12019710B2 (en) | Managing and streaming a plurality of large-scale datasets | |
CN111159310A (en) | Extensible image generation space-time fusion algorithm with information gain strategy | |
CN110110107A (en) | A kind of Methods on Multi-Sensors RS Image various dimensions method for organizing based on cloud storage | |
Mitra et al. | Glance: A generative approach to interactive visualization of voluminous satellite imagery | |
CN115662346B (en) | Demura compensation value compression method and system | |
US20200195964A1 (en) | Electronic circuit and electronic device performing motion estimation through hierarchical search | |
Albrecht et al. | Pairs (Re) loaded: system design & benchmarking for scalable geospatial applications | |
Happ et al. | Towards distributed region growing image segmentation based on MapReduce | |
Alkathiri et al. | Kluster: Application of k-means clustering to multidimensional GEO-spatial data |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |