CN111523451A - Method for constructing high-space-time resolution NDVI data - Google Patents
Method for constructing high-space-time resolution NDVI data Download PDFInfo
- Publication number
- CN111523451A CN111523451A CN202010321530.1A CN202010321530A CN111523451A CN 111523451 A CN111523451 A CN 111523451A CN 202010321530 A CN202010321530 A CN 202010321530A CN 111523451 A CN111523451 A CN 111523451A
- Authority
- CN
- China
- Prior art keywords
- resolution
- ndvi
- data
- time
- pixel
- 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
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 24
- 238000006243 chemical reaction Methods 0.000 claims description 10
- 230000002123 temporal effect Effects 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 5
- 238000010586 diagram Methods 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 abstract description 2
- 230000004927 fusion Effects 0.000 description 14
- 238000001228 spectrum Methods 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 238000012952 Resampling Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4053—Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Geometry (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The invention claims a method for constructing high-space-time resolution NDVI data. The method combines a method of mixed pixel decomposition and a method based on a weight function, and comprises the following steps: the method comprises the steps of preprocessing high-resolution NDVI data and low-resolution NDVI data at a reference moment and low-resolution NDVI data at a prediction moment, obtaining a ground surface classification map of the high-resolution NDVI data at the reference moment, then constructing a linear equation set based on a mixed pixel decomposition method, taking the least square solution of the linear equation set as an NDVI variation value of ground object categories of the high-resolution data at the prediction moment, adding the variation value to the high-resolution NDVI data at the reference moment, finally obtaining the high-space-time resolution NDVI data at the prediction moment according to the weight value of similar pixels selected in a moving window and the calculation of a correlation coefficient, and completing the reconstruction of a high-space-time resolution NDVI data set. The invention provides an effective method for constructing the high-space-time resolution NDVI data set.
Description
Technical Field
The invention belongs to the field of remote sensing data space-time fusion, particularly belongs to a method for constructing high-space-time resolution NDVI data, and relates to a remote sensing image fusion and classification technology.
Background
At present, the Normalized Differentiated Vegetation Index (NDVI) proposed by Rouse is the most widely used vegetation Index by virtue of its simple inversion algorithm and clear physical meaning.
The normalized vegetation index is widely applied to the research of land cover types, vegetation dynamic change and phenology due to the advantages of wide space coverage and high vegetation monitoring sensitivity. The revisit period of the MODIS sensor satellite data is 1-2 days, the high time resolution can be used for identifying and monitoring the dynamic change of the earth surface vegetation, but the spatial resolution reaches 250-1000 m, and high-precision earth surface information is difficult to depict. The spatial resolution of the Landsat satellite data is 30m, different ground features can be recognized with high precision, the 16-day revisit cycle limits the extraction of information of Landsat data on ground surface coverage change, particularly, in the critical period of crop growth, the 16-day interval can generate great information change, and in addition, data loss caused by factors such as cloud coverage areas is avoided, so that high-quality Landsat remote sensing image data is difficult to obtain. By fusing Landsat and MODIS remote sensing satellite data, a high-resolution data set can be synthesized by combining the advantages of the Landsat and the MODIS remote sensing satellite data, so that the monitoring on the earth surface coverage change and the vegetation growth can be better realized. In order to meet the requirement that the remote sensing data of the remote sensing dynamic monitoring of the earth surface information has high spatial resolution and high temporal resolution at the same time, the remote sensing data space-time fusion technology is a better method for solving the problems.
In recent years, scholars at home and abroad carry out a great deal of research on remote sensing space-time fusion technology. The comparison is typically a spatio-temporal fusion model based on a weighting function and a spatio-temporal fusion model based on a mixed pixel decomposition.
The method is characterized in that the influence of high time resolution is directly calculated from low space resolution in a space-time fusion model based on a weight function, the method comprehensively considers space distance, spectral distance and time distance, and a central pixel is calculated by introducing a moving window and utilizing adjacent spectral similar pixels; on the basis of classifying or dividing the high-resolution images of the known time phase in the space-time fusion model based on the mixed pixel decomposition, a spectrum mixing model between the high-resolution images of the known time phase and the corresponding low-resolution images is established by using a spectrum mixing theory, the spectrum mixing model is further applied to the low-resolution images of the unknown time phase to be predicted, and then the time phase change information of different ground object components on the low-resolution images and the corresponding spectrum mixing model are used for solving the time phase change quantity of the high resolution, so that the high-resolution images of the unknown time phase are predicted.
The method is characterized in that the spectrum unmixing of original data by using a mixed pixel method is not considered in a space-time fusion model based on a weight function, but the data after resampling is directly adopted, so that the prediction result in a heterogeneous region is not ideal, and the influence of surrounding pixels on a central pixel is not considered in the traditional mixed pixel decomposition method.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A method for constructing high spatial and temporal resolution NDVI data is provided. The technical scheme of the invention is as follows:
a method of constructing high spatial and temporal resolution NDVI data, comprising the steps of:
step S1, selecting a high-spatial-resolution image (the spatial resolution of Landsat data is 30 meters) and a high-temporal-resolution image (the temporal resolution of MODIS data is 1 day) at a reference moment and a low-spatial-resolution image at a prediction moment, and respectively calculating Normalized Difference Vegetation Index (NDVI) data corresponding to all the input data;
step S2, classifying the high-resolution images at the reference time by using an unsupervised classification method and obtaining a class abundance map;
step S3, based on a mixed pixel decomposition method, performing pixel decomposition by using the class abundance map obtained in the step S2 and the low-resolution NDVI data of the reference time, and constructing a linear equation set solution and a least square solution to obtain an NDVI change value from the reference time to a prediction time;
step S4, searching the high-resolution NDVI data similar pixels at the reference moment by using a moving window technology, and calculating the conversion coefficient Vi and the weight function Wi of the similar pixels;
and step S5, calculating the central pixel value of the prediction time by using the NDVI change value obtained in the step S3 and combining the conversion coefficient Vi and the weight function Wi obtained at the time of the step S4, and obtaining the high-spatial-resolution NDVI data of the prediction time.
Further, the step S1 of calculating the normalized difference vegetation index NDVI data specifically includes:
NDVI＝(NIR-R)/(NIR+R) (1)
in the formula: NIR is the surface reflectance value in the near infrared band and R is the surface reflectance value in the infrared band. Wherein the NDVI value ranges between-1 and 1.
Further, in step S2, classifying the high-resolution image at the reference time by using an unsupervised classification method and obtaining a class abundance map specifically includes:
the method comprises the steps of classifying high-resolution NDVI remote sensing images at a reference moment by using a traditional unsupervised classification method ISO-DATA, extracting abundance maps of various categories in a grid with the same size as pixels of the low-resolution NDVI remote sensing images on classification maps by using a remote sensing DATA processing software ArcGIS platform, and specifically realizing the following formula:
fl(xi,yi)＝Nl(xi,yi)/m (2)
wherein f isl(xi,yi) Representing low resolution picture elements (x)i,yi) Area ratio of middle class L, NL(xi,yi) High resolution pels in low resolution pels (x) representing class Li,yi) M represents the number of high-resolution pixels in one low-resolution pixel.
Further, the step S3 is based on a mixed pixel decomposition method, and performs pixel decomposition by using the class abundance map obtained in step S2 and the low-resolution NDVI data at the reference time, and constructs a linear equation set solution to calculate a least square solution to obtain an NDVI variation value from the reference time to the predicted time, which specifically includes:
based on a mixed pixel decomposition method, pixel decomposition is carried out by using the obtained class abundance diagram and low-resolution NDVI data at a reference moment to construct a linear equation set, and the formula is as follows:
Δ C in formula (2)NDVI(xi,yi) Low resolution picture element (x) representing the reference time to the predicted timei,yi) NDVI Change amount of (a), (b), (c)l(xi,yi) Indicating the L-th ground type in a low resolution pixel (x)i,yi) The abundance value of (1) is obtained by solving the least square solution to obtain delta FNDVI(l) And representing the amount of NDVI change of the lth terrain type in the high resolution pel from the reference time to the predicted time.
Further, in the step S4, searching for the high resolution NDVI data similar pixel at the reference time by using a moving window technique, and calculating a conversion coefficient Vi and a weight function Wi of the similar pixel;
in the formula, Hi t2、Li t2And Hi t1、Li t1Respectively represent the ith high-resolution and low-resolution pixel values corresponding to the reference time t1 and t 2.
In the formula, RiAnd DiRespectively representing the spectral difference and relative distance between the similar and central pixels.
Further, in step 5, the central pixel value at the prediction time is calculated by using the NDVI variation value obtained in step S3 and combining the conversion coefficient Vi and the weight function Wi obtained at the time of step S4, so as to obtain the high spatial resolution NDVI data at the prediction time, which specifically includes:
and (4) calculating the NDVI value of the central pixel by using a formula (6) to generate a final prediction image.
In the formula, Xw/2、Yw/2Representing the position of the center pixel; t is tp、t0Respectively representing a predicted time and a reference time; n represents the number of similar pixels in the moving window; i represents the ith similar pixel in the moving window; Δ FNDVIRepresenting the amount of NDVI change of the picture element from the reference time to the prediction time.
The invention has the following advantages and beneficial effects:
the main innovation point of the method is that by combining a method based on mixed pixel decomposition and a method based on a weight function, the pixel change value of each specific ground object type is obtained by using the mixed pixel method to serve as a fusion basis, and the traditional spatio-temporal model based on the weight function directly uses the resampled data as the fusion basis. The method can solve the problem of mixed pixels in the original space-time model based on the weight function to a certain extent, thereby effectively improving the prediction performance of the space-time fusion model in heterogeneous regions and providing an effective method for constructing the high space-time resolution NDVI data set.
Drawings
FIG. 1 is a flow chart of a method of constructing high spatial-temporal resolution NDVI data according to a preferred embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the invention relates to a method for constructing high-space-time resolution NDVI data, the specific implementation flow of the method is shown in figure 1, and the method mainly comprises the following steps:
the invention provides a method for constructing high-space-time resolution NDVI data, which specifically comprises the following steps:
step S1, selecting the high-low resolution image at the reference time and the low-time resolution image at the predicted time, and calculating and acquiring corresponding NDVI data respectively.
And step S2, classifying the high-resolution images at the reference time by using unsupervised classification and obtaining a class abundance map of the high-resolution images.
And S3, based on a mixed pixel decomposition method, performing pixel decomposition by using the class abundance map obtained in the step S2 and the low-resolution NDVI data of the reference time, and constructing a linear equation set solution and a least square solution to obtain the NDVI change value from the reference time to the prediction time.
Step S4, searching the high-resolution NDVI data similar pixels at the reference time by using the moving window technology, and calculating the conversion coefficient Vi and the weight function Wi of the similar pixels
And step S5, calculating the central pixel value of the prediction time by using the NDVI change obtained in the step S3 and combining the conversion coefficient and the weight function obtained at the time of the step S4, and obtaining the high-spatial-resolution NDVI data of the prediction time.
The step S1 acquires the reference time image and the predicted time image, and calculates the NDVI data thereof by preprocessing.
Preferably, in step S2, the traditional unsupervised classification method ISO-DATA is used to classify the high-resolution NDVI remote sensing image at the reference time, and the ArcGIS is used to extract an abundance map of each category in the grid with the same pixel size as that of the low-resolution NDVI remote sensing image on the classification map, and the specific implementation formula is as follows:
fl(xi,yi)＝Nl(xi,yi)/m (1)
wherein f isl(xi, yi) represents the area ratio of class L in the low resolution pixel (xi, yi), Nl(xi, yi) high resolution pel in class LThe number of pixels in the low-resolution pixels (xi, yi) is m, and m represents the number of high-resolution pixels in one low-resolution pixel.
Preferably, in step S3, based on the mixed pixel decomposition method, pixel decomposition is performed using the class abundance map obtained in step S2 and the low-resolution NDVI data at the reference time to construct a linear equation set, where the formula is as follows:
the formula (1) represents the NDVI variation of the low-resolution pixels (xi, yi) from the reference time to the prediction time, represents the abundance of the L-th ground object type in the low-resolution pixels (xi, yi), and solves the least square solution to obtain delta FNDVI(l) And the NDVI variation of the L th ground object type of the high-resolution image element from the reference time to the prediction time is represented.
Preferably, in step S4, the linear regression model is used to calculate the transformation coefficient Vi of the similar pixel, and the relative distance between the similar pixel and the central pixel is used to obtain the weight function Wi.
Preferably, in the step 5, the NDVI value of the central pixel is calculated by using formula (2), so as to generate a final predicted image.
The method combines a mixed pixel decomposition method and a weight function method, not only considers the conversion coefficient and the weight function between similar pixels by using a weight function-based space-time fusion model, but also obtains the NDVI variation value of a specific earth surface type by performing spectral unmixing on a low-resolution pixel by using the mixed pixel decomposition method as a further fusion basis, rather than adopting direct resampling data, and provides an effective method for constructing a high-space-time resolution NDVI data set.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (6)
1. A method of constructing high spatial and temporal resolution NDVI data, comprising the steps of:
step S1, selecting a high-spatial-resolution image (the spatial resolution of Landsat data is 30 meters) and a high-temporal-resolution image (the temporal resolution of MODIS data is 1 day) at a reference moment and a low-spatial-resolution image at a prediction moment, and respectively calculating Normalized Difference Vegetation Index (NDVI) data corresponding to all the input data;
step S2, classifying the high-resolution images at the reference time by using an unsupervised classification method and obtaining a class abundance map;
step S3, based on a mixed pixel decomposition method, performing pixel decomposition by using the class abundance map obtained in the step S2 and the low-resolution NDVI data of the reference time, and constructing a linear equation set solution and a least square solution to obtain an NDVI change value from the reference time to a prediction time;
step S4, searching the high-resolution NDVI data similar pixels at the reference moment by using a moving window technology, and calculating the conversion coefficient Vi and the weight function Wi of the similar pixels;
and step S5, calculating the central pixel value of the prediction time by using the NDVI change value obtained in the step S3 and combining the conversion coefficient Vi and the weight function Wi obtained at the time of the step S4, and obtaining the high-spatial-resolution NDVI data of the prediction time.
2. The method according to claim 1, wherein the step S1 of calculating the normalized difference vegetation index NDVI data specifically comprises:
NDVI＝(NIR-R)/(NIR+R) (1)
in the formula: NIR is the surface reflectance value in the near infrared band and R is the surface reflectance value in the infrared band, where NDVI ranges between-1 and 1.
3. The method according to claim 1, wherein the step S2 is performed by classifying the high-resolution image at the reference time by an unsupervised classification method and obtaining a class abundance map, and specifically comprises:
the method comprises the steps of classifying high-resolution NDVI remote sensing images at a reference moment by using a traditional unsupervised classification method ISO-DATA, extracting abundance maps of various categories in a grid with the same size as pixels of the low-resolution NDVI remote sensing images on classification maps by using a remote sensing DATA processing software ArcGIS platform, and specifically realizing the following formula:
fl(xi,yi)＝Nl(xi,yi)/m (2)
wherein f isl(xi,yi) Representing low resolution picture elements (x)i,yi) Area ratio of middle class L, NL(xi,yi) High resolution pels in low resolution pels (x) representing class Li,yi) M represents the number of high-resolution pixels in one low-resolution pixel.
4. The method according to claim 3, wherein the step S3 is based on a mixed pixel decomposition method, the pixel decomposition is performed by using the class abundance map obtained in the step S2 and the low-resolution NDVI data at the reference time, and a linear equation set solution least square solution is constructed to obtain the NDVI variation value from the reference time to the prediction time, and specifically comprises:
based on a mixed pixel decomposition method, pixel decomposition is carried out by using the obtained class abundance diagram and low-resolution NDVI data at a reference moment to construct a linear equation set, and the formula is as follows:
Δ C in formula (2)NDVI(xi,yi) When indicating the referenceLow resolution picture element (x) at moment of predictioni,yi) NDVI Change amount of (a), (b), (c)l(xi,yi) Indicating the L-th ground type in a low resolution pixel (x)i,yi) The abundance value of (1) is obtained by solving the least square solution to obtain delta FNDVI(l) And representing the amount of NDVI change of the lth terrain type in the high resolution pel from the reference time to the predicted time.
5. The method according to claim 4, wherein in step S4, the high-spatial-temporal-resolution NDVI data are searched for similar pixels in the high-spatial-resolution NDVI data at the reference time by using a moving window technique, and the transform coefficients Vi and the weight functions Wi of the similar pixels are calculated, wherein the formulas are respectively as follows:
in the formula, Hi t2、Li t2And Hi t1、Li t1Respectively representing the ith high-resolution and low-resolution pixel values corresponding to the reference time t1 and t 2;
in the formula, RiAnd DiRespectively representing the spectral difference and relative distance between the similar and central pixels.
6. The method according to claim 5, wherein in step 5, the NDVI variation value obtained in step S3 is used, and the central pixel value at the prediction time is calculated by combining the conversion coefficient Vi and the weight function Wi obtained at step S4, so as to obtain the NDVI data with high spatial resolution at the prediction time, which specifically comprises:
and (3) calculating the NDVI value of the central pixel of the moving window by using a formula (6), and calculating the value of each pixel of the predicted image to generate a final predicted image.
In the formula, Xw/2、Yw/2Representing the position of the center pixel; t is tp、t0Respectively representing a predicted time and a reference time; n represents the number of similar pixels in the moving window; i represents the ith similar pixel in the moving window; Δ FNDVIRepresenting the amount of NDVI change of the picture element from the reference time to the prediction time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010321530.1A CN111523451B (en) | 2020-04-22 | 2020-04-22 | Method for constructing high space-time resolution NDVI data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010321530.1A CN111523451B (en) | 2020-04-22 | 2020-04-22 | Method for constructing high space-time resolution NDVI data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111523451A true CN111523451A (en) | 2020-08-11 |
| CN111523451B CN111523451B (en) | 2023-05-30 |
Family
ID=71904368
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010321530.1A Active CN111523451B (en) | 2020-04-22 | 2020-04-22 | Method for constructing high space-time resolution NDVI data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111523451B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113077384A (en) * | 2021-03-10 | 2021-07-06 | 中山大学 | Data spatial resolution improving method, device, medium and terminal equipment |
| CN114301905A (en) * | 2020-09-23 | 2022-04-08 | 华为技术有限公司 | Resolution conversion method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014182516A (en) * | 2013-03-18 | 2014-09-29 | Fujitsu Ltd | Tree species identification device and tree species identification method |
| US20150016668A1 (en) * | 2013-07-12 | 2015-01-15 | Ut-Battelle, Llc | Settlement mapping systems |
| CN105046648A (en) * | 2015-06-25 | 2015-11-11 | 北京师范大学 | Method for constructing high temporal-spatial remote sensing data |
| CN107103584A (en) * | 2017-04-11 | 2017-08-29 | 北京师范大学 | A kind of production high-spatial and temporal resolution NDVI weighted based on space-time method |
| CN108613933A (en) * | 2018-06-13 | 2018-10-02 | 中南林业科技大学 | Forest land arid space-time dynamic monitoring method based on multi-sources RS data fusion |
| CN110363246A (en) * | 2019-07-18 | 2019-10-22 | 滨州学院 | A kind of fusion method of high-spatial and temporal resolution vegetation index NDVI |
-
2020
- 2020-04-22 CN CN202010321530.1A patent/CN111523451B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014182516A (en) * | 2013-03-18 | 2014-09-29 | Fujitsu Ltd | Tree species identification device and tree species identification method |
| US20150016668A1 (en) * | 2013-07-12 | 2015-01-15 | Ut-Battelle, Llc | Settlement mapping systems |
| CN105046648A (en) * | 2015-06-25 | 2015-11-11 | 北京师范大学 | Method for constructing high temporal-spatial remote sensing data |
| CN107103584A (en) * | 2017-04-11 | 2017-08-29 | 北京师范大学 | A kind of production high-spatial and temporal resolution NDVI weighted based on space-time method |
| CN108613933A (en) * | 2018-06-13 | 2018-10-02 | 中南林业科技大学 | Forest land arid space-time dynamic monitoring method based on multi-sources RS data fusion |
| CN110363246A (en) * | 2019-07-18 | 2019-10-22 | 滨州学院 | A kind of fusion method of high-spatial and temporal resolution vegetation index NDVI |
Non-Patent Citations (5)
| Title |
|---|
| MINGQUAN WU等: "Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring", 《INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH》, vol. 12, no. 08, 20 August 2015 (2015-08-20), pages 9920 - 9937 * |
| ZHANGYAN JIANG等: "Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction", vol. 101, no. 3, pages 366 - 378 * |
| 孙锐等: "MODIS和HJ-1CCD数据时空融合重构NDVI时间序列", vol. 20, no. 3, pages 361 - 373 * |
| 石月婵等: "融合多源遥感数据生成高时空分辨率数据的方法对比", 《红外与毫米波学报》, vol. 34, no. 01, 15 February 2015 (2015-02-15), pages 92 - 99 * |
| 郭兴宇: "基于Landsat/MODIS数据融合的土地利用/覆被分类研究——以秦皇岛地区为例", 《中国优秀博硕士学位论文全文数据库（硕士）基础科学辑》, no. 07, 15 July 2018 (2018-07-15), pages 22 - 25 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114301905A (en) * | 2020-09-23 | 2022-04-08 | 华为技术有限公司 | Resolution conversion method and device |
| CN114301905B (en) * | 2020-09-23 | 2023-04-04 | 华为技术有限公司 | Resolution conversion method and device |
| CN113077384A (en) * | 2021-03-10 | 2021-07-06 | 中山大学 | Data spatial resolution improving method, device, medium and terminal equipment |
| CN113077384B (en) * | 2021-03-10 | 2022-04-29 | 中山大学 | Data spatial resolution improving method, device, medium and terminal equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111523451B (en) | 2023-05-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Spatio-temporal fusion for remote sensing data: An overview and new benchmark | |
| Li et al. | Hyperspectral pansharpening via improved PCA approach and optimal weighted fusion strategy | |
| CN102829739B (en) | Object-oriented remote sensing inversion method of leaf area index of crop | |
| CN103914678B (en) | Abandoned land remote sensing recognition method based on texture and vegetation indexes | |
| CN110363246B (en) | Fusion method of vegetation index NDVI with high space-time resolution | |
| CN108428220B (en) | Automatic geometric correction method for ocean island reef area of remote sensing image of geostationary orbit satellite sequence | |
| Li et al. | A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions | |
| Gou et al. | Remote sensing image super-resolution reconstruction based on nonlocal pairwise dictionaries and double regularization | |
| CN112560570A (en) | High-resolution earth surface temperature estimation method based on cooperative downscaling and data fusion | |
| CN112766155A (en) | Deep learning-based mariculture area extraction method | |
| Liu et al. | SSAU-Net: A spectral–spatial attention-based U-Net for hyperspectral image fusion | |
| CN111523451A (en) | Method for constructing high-space-time resolution NDVI data | |
| CN106097252A (en) | High spectrum image superpixel segmentation method based on figure Graph model | |
| Jiang et al. | Unmixing-based spatiotemporal image fusion accounting for complex land cover changes | |
| Dai et al. | A remote sensing spatiotemporal fusion model of landsat and modis data via deep learning | |
| CN114387531B (en) | Surface temperature downscaling method based on improved geographic weighted regression model | |
| CN117314811A (en) | SAR-optical image fusion method based on hybrid model | |
| CN116343058A (en) | Global collaborative fusion-based multispectral and panchromatic satellite image earth surface classification method | |
| Wang et al. | Multiresolution analysis pansharpening based on variation factor for multispectral and panchromatic images from different times | |
| Wang et al. | A comprehensive review of spatial-temporal-spectral information reconstruction techniques | |
| Jing et al. | Cloud removal for optical remote sensing imagery using the SPA-CycleGAN network | |
| CN112446256A (en) | Vegetation type identification method based on deep ISA data fusion | |
| Liu et al. | Bi-LSTM model for time series leaf area index estimation using multiple satellite products | |
| CN109840539B (en) | Remote sensing space-time data fusion method based on ground block pattern spots | |
| CN112883823A (en) | Land cover category sub-pixel positioning method based on multi-source remote sensing data fusion |
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 |