CN109543729A - Time series data land cover classification method based on characteristic parameter cluster - Google Patents
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Abstract
The purpose of the present invention is to provide the time series data land cover classification methods clustered based on characteristic parameter, read the MODIS timing image of windy and sandy soil first;Read cluster initial value;Research area's windy and sandy soil situation is classified, the value for extracting these characteristic points is drawn indicatrix and inputted as the initial value of subsequent classification;Calculate characteristic parameter;It calculates subordinated-degree matrix and calculates cluster centre;Distance is less than precision, carries out in next step, divides time series data classification according to subordinated-degree matrix, finally exports result.The beneficial effects of the invention are as follows algorithms preferably to provide important parameter for the acquisition of windy and sandy soil information, for the monitoring of large scale windy and sandy soil and the classification foundation of Monitoring of Resource and Environment.
Description
Technical field
The invention belongs to remote sensing technology field, be related to it is a kind of based on characteristic parameter cluster to MODIS time series data
Carry out land cover classification method.
Background technique
Land use change survey is always in constantly driving land cover pattern variation, and climate change is studied in these variations, ecological environment
The work such as monitoring have great significance.Windy and sandy soil information is the premise of Changeement, but due to nature and social factor
It influences, is constantly among dynamic change, it is difficult to quickly, timely obtain latest development.Remote sensing satellite data have macroscopic view,
Dynamically, fast and accurately advantage has irreplaceable status in the detection of a wide range of surface cover.For monitoring on a large scale,
High spatial resolution satellite generally has the lower return visit period, influences vulnerable to weather conditions, can not obtain effective image in time
Data, it is difficult to effective monitoring in real time is carried out to big region, and middle low resolution image has greater advantage in these areas.It grinds
Study carefully and shows more accurately portray windy and sandy soil type using high time resolution image.Vegetation index enhances vegetation letter
Breath, weakens non-vegetation information, provides an important basis for Land Information cover.It is imaged using MODIS intermediate-resolution
Spectrometer (moderate-resolution imaging spectroradiometer), the high time resolution timing of production
Data carry out timing curve signature analysis in conjunction with land use data, extract timing curve characteristic parameter, based on fuzzy poly-
Class algorithm fast implements the automatic classification of windy and sandy soil in regional scope, and is analyzed its classification results and precision, with
Phase provides technical method for the quick and precisely monitoring that Land in Regional Land is covered and supports.
Summary of the invention
The purpose of the present invention is to provide the time series data land cover classification methods clustered based on characteristic parameter, originally
Advantageous effect of the invention is that algorithm preferably can provide important parameter for the acquisition of windy and sandy soil information, is covered for large scale soil
Monitored and Monitoring of Resource and Environment classification foundation.
The technical scheme adopted by the invention is that following the steps below:
Step 1: reading MODIS time sequence image data collection;
Step 2: reading cluster initial value;
Research area's windy and sandy soil situation is classified, the value for extracting these characteristic points draws indicatrix as subsequent classification
Initial value input;
Merge ground class be five classes: one season crop, two season crop, Forest and sod, construction land, waters.
Wherein, classification water irrigate the fields as one season crop;Nonirrigated farmland as two season crop;Orchard, forest land are as Forest and sod;It is waste
Meadow, city, organic town are as construction land;The reservoir water surface, the river water surface are as waters.
Step 3: calculating characteristic parameter;
Lance distance is selected to define the distance of two curves, it, which overcomes Euclidean distance, the shortcomings that dimension, to big
Singular value is insensitive, is defined as:
Wherein m is time series Xi、XjLength, the instant number of phases.Lance distance, which can overcome, the shortcomings that dimension, but not
Similar curves can be distinguished well, therefore the statistical nature of introducing curve and related coefficient are corrected.Basic statistics feature
It is to describe some statistics of time series global structure including extreme value, mean value and variance, it is not the distinctive spy of time series
Sign, but can be used for describing the feature of any group of data.Related coefficient can be very good reflection curve correlation and correlation
Direction.Using the statistical nature of timing curve, lance distance and related coefficient common weight composition characteristic parameter, characteristic parameter is used
Distance defines the similarity degrees of two curves.
d(Xi, Xj)=ω1Sij+ω2Lij+ω3RijI, j=1,2 ..., n (2)
Step 4: calculating subordinated-degree matrix and calculate cluster centre;
Fuzzyc-means (FCM) be it is a kind of permission data belong to two classes or more than clustering method.The present invention uses should
Characteristic information in clustering time series data, based on the objective function minimized below:
Wherein m is greater than 1 any real number, uijIt is xiDegree of membership in j class, xiIt is the measured value of i-th of data, cj
It is the cluster centre of j class.| | * | | it is the similarity benchmark between any one measured value and center.Fuzzy is by changing above
In generation, arrives optimal objective function, during which constantly updates degree of membership uijWith cluster centre cj。
Step 6: distance is less than precision, then carries out in next step, otherwise jumping to step 3;
Step 7: dividing time series data classification according to subordinated-degree matrix;
Step 8: output result.
Detailed description of the invention
Fig. 1 status is the timing curve of water body.
Specific embodiment
The present invention is described in detail With reference to embodiment.
It studies area and selects Taian Shandong city, be located at middle Shandong Province, between 116 ° 20 ' -117 ° 59 ' of east longitude, north latitude 35-degree
Between 38 ' -36 ° 28 ', entire topography is southwester tilted from northeast, geomorphic type multiplicity.Belong to the continental monsoon semi-moist in temperate zone
Climatic province makes a clear distinction between the four seasons, and light temperature is synchronous, rain heat same season, 12.9 DEG C of annual gas low temperature, 697 millimeters of average annual precipitation.MAIN SOILS
Type is brown earth, cinnamon soil, Shajiang black soil and moisture soil, and overall soil fertility is higher.By 2016, Tai'an territory total area
776141.48 hectares, wherein arable land 364721.23 hectares, 40559.69 hectares of field, 111280.47 hectares of forest land, construction are used
125649.68 hectares of ground, 62695.08 hectares of unused land.
Select vegetation product MODIS13Q1 (the MODIS/Terra Vegetation Indices 16- of MODIS/Terra
Day L3Global 250m SIN Grid) data.The time range of data is 16 days to 2016 October in 2015 of September 29
Day, every 16 days time points, the data file at totally 23 time points.The spatial resolution of data is 250m.Research on utilization area
Present landuse maps in 2009 and online high-resolution satellite image are as nicety of grading reference foundation.With present status of land utilization
Figure is reference, in conjunction with day map high definition image, generates test samples, amounts to 2065 pixels.Using accuracy test sample to point
Class result carries out accuracy test, the results are shown in Table 1.
1 classification results confusion matrix of table
Classification results Kappa coefficient is 0.8432.Wherein, nicety of grading of all categories is respectively construction land 82.71%, and one
Season crop 88.87%, Forest and sod 93.95%, two season crop 87.59%, water body 58.82%.Wherein water body and construction land are missed
Score highest, this is also consistent with indicatrix variation in cluster result.
It is water body lake on status Fig. 1, chooses middle of a lake unit and successively draw curve outward, it can be seen that EVI value exists always
Rise, since curvilinear characteristic itself is similar, is just accidentally divided into construction land being raised to the L2 stage.
The present invention is based on the EVI product data of MODIS 250m resolution ratio to have made 2015-2016 time series data collection, chooses
Statistical nature, lance distance and the related coefficient common weight composition characteristic parameter of timing curve, with the distance of characteristic parameter come
Define the similarity degree of two curves.Data category is divided by the method for fuzzy clustering, is extracted research area's windy and sandy soil feelings
Condition, and classification results precision is analyzed.Main Conclusions is as follows:
(1) timing curve is smoothed using Hants method, different vegetative coverage timing curve features can be made more
Add obvious, and smoothing processing enchancement factor influences the exceptional value generated, provides the foundation for further sort research.
(2) by analyzing characteristic parameter, overture when statistical nature, lance distance and related coefficient respectively define is obtained
The feature of line.Statistical nature reflects that the basic numerical attribute of curve, lance distance reflect the feature of timing curve, and related coefficient is anti-
The similarity degree of curvilinear motion is reflected, characteristic parameter complements each other, and can obtain preferable classification knot by adjusting characteristic parameter
Fruit.
(3) by the cluster to curvilinear characteristic parameter, pass through the comparative analysis to cluster front and back ground class curve, display cluster
Algorithm can further mining data built-in attribute, the cluster curve closer to reality is provided.It can be avoided because threshold value is selected
It selects.
(4) pass through fuzzy clustering operation, windy and sandy soil be divided into five classes: construction land, Forest and sod, one season Crops Land,
Two season Crops Land and water body.As a result it is 0.8432 that overall classification accuracy, which reaches 89.37%, kappa coefficient,.
The above is only not to make limit in any form to the present invention to better embodiment of the invention
System, any simple modification that embodiment of above is made according to the technical essence of the invention, equivalent variations and modification,
Belong in the range of technical solution of the present invention.
Claims (4)
1. based on characteristic parameter cluster time series data land cover classification method, it is characterised in that according to the following steps into
Row:
Step 1: reading the MODIS timing image of windy and sandy soil;
Step 2: reading cluster initial value;
Research area's windy and sandy soil situation is classified, the value for extracting these characteristic points draws indicatrix as the initial of subsequent classification
Value input;
Step 3: calculating characteristic parameter;
Step 4: calculating subordinated-degree matrix and calculate cluster centre;
Step 5: distance is less than precision, then carries out in next step, otherwise jumping to step 3;
Step 6: dividing time series data classification according to subordinated-degree matrix;
Step 7: output result.
2. according to the time series data land cover classification method based on characteristic parameter cluster described in claim 1, feature
Be: in the step 2 class be five classes: one season crop, two season crop, Forest and sod, construction land, waters;
Wherein, classification water irrigate the fields as one season crop;Nonirrigated farmland as two season crop;Orchard, forest land are as Forest and sod;Weeds
Ground, city, organic town are as construction land;The reservoir water surface, the river water surface are as waters.
3. according to the time series data land cover classification method based on characteristic parameter cluster described in claim 1, feature
It is: calculation of characteristic parameters method in the step 3: selects lance distance to define the distance of two curves;
Wherein m is time series Xi、XjLength, added jointly using the statistical nature, lance distance and related coefficient of timing curve
Composition characteristic parameter is weighed, the similarity degree of two curves is defined with characteristic parameter distance:
d(Xi, Xj)=ω1Sij+ω2Lij+ω3RijI, j=1,2 ..., n. (2)
4. according to the time series data land cover classification method based on characteristic parameter cluster described in claim 1, feature
It is: calculates subordinated-degree matrix in the step 4 and calculate cluster centre;
Using the characteristic information in Fuzzyc-means clustering method cluster time series data, based on the target letter minimized below
Number:
Wherein m is greater than 1 any real number, uijIt is xiDegree of membership in j class, xiIt is the measured value of i-th of data, cjIt is j class
Cluster centre, | | * | | be the similarity benchmark between any one measured value and center;Fuzzy is by iterating to above most
During which excellent objective function constantly updates degree of membership uijWith cluster centre cj;
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110516552A (en) * | 2019-07-29 | 2019-11-29 | 南京航空航天大学 | A kind of multipolarization radar image classification method and system based on timing curve |
CN111984702A (en) * | 2020-08-17 | 2020-11-24 | 北京大学深圳研究生院 | Method, device, equipment and storage medium for analyzing spatial evolution of village and town settlement |
CN113989668A (en) * | 2021-10-27 | 2022-01-28 | 辽宁工程技术大学 | Remote sensing crop automatic classification method based on time series characteristics |
CN113989668B (en) * | 2021-10-27 | 2024-10-15 | 辽宁工程技术大学 | Remote sensing crop automatic classification method based on time sequence characteristics |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104318270A (en) * | 2014-11-21 | 2015-01-28 | 东北林业大学 | Land cover classification method based on MODIS time series data |
CN105005784A (en) * | 2015-05-21 | 2015-10-28 | 中国科学院遥感与数字地球研究所 | Time sequence remote sensing image land cover classification method based on CD-DTW distance |
CN107273820A (en) * | 2017-05-26 | 2017-10-20 | 中国科学院遥感与数字地球研究所 | A kind of Land Cover Classification method and system |
CN107345860A (en) * | 2017-07-11 | 2017-11-14 | 南京康尼机电股份有限公司 | Rail vehicle door sub-health state recognition methods based on Time Series Data Mining |
CN108615054A (en) * | 2018-04-18 | 2018-10-02 | 清华大学 | The overall target construction method that similitude is weighed between drainage pipeline networks node |
-
2018
- 2018-11-08 CN CN201811322302.5A patent/CN109543729A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104318270A (en) * | 2014-11-21 | 2015-01-28 | 东北林业大学 | Land cover classification method based on MODIS time series data |
CN105005784A (en) * | 2015-05-21 | 2015-10-28 | 中国科学院遥感与数字地球研究所 | Time sequence remote sensing image land cover classification method based on CD-DTW distance |
CN107273820A (en) * | 2017-05-26 | 2017-10-20 | 中国科学院遥感与数字地球研究所 | A kind of Land Cover Classification method and system |
CN107345860A (en) * | 2017-07-11 | 2017-11-14 | 南京康尼机电股份有限公司 | Rail vehicle door sub-health state recognition methods based on Time Series Data Mining |
CN108615054A (en) * | 2018-04-18 | 2018-10-02 | 清华大学 | The overall target construction method that similitude is weighed between drainage pipeline networks node |
Non-Patent Citations (2)
Title |
---|
李深洛: "基于特征的时间序列聚类", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
王伶俐 等: "植被指数时序数据距离测度方法评价", 《遥感学报》 * |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110516552A (en) * | 2019-07-29 | 2019-11-29 | 南京航空航天大学 | A kind of multipolarization radar image classification method and system based on timing curve |
CN110516552B (en) * | 2019-07-29 | 2021-02-05 | 南京航空航天大学 | Multi-polarization radar image classification method and system based on time sequence curve |
CN111984702A (en) * | 2020-08-17 | 2020-11-24 | 北京大学深圳研究生院 | Method, device, equipment and storage medium for analyzing spatial evolution of village and town settlement |
CN113989668A (en) * | 2021-10-27 | 2022-01-28 | 辽宁工程技术大学 | Remote sensing crop automatic classification method based on time series characteristics |
CN113989668B (en) * | 2021-10-27 | 2024-10-15 | 辽宁工程技术大学 | Remote sensing crop automatic classification method based on time sequence characteristics |
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Application publication date: 20190329 |