CN111105402A - SEVI (sequence independent variable) adjustment factor optimization method based on information entropy - Google Patents
SEVI (sequence independent variable) adjustment factor optimization method based on information entropy Download PDFInfo
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Abstract
The invention relates to an SEVI (sequence independent variable) adjustment factor optimization method based on information entropy, which comprises the steps of firstly selecting a research area according to a research target and obtaining a corresponding long-time sequence remote sensing image; calculating a vegetation index for eliminating the shade of the research area SEVI by using the reflectivity data: then selecting a pure vegetation complex terrain area, and calculating a Shannon information entropy H (v) by utilizing a shadow elimination vegetation index SEVI; starting the adjustment factor from 0, sequentially increasing the adjustment factor at preset intervals T, and respectively calculating SEVI and H (v); and when the value of H (v) is maximum, taking the adjustment factor corresponding to the maximum H (v) as the optimal adjustment factor. The method does not need DEM data and remote sensing image classification, and does not depend on ground survey data.
Description
Technical Field
The invention relates to the field of vegetation detection, in particular to an SEVI (sequence-independent vegetation index) regulating factor optimization method based on information entropy.
Background
The existing optimization methods for regulating factors in topographic shadow elimination vegetation index SEVI and early achievements TAVI and TCVI thereof mainly comprise 4 types: "matching method (national patent No. 200910111688X)", "extreme value (Max) method (national patent No. 201010180895.3)", "correlation coefficient (r) method (national patent No. 2015108077580)", and solar altitude method (national patent No. 201611127461.0).
The 4 optimization algorithms can effectively reduce the influence of terrain shadow on the vegetation information in the mountainous area without the support of data such as DEM and the like. However, the matching method and the extreme method need to classify the remote sensing images first, wherein the matching method also needs the support of ground data and the like; the correlation coefficient method is an empirical algorithm, and the theoretical basis is weak; the s factor in the solar elevation angle method is not easy to determine, and the s factor influences the large-scale popularization and application of the SEVI technology in long-time sequence monitoring of vegetation in mountainous areas and the like.
Disclosure of Invention
In view of this, the invention aims to provide an SEVI adjustment factor optimization method based on information entropy, which does not need DEM data and remote sensing image classification and does not depend on ground survey data.
The invention is realized by adopting the following scheme: an SEVI adjustment factor optimization method based on information entropy comprises the following steps:
selecting a research area according to a research target, and acquiring a corresponding long-time sequence remote sensing image;
calculating a vegetation index for eliminating the shade of the research area SEVI by using the reflectivity data:
wherein f (Delta) is a regulatory factor, BrFor red band reflectivity data of remote-sensing images, BnirThe data is the reflectivity data of the remote sensing image near infrared band;
selecting a pure vegetation complex terrain area (such as a mountainous area natural protection area with good vegetation protection), and calculating a Shannon information entropy H (v) by utilizing a shadow elimination vegetation index SEVI;
starting the adjustment factor f (delta) from 0, sequentially increasing the adjustment factor f (delta) at preset intervals T, and respectively calculating SEVI and H (v);
when the value of H (v) is maximum, taking the adjustment factor f (delta) corresponding to the maximum H (v) as the optimal adjustment factor fopt：
fopt＝argmax(H(v))。
Further, selecting a pure vegetation complex terrain area, and calculating a shannon information entropy h (v) by using a shadow elimination vegetation index SEVI specifically comprises:
wherein,
in the formula, piIs the proportion of the i pixel information; n is the number of pixels in the region of interest,
and f (delta) of the i pixel is equal to the SEVI calculation result when the value is equal to f.
Further, the preset interval T is 0.001.
Further, the air conditioner is provided with a fan,
compared with the prior art, the invention has the following beneficial effects:
1. the method uses the information entropy to calculate the adjustment factor, has stronger theoretical basis, gets rid of the defect that the original empirical algorithm is difficult to understand, and can be applied to monitoring and analyzing the vegetation change in the mountainous area for a long time sequence;
2. the optimal solution determined by the invention ensures that SEVI can effectively eliminate the interference of various terrain shadows on the vegetation information in the mountainous area;
3. the method only needs the waveband data carried by the remote sensing image, does not need the support of ground survey data or field investigation data and the like, and realizes the minimization of data cost and time cost;
4. the method has simple process and strong operability.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating an f (Δ) optimization calculation curve according to an embodiment of the present invention.
Fig. 3 shows a long-term sequence topographic shadow map according to an embodiment of the present invention, (a) shows a map in 2011, (b) shows a map in 2012, (c) shows a map in 2013, (d) shows a map in 2014, (e) shows a map in 2015, (f) shows a map in 2016, (g) shows a map in 2017, and (h) shows a map in 2018.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides an SEVI adjustment factor optimization method based on information entropy, including the following steps:
selecting a research area according to a research target, and acquiring a corresponding long-time sequence remote sensing image;
calculating a vegetation index for eliminating the shade of the research area SEVI by using the reflectivity data:
wherein f (Delta) is a regulatory factor, BrFor red band reflectivity data of remote-sensing images, BnirThe data is the reflectivity data of the remote sensing image near infrared band;
selecting a pure vegetation complex terrain area (such as a mountainous area natural protection area with good vegetation protection), and calculating a Shannon information entropy H (v) by utilizing a shadow elimination vegetation index SEVI;
starting the adjustment factor f (delta) from 0, sequentially increasing the adjustment factor f (delta) at preset intervals T, and respectively calculating SEVI and H (v);
when the value of H (v) is maximum, taking the adjustment factor f (delta) corresponding to the maximum H (v) as the optimal adjustment factor fopt(as shown in fig. 2, the dot in fig. 2 is f (Δ) corresponding to the maximum h (v)):
fopt＝argmax(H(v))。
in this embodiment, the selecting a pure vegetation complex terrain area, and calculating a shannon information entropy h (v) by using a shadow elimination vegetation index SEVI specifically includes:
wherein,
in the formula, piIs the proportion of the i pixel information; n is the number of pixels in the region of interest,
and f (delta) of the i pixel is equal to the SEVI calculation result when the value is equal to f.
In this embodiment, the preset interval T is 0.001.
In the present embodiment, it is preferred that,
the SEVI calculation result of the embodiment is shown in fig. 3, and application and verification of the method to the multispectral remote sensing image in the 8 th period of 2018 in the natural protection area 2011-2018 in the wuyishan mountain of china show that the average errors of the SEVI in the 8 th period calculated by the method in the terrain ghost and the shadow relative to the non-shadow sunny slope are only 0.93% and-1.94% respectively, the average value of the determination coefficient of the SEVI and the cosine value of the sun incident angle (cosi) correlation analysis is only 0.005, the sample variation coefficient is less than 9.69%, and a good terrain shadow correction effect of the remote sensing image of the long-time sequence is obtained.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (4)
1. An SEVI regulation factor optimization method based on information entropy is characterized by comprising the following steps:
selecting a research area according to a research target, and acquiring a corresponding long-time sequence remote sensing image;
calculating a vegetation index for eliminating the shade of the research area SEVI by using the reflectivity data:
wherein f (Delta) is a regulatory factor, BrFor red band reflectivity data of remote-sensing images, BnirThe data is the reflectivity data of the remote sensing image near infrared band;
selecting a pure vegetation complex terrain area, and calculating a Shannon information entropy H (v) by utilizing a shadow elimination vegetation index SEVI;
starting the adjustment factor f (delta) from 0, sequentially increasing the adjustment factor f (delta) at preset intervals T, and respectively calculating SEVI and H (v);
and when the value of H (v) is maximum, taking the adjustment factor f (delta) corresponding to the maximum H (v) as the optimal adjustment factor.
2. The SEVI regulation factor optimization method based on information entropy according to claim 1, wherein the method for selecting the pure vegetation complex terrain area and calculating the Shannon information entropy H (v) by using the shadow elimination vegetation index SEVI specifically comprises the following steps:
wherein,
in the formula, piIs the proportion of the i pixel information; n is the number of pixels in the region of interest,
and f (delta) of the i pixel is equal to the SEVI calculation result when the value is equal to f.
3. An information entropy based SEVI adjustment factor optimization method according to claim 1, wherein the preset interval T is 0.001.
4. An information entropy based SEVI adjustment factor optimization method according to claim 2,
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112964643A (en) * | 2021-02-03 | 2021-06-15 | 福州大学 | Method for correcting landform falling shadow of visible light wave band of remote sensing image |
| CN114332645A (en) * | 2021-12-31 | 2022-04-12 | 福州大学 | SEVI (gradient information entropy) regulation factor optimization method based on steep slope block information entropy |
| CN114778483A (en) * | 2022-04-25 | 2022-07-22 | 福州大学 | Method for correcting terrain shadow of remote sensing image near-infrared wave band for monitoring mountainous region |
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| CN112964643A (en) * | 2021-02-03 | 2021-06-15 | 福州大学 | Method for correcting landform falling shadow of visible light wave band of remote sensing image |
| CN114332645A (en) * | 2021-12-31 | 2022-04-12 | 福州大学 | SEVI (gradient information entropy) regulation factor optimization method based on steep slope block information entropy |
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