CN106600586B - A kind of TAVI regulatory factor algorithm based on solar elevation - Google Patents

A kind of TAVI regulatory factor algorithm based on solar elevation Download PDF

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CN106600586B
CN106600586B CN201611127461.0A CN201611127461A CN106600586B CN 106600586 B CN106600586 B CN 106600586B CN 201611127461 A CN201611127461 A CN 201611127461A CN 106600586 B CN106600586 B CN 106600586B
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tavi
remote sensing
sensing image
solar elevation
regulatory factor
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CN106600586A (en
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江洪
毛政元
肖桂荣
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Fuzhou University
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Fuzhou University
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Priority to PCT/CN2017/076022 priority patent/WO2018028191A1/en
Priority to US15/740,011 priority patent/US10527542B2/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • 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
    • 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
    • G06T2207/10048—Infrared image
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30184—Infrastructure
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture

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Abstract

The present invention relates to a kind of TAVI regulatory factor algorithm based on solar elevation, comprising the following steps: Yunnan snub-nosed monkey, the apparent reflectivity values distribution of analysis image wave band extract solar elevation and calculate regulatory factor.The present invention is not necessarily to dem data and classification of remote-sensing images, independent of ground investigation data, using the solar elevation with actual physical meaning as calculating parameter, ensure that TAVI can effectively eliminate interference of the influence of topography to vegetation information, reach and is better than the C model topographical correction effect based on dem data, and the problems such as atural object vegetation information inversion accuracy as caused by remote sensing image and dem data registration accuracy difference declines is avoided, promoting to TAVI in the widespread adoption of the accurate inverting of complicated landform mountain area vegetation information has important scientific meaning and economic value.

Description

A kind of TAVI regulatory factor algorithm based on solar elevation
Technical field
The present invention relates to TAVI regulatory factor algorithm field, especially a kind of TAVI regulatory factor based on solar elevation Algorithm.
Background technique
The existing landform published, which adjusts regulatory factor f (Δ) optimization method in vegetation index (TAVI), mainly 2 kinds: " searching of optimal matching method " and " the method for optimizing extremums ".
" searching of optimal matching " algorithm (national patent 200910111688X) calculates step are as follows: (1) image classification divides distant Feel the Schattenseite and tailo of massif in image, and chooses typical region；(2) target identification, by ground investigation data, on-the-spot investigation High resolution image data of data, data of taking photo by plane or GoogleEarth etc. verify the homogenieity of Schattenseite and tailo vegetation, know The other typical region Schattenseite part consistent or close with tailo vegetation；(3) Optimized Matching enables f (Δ) since 0, incremented by successively, TAVI is investigated to change in the vegetation index value of typical region Schattenseite and tailo vegetation uniform portion, it, can be true when the two is equal Determine the optimal result of f (Δ).
" extremal optimization " algorithm (national patent number 201010180895.3) calculates step are as follows: (1) image classification divides distant Feel the Schattenseite and tailo of massif in image；(2) extreme value is calculated, the maximum value M of Schattenseite part TAVI is calculatedTAVI yinWith tailo part The maximum value M of TAVITAVI sun；(3) iteration optimizing enables f (Δ) since 0, incremented by successively, when meeting the condition of following formula, Obtain f (Δ) optimal value.
|MTAVI yin-MTAVI sun|≤ε, ε → 0, f (Δ)=0~∞
According to above-mentioned 2 kinds of optimization algorithms, support of the TAVI without data such as DEM can effectively cut down landform and plant to mountain area By the influence of information.But this 2 kinds of optimization algorithms are empirical strong and physical significance is on the weak side, and require to divide remote sensing image Class；Wherein, " searching of optimal matching " algorithm also needs the support of ground data etc., and " extremal optimization " algorithm is easily trapped into local optimum Rather than global optimum, the automation application that this all limits TAVI is horizontal, is unfavorable for TAVI and promotes and applies on a large scale.
Summary of the invention
In view of this, the purpose of the present invention is to propose to a kind of TAVI regulatory factor algorithm based on solar elevation, is not necessarily to Dem data and classification of remote-sensing images, at the same independent of ground investigation data, and there is actual physical meaning, to TAVI multiple The widespread adoption of the accurate inverting of miscellaneous landform mountain area vegetation information, which is promoted, has important scientific meaning and economic value.
The present invention is realized using following scheme: a kind of TAVI regulatory factor algorithm based on solar elevation specifically includes Following steps:
Step S1: pre-processing remote sensing image, by remote sensing image radiation calibration, generates image apparent reflectance number According to；
Step S2: carrying out quality analysis to remote sensing image, counts remote sensing image red spectral band and near infrared band is apparently anti- Penetrate rate data；It is whether reasonable in the two wave band reflectivity to analyze mountain area vegetation, determines whether image is good for use；Usual mountain area For vegetation red spectral band reflectivity average value 0.05 or so, near infrared band reflectivity average value is greater than 0.2.
Step S3: calculating regulatory factor f (Δ), reads solar elevation when satellite passes by from remote sensing image header file；Its The calculating of middle f (Δ) uses following formula:
F (Δ)=s-sin (α)；
In formula, s is sensor parameters, and α is solar elevation；
Step S4: landform is calculated using following formula and adjusts vegetation index TAVI:
In formula, BnirFor remote sensing image near infrared band apparent reflectance data, BrIt is apparently anti-for remote sensing image red spectral band Penetrate rate data.
Further, in the step S2 further include: calculate mean value, intermediate value, the variance of red spectral band and near infrared band Deng.
Further, the remote sensing image includes optical remote sensing image data etc..
Further, in step S3, the default value of the sensor parameters s is 1, and different sensors value is slightly done micro- It adjusts, it is 1.2 that wherein 5 value of Landsat, which is 0.9, Landsat, 8 value,.
Compared with prior art, the invention has the following beneficial effects:
1, physical significance is strong, easy to operate: the present invention calculates regulatory factor, tool using solar elevation and the sensor factor There is stronger physical significance, calculation process is simple, greatly improves TAVI application automatization level.
2, topographical correction effect is obvious: present invention determine that regulatory factor, guarantee landform adjust vegetation index can effectively disappear Interference except the influence of topography to vegetation information；By multispectral distant to research area difference phase Landsat TM (as shown in Figure 1) Feel image data application (including 8 image data of Landsat 5 and Landsat), show present invention determine that regulatory factor energy Guarantee the TAVI of 8 image of different phase Landsat 5 and Landsat and the related coefficient of solar incident angle cosine value (cosi) (r) absolute value average value is lower than 0.1 (such as table 1), is better than other common vegetation indexs, calculates than the C correcting image based on DEM NDVI effect is not bad (as shown in Figure 1).
Different phase Landsat TM data f (Δ) the regulatory factor calculated results of table 1 and the anti-influence of topography effect ratio of TAVI Compared with
3, data requirements is few, at low cost: the wave band data and altitude of the sun that the present invention only needs remote sensing image self-contained Angular data, without the support of ground investigation data or on-the-spot investigation data etc., data cost and time cost are realized and are minimized.
Detailed description of the invention
Fig. 1 is image contrast schematic diagram in present invention research area.
Fig. 2 is method flow schematic diagram of the invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
As shown in Fig. 2, present embodiments providing a kind of TAVI regulatory factor algorithm based on solar elevation, specifically include Following steps:
Step S1: pre-processing remote sensing image, by remote sensing image radiation calibration, generates image apparent reflectance number According to；
Step S2: carrying out quality analysis to remote sensing image, counts remote sensing image red spectral band and near infrared band is apparently anti- Penetrate rate data；It is whether reasonable in the two wave band reflectivity to analyze mountain area vegetation, determines whether image is good for use；Usual mountain area For vegetation red spectral band reflectivity average value 0.05 or so, near infrared band reflectivity average value is greater than 0.2.
Step S3: calculating regulatory factor f (Δ), reads solar elevation when satellite passes by from remote sensing image header file；Its The calculating of middle f (Δ) uses following formula:
F (Δ)=s-sin (α)；
In formula, s is sensor parameters, and α is solar elevation；
Step S4: landform is calculated using following formula and adjusts vegetation index TAVI:
In formula, BnirFor remote sensing image near infrared band apparent reflectance data, BrIt is apparently anti-for remote sensing image red spectral band Penetrate rate data.
In the present embodiment, in the step S2 further include: calculate the mean value of red spectral band and near infrared band, intermediate value, Variance etc..
In the present embodiment, the remote sensing image includes optical remote sensing image data etc..
In the present embodiment, in step S3, the default value of the sensor parameters s is 1, and different sensors value is slightly done Fine tuning, it is 1.2 that wherein 5 value of Landsat, which is 0.9, Landsat, 8 value,.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with Modification, is all covered by the present invention.

Claims (3)

1. a kind of TAVI regulatory factor algorithm based on solar elevation, it is characterised in that: the following steps are included:
Step S1: pre-processing remote sensing image, by remote sensing image radiation calibration, generates the apparent reflectivity data of image；
Step S2: quality analysis is carried out to remote sensing image, counts remote sensing image red spectral band and near infrared band apparent reflectance Data；It is whether reasonable in the two wave band reflectivity to analyze mountain area vegetation, determines whether image is good for use；
Step S3: calculating regulatory factor f (Δ), reads solar elevation when satellite passes by from remote sensing image header file；Wherein f The calculating of (Δ) uses following formula:
F (Δ)=s-sin (α)；
In formula, s is sensor parameters, and α is solar elevation；
Step S4: landform is calculated using following formula and adjusts vegetation index TAVI:
In formula, BnirFor remote sensing image near infrared band apparent reflectance data, BrFor remote sensing image red spectral band apparent reflectance Data.
2. a kind of TAVI regulatory factor algorithm based on solar elevation according to claim 1, it is characterised in that: described In step S2 further include: calculate mean value, intermediate value, the variance of red spectral band and near infrared band.
3. a kind of TAVI regulatory factor algorithm based on solar elevation according to claim 1, it is characterised in that: described Remote sensing image includes optical remote sensing image data.
CN201611127461.0A 2016-08-10 2016-12-09 A kind of TAVI regulatory factor algorithm based on solar elevation Active CN106600586B (en)

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CN201611127461.0A CN106600586B (en) 2016-12-09 2016-12-09 A kind of TAVI regulatory factor algorithm based on solar elevation
PCT/CN2017/076022 WO2018028191A1 (en) 2016-08-10 2017-03-09 Tavi calculation method based on waveband ration model and solar elevation angle
US15/740,011 US10527542B2 (en) 2016-08-10 2017-03-19 Method of calculating TAVI based on a band ratio model and solar altitude angle

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107730502A (en) * 2017-11-15 2018-02-23 福州大学 A kind of algorithm on the s factors in new TAVI models
CN109031343B (en) * 2018-07-13 2022-04-15 福州大学 Automatic optimization algorithm for SEVI (sequence independent optimization) adjustment factors of window traversal
CN109471125B (en) * 2018-10-17 2023-03-24 福州大学 Globally optimized SEVI (sequence independent variable) adjustment factor method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
VegetationMonitoringinRuggedTerrainwithOneNovelTopography-AdjustedVegetationIndex(TAVI);江洪等;《20103rdInternationalCongressonImageandSignalProcessing》;20101231;第2294-2297页 *
地形调节植被指数及其在森林动态监测中的应用;江洪等;《北京林业大学学报》;20110915;第33卷(第5期);第8-12页 *
地形调节植被指数构建及在植被覆盖度遥感监测中的应用;江洪等;《福州大学学报(自然科学版)》;20100831;第38卷(第4期);全文 *

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