CN112200349A - Remote sensing image heat island effect prediction method based on single window algorithm and PredRNN - Google Patents

Remote sensing image heat island effect prediction method based on single window algorithm and PredRNN Download PDF

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CN112200349A
CN112200349A CN202010972149.1A CN202010972149A CN112200349A CN 112200349 A CN112200349 A CN 112200349A CN 202010972149 A CN202010972149 A CN 202010972149A CN 112200349 A CN112200349 A CN 112200349A
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罗培韬
吕新杰
姜峥超
李君�
王玲霞
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Abstract

The invention discloses a remote sensing image heat island effect prediction method based on a single window algorithm and PredRNN, which comprises the following steps: downloading the image; image preprocessing; a single window algorithm; cutting the satellite remote sensing image processed by the single-window algorithm at each moment into a plurality of small pictures according to a certain size, marking the area and the time sequence number of each picture, and constructing a PredRNN model; processing the segmented picture set into a sequence set form, segmenting the picture sequence set according to the ratio of 8:2 of a training set to a testing set, training by using PredRNN, using the testing set as the evaluation of parameter tuning of the model, and finally determining the model to obtain the change relation of the model on the historical satellite remote sensing picture and the future satellite remote sensing picture. According to the method, on one hand, the earth surface temperature inversion result of the time point when the satellite image is missing is supplemented, and the regularity and continuity of time are guaranteed; on the other hand, the effect of predicting the future thermal environment of the city is realized.

Description

Remote sensing image heat island effect prediction method based on single window algorithm and PredRNN
Technical Field
The invention relates to a heat island effect prediction method, belongs to the technical field of earth science, and particularly relates to a remote sensing image heat island effect prediction method based on a single window algorithm and PredRNN.
Background
The surface temperature plays a crucial role in the energy exchange between the earth's surface and the atmosphere, and has a significant impact on climate, environmental changes, and human production and life. Any object whose temperature exceeds the thermodynamic temperature of 0K will constantly emit thermal infrared radiation outwards. The thermal infrared remote sensing is based on a satellite-borne or airborne thermal infrared sensor to acquire thermal infrared information of two atmospheric windows of 3-5 mu m and 8-14 mu m of a target ground object to identify the ground object and quantitatively invert surface parameter information, such as remote sensing inversion of surface temperature and surface emissivity.
The single window algorithm is suitable for remote sensing data with only one thermal wave band and is mainly used for performing surface temperature inversion on TM6 and ETM + data. The algorithm utilizes the radiation values observed by a single thermal infrared channel within an atmospheric window. In the prior art, the remote sensing image is subjected to temperature inversion only based on a single-window algorithm to obtain urban earth surface temperature data. Therefore, the earth surface temperature of the city over the years is monitored, and the development dynamics of the urban heat island effect is analyzed.
But the time continuity is poor due to the low time resolution of most freely acquired satellite images. Therefore, most researches only can carry out research and analysis on the existing satellite images, and the surface temperature is calculated by using a proper algorithm, so that the urban heat island effect is monitored and the rule is analyzed.
Disclosure of Invention
In order to overcome the defects of the technology, the invention provides a remote sensing image heat island effect prediction method based on a single window algorithm and PredRNN.
In order to solve the technical problems, the invention adopts the technical scheme that: a remote sensing image heat island effect prediction method based on a single window algorithm and PredRNN comprises the following steps:
step 1, image downloading, namely downloading a remote sensing image of a designated research area over the years according to research requirements;
step 2, image preprocessing, namely preprocessing operations such as geometric correction, inlaying, clipping, radiometric calibration, atmospheric correction and the like are carried out on the image;
step 3, performing a single window algorithm, namely calculating a spectral radiation value of a DN value of an image thermal infrared band by using a formula 1, calculating a brightness temperature of the image thermal infrared band by using a formula 2, calculating an NDVI value by using the image infrared and near infrared bands, calculating a ground surface specific radiance by using the NDVI and a formula 3, estimating an atmospheric transmittance, and calculating a ground surface temperature by using the single window algorithm;
step 4, cutting the satellite remote sensing image processed by the single-window algorithm at each moment into a plurality of small pictures according to a certain size, marking the region and the time sequence number of each picture, and constructing a PredRNN model, wherein the PredRNN is connected to the first layer of the next sequence as a hidden layer input by establishing a channel at the topmost layer of the previous sequence, so that the effect that the network can combine and utilize the characteristics closest to an output layer is realized;
step 5, processing the picture set segmented in the step 4 into a sequence set form, segmenting the picture sequence set according to the ratio 8:2 of a training set to a test set, training by using PredRNN, using the test set as the evaluation of parameter tuning of the model, and finally determining the model to obtain the change relation of the model on the historical satellite remote sensing picture and the future satellite remote sensing picture;
and 6, carrying out extrapolation prediction on the future urban surface temperature through the trained PredRNN model.
Further, equation 1 is:
L10＝gain*DN+bias
in the formula: l is10Is a spectral radiance value; gain is the gain amount; bias is the offset.
Further, equation 2 is:
Figure BDA0002684486490000021
in the formula: t is10Representing the brightness temperature (K) of the thermal infrared band of the image; l is10Is the spectral radiance value W/(m)2*μm*sr)；K1K2A constant value preset before transmission.
Further, formula 3 includes formula 31 or formula 32;
equation 31 is:
ε＝PvRvεv+(1-Pv)Rmεm+dε
equation 32 is:
ε＝PvRvεv+(1-Pv)Rsεs+dε
formula 31 is used for calculating the surface emissivity of the town earth surface, and formula 32 is used for calculating the surface emissivity of the natural earth surface;
in the formula: epsilons，εm，εvRespectively the surface emissivity of bare soil, the surface of a building and the surface emissivity of vegetation pure pixel;
Figure BDA0002684486490000031
Pvis the proportion of the mixed pixels to be planted,
Figure BDA0002684486490000032
Rv＝0.9332+0.0585Pv
Rm＝0.9886+0.1287Pv
NDVIsa normalized vegetation index representing an area completely covered by bare soil or no vegetation; NDVIvIs the normalized vegetation index of the completely covered picture element; rv，RmThe temperature ratios of vegetation and buildings, respectively.
Further, the inversion algorithm of the single window algorithm is shown in equation (3-5):
Figure BDA0002684486490000033
wherein LST is the surface temperature (K); t issRepresents the BT value (K) received by the satellite; t isaRepresents the atmospheric average action temperature value (K): a. b is a reference coefficient, a-67.355351, b-0.458606: C. d is obtained as shown in equations (3-6) and (3-7):
C＝ετ (3-6)
D＝(1-τ)[1+(1-ε)τ] (3-7)
where ε represents the surface emissivity and τ represents the atmospheric transmittance.
Further, the neural unit structure of PredRNN is shown in fig. 2:
wherein WtNeural elements at layer t for the PredRNN network, XtFor an incoming picture of the network at time t,
Figure BDA0002684486490000042
for the hidden state of the ith neuron in the network at time t,
Figure BDA0002684486490000043
images are output for the i-layer network at time t,
Figure BDA0002684486490000044
the picture is an output picture of the PredRNN network at the time t;
Figure BDA0002684486490000045
Figure BDA0002684486490000046
Figure BDA0002684486490000047
Figure BDA0002684486490000048
Figure BDA0002684486490000049
Figure BDA00026844864900000410
wherein
Figure BDA00026844864900000411
The picture input to the first layer PredRNN network at time t, gt, the abstract extraction of the PredRNN neural unit on the current time input and last time input information,
Figure BDA00026844864900000412
extracting the convolution kernel of the layer for the input picture at the current moment for the abstract image information,
Figure BDA00026844864900000413
a convolution kernel of an image output at the last moment is taken as an abstract information extraction layer; ft is the forgetting gate of the PredRN network at time t,
Figure BDA00026844864900000414
Figure BDA0002684486490000051
respectively inputting a picture at the current moment, inputting a picture at the last moment and performing convolution kernel in a network hidden layer state at the last moment for the forgetting gate; i.e. itIs the input gate of the network at time t,
Figure BDA0002684486490000052
input pictures for the input gate at the present moment, respectivelyInputting a picture at a moment, and performing convolution kernel of a network hidden layer state at the last moment; o istIs an output gate of the network and is,
Figure BDA0002684486490000053
respectively inputting a picture at the current moment, inputting a picture at the last moment and performing convolution kernel of a network hidden layer state at the last moment for the output gate; the symbol represents a convolution operation, the symbol represents a dot product operation.
The method carries out earth surface temperature inversion on the existing satellite images, and carries out model training by using the inversion result. On one hand, the model supplements the earth surface temperature inversion result of the time point when the satellite image is missing, and guarantees the regularity and continuity of time; on the other hand, the effect of predicting the future thermal environment of the city is realized.
Drawings
Fig. 1 is an overall flow chart of urban heat island effect prediction according to the invention.
FIG. 2 is a schematic diagram of the structure of the neural unit of PredRNN.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 shows a method for predicting the heat island effect of a remote sensing image based on a single window algorithm and PredRNN, which is characterized in that: the method comprises the following steps:
step 1, downloading images: downloading a remote sensing image of a designated research area over the years according to research requirements;
step 2, image preprocessing: carrying out preprocessing operations such as geometric correction, embedding, cutting, radiometric calibration, atmospheric correction and the like on the image;
step 3, single window algorithm:
a. calculating a spectral radiation value of the DN value of the thermal infrared band of the image by using a formula 1, wherein the formula 1 is as follows:
L10＝gain*DN+bias
in the formula: l is10Is a spectral radiance value; gain is the gain amount; bias is offset;
b. calculating the brightness and temperature of the thermal infrared band of the image by using a formula 2, wherein the formula 2 is as follows:
Figure BDA0002684486490000061
in the formula: t is10Representing the brightness temperature (K) of the thermal infrared band of the image; l is10Is the spectral radiance value W/(m)2*μm*sr)；K1K2A constant preset before transmission;
c. calculating the NDVI value by utilizing the infrared and near-infrared wave bands of the image;
d. calculating the surface emissivity by using the NDVI and the formula 3, wherein the formula 3 includes the formula 31 or the formula 32, and the formula 31 is:
ε＝PvRvεv+(1-Pv)Rmεm+dε
equation 32 is:
ε＝OvRvεv+(1-Pv)Rsεs+dε
formula 31 is used for calculating the surface emissivity of the town earth surface, and formula 32 is used for calculating the surface emissivity of the natural earth surface;
in the formula: epsilons，εm，εvRespectively the surface emissivity of bare soil, the surface of a building and the surface emissivity of vegetation pure pixel;
Figure BDA0002684486490000062
Pvis the proportion of the mixed pixels to be planted,
Figure BDA0002684486490000063
Rv＝0.9332+0.0585Pv
Rm＝0.9886+0.1287Pv
NDVIsa normalized vegetation index representing an area completely covered by bare soil or no vegetation;NDVIvis the normalized vegetation index of the completely covered picture element; rv，RmTemperature ratios of vegetation and buildings respectively;
e. and (3) estimating the atmospheric transmittance, and calculating the earth surface temperature by using a single-window algorithm, wherein an inversion algorithm of the single-window algorithm is shown as a formula (3-5):
Figure BDA0002684486490000071
wherein LST is the surface temperature (K); t issRepresents the BT value (K) received by the satellite; t isaRepresents the atmospheric mean action temperature value (K); a. b is a reference coefficient, a is-67.355351, b is 0.458606; C. d is obtained as shown in equations (3-6) and (3-7):
C＝ετ (3-6)
D＝(1-τ)[1+(1-ε)τ] (3-7)
where ε represents the surface emissivity and τ represents the atmospheric transmittance.
And 4, step 4:
a. cutting the satellite remote sensing image processed by the single-window algorithm at each moment into a plurality of small pictures according to a certain size, and marking the area and the time sequence number of each picture
b. Constructing a PredRNN model, wherein the earliest model for performing time-space prediction is a ConvLSTM model, and improving the model structure of the LSTM, adding a CNN convolution network layer in the model structure to ensure that the model has better prediction effect on a radar echo map, and the model is used for extracting more complex features, wherein the extraction effect is usually realized by stacking multiple layers of networks, but the ConvLSTM model structure omits the features extracted from the top layer of the last sequence, so that the PredRNN is structurally improved, and the nerve unit structure of the PredRNN is shown in FIG. 2:
wherein WtNeural elements at layer t for the PredRNN network, XtFor an incoming picture of the network at time t,
Figure BDA0002684486490000081
for the hidden state of the ith neuron in the network at time t,
Figure BDA0002684486490000082
images are output for the i-layer network at time t,
Figure BDA0002684486490000083
the picture is an output picture of the PredRNN network at the time t;
Figure BDA0002684486490000084
Figure BDA0002684486490000085
Figure BDA0002684486490000086
Figure BDA0002684486490000087
Figure BDA0002684486490000088
Figure BDA0002684486490000089
wherein
Figure BDA00026844864900000810
The picture input to the first layer PredRNN network at time t, gt, the abstract extraction of the PredRNN neural unit on the current time input and last time input information,
Figure BDA00026844864900000811
for drawingThe image information extraction layer performs convolution kernel on the input picture at the current moment,
Figure BDA00026844864900000812
and outputting the convolution kernel of the picture at the last moment for the abstract information extraction layer. f. oftFor the forgetting gate of the PredRN network at time t,
Figure BDA00026844864900000813
Figure BDA00026844864900000814
and respectively inputting a picture at the current moment, inputting a picture at the last moment and performing convolution kernel in a network hidden layer state at the last moment for the forgetting gate. i.e. itIs the input gate of the network at time t,
Figure BDA00026844864900000815
and respectively inputting a picture at the current moment, inputting a picture at the last moment and performing convolution kernel in the hidden layer state of the network at the last moment for the input gate. O istIs an output gate of the network and is,
Figure BDA00026844864900000816
respectively inputting a picture at the current moment, inputting a picture at the last moment and performing convolution kernel of a network hidden layer state at the last moment for the output gate; symbol represents convolution operation, symbol represents dot product operation;
PredRNN establishes a channel through the top layer of the last sequence to be connected to the first layer of the next sequence to be used as hidden layer input, and the effect that the network can combine and utilize the characteristics of the closest output layer is achieved;
step 5, processing the picture set segmented in the step 4 into a sequence set form, segmenting the picture sequence set according to the ratio 8:2 of a training set to a test set, training by using PredRNN, using the test set as the evaluation of parameter tuning of the model, and finally determining the model to obtain the change relation of the model on the historical satellite remote sensing picture and the future satellite remote sensing picture;
and 6, carrying out extrapolation prediction on the future urban surface temperature through the trained PredRNN model.
The method provided by the invention can be used for carrying out simulation prediction on the time point of satellite image loss by utilizing a deep learning model-PredRNN while monitoring the urban heat island effect. On one hand, the time continuity of the heat island effect research of the city over the years can be enhanced, and on the other hand, the future thermal environment of the city can be predicted and analyzed. The method firstly utilizes the traditional algorithm to analyze the urban heat island effect capable of obtaining the satellite image, achieves the purpose of monitoring the historical result and simultaneously realizes the simulation and prediction of the urban heat island effect in the future. The method not only can carry out surface temperature inversion on the time points at which the satellite images can be obtained, but also utilizes the machine learning means to carry out urban heat island inversion research on the time points at which the images are missing.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the technical scope of the present invention.

Claims (6)

1. A remote sensing image heat island effect prediction method based on a single window algorithm and PredRNN is characterized by comprising the following steps: the method comprises the following steps:
step 1, downloading images: downloading a remote sensing image of a designated research area over the years according to research requirements;
step 2, image preprocessing: carrying out preprocessing operations such as geometric correction, embedding, cutting, radiometric calibration, atmospheric correction and the like on the image;
step 3, single window algorithm: calculating a spectral radiance value of a DN value of an image thermal infrared band by using a formula 1, calculating a brightness temperature of the image thermal infrared band by using a formula 2, calculating an NDVI value by using the image infrared band and a near infrared band, calculating a ground surface emissivity by using the NDVI and a formula 3, estimating an atmospheric transmittance, and calculating a ground surface temperature by using a single window algorithm;
step 4, cutting the satellite remote sensing image processed by the single-window algorithm at each moment into a plurality of small pictures according to a certain size, marking the area and the time sequence number of each picture, and constructing a PredRNN model, wherein the PredRNN is connected to the first layer of the next sequence as a hidden layer input by establishing a channel at the topmost layer of the previous sequence, so that the effect that the network can combine and utilize the characteristics closest to an output layer is realized;
step 5, processing the picture set segmented in the step 4 into a sequence set form, segmenting the picture sequence set according to the ratio of 8:2 of a training set to a test set, training by using PredRNN, using the test set as the evaluation of parameter tuning of the model, and finally determining the model to obtain the change relation of the model relative to the historical satellite remote sensing picture and the future satellite remote sensing picture;
and 6, carrying out extrapolation prediction on the future urban surface temperature through the trained PredRNN model.
2. The remote sensing image heat island effect prediction method based on the single window algorithm and PredRNN according to claim 1, characterized in that:
the formula 1 is:
L10＝gain*DN+bias
in the formula: l is10Is a spectral radiance value; gain is the gain amount; bias is the offset.
3. The remote sensing image heat island effect prediction method based on the single window algorithm and PredRNN according to claim 1, characterized in that:
the formula 2 is:
Figure FDA0002684486480000021
in the formula: t is10Representing the brightness temperature (K) of the thermal infrared band of the image; l is10Is the spectral radiance value W/(m)2*μm*sr)；K1K2A constant value preset before transmission.
4. The remote sensing image heat island effect prediction method based on the single window algorithm and PredRNN according to claim 1, characterized in that: the formula 3 includes a formula 31 or a formula 32;
the equation 31 is:
ε＝PvRvεv+(1-Pv)Rmεm+dε
the equation 32 is:
ε＝PvRvεv+(1-Pv)Rsεs+dε
formula 31 is used for calculating the surface emissivity of the town earth surface, and formula 32 is used for calculating the surface emissivity of the natural earth surface;
in the formula: epsilons，εm，εvRespectively the surface emissivity of bare soil, the surface of a building and the surface emissivity of vegetation pure pixel;
Figure FDA0002684486480000022
Pvis the proportion of the mixed pixels to be planted,
Figure FDA0002684486480000023
Rv＝0.9332+0.0585Pv
Rm＝0.9886+0.1287Pv
NDVIsa normalized vegetation index representing an area completely covered by bare soil or no vegetation; NDVIvIs the normalized vegetation index of the completely covered picture element; rv，RmThe temperature ratios of vegetation and buildings, respectively.
5. The remote sensing image heat island effect prediction method based on the single window algorithm and PredRNN according to claim 1, characterized in that: the inversion algorithm of the single window algorithm is shown as formula (3-5):
Figure FDA0002684486480000031
wherein LST is the surface temperature (K); t issRepresents the BT value (K) received by the satellite; t isaRepresents the atmospheric mean temperature value (K): a. b is a reference coefficient, a is-67.355351, b is 0.458606; C. d is obtained as shown in formulas (3-6) and (3-7):
C＝ετ (3-6)
D＝(1-τ)[1+(1-ε)τ] (3-7)
where ε represents the surface emissivity and τ represents the atmospheric transmittance.
6. The remote sensing image heat island effect prediction method based on the single window algorithm and PredRNN according to claim 1, characterized in that: the structure of the nerve unit of PredRNN is shown in FIG. 2:
wherein WtNeural elements at layer t for the PredRNN network, XtFor the input picture of the network at time t,
Figure RE-FDA0002791660360000032
for the hidden state of the ith neuron in the network at time t,
Figure RE-FDA0002791660360000033
for the output image of the i-th layer network at time t,
Figure RE-FDA0002791660360000034
the picture is an output picture of the PredRNN network at the time t;
Figure RE-FDA0002791660360000035
Figure RE-FDA0002791660360000036
Figure RE-FDA0002791660360000037
Figure RE-FDA0002791660360000038
Figure RE-FDA0002791660360000039
Figure RE-FDA00027916603600000310
wherein
Figure RE-FDA00027916603600000311
Pictures input to the first layer PredRNN network for time t, gtFor abstract extraction of current and last time input information by PredRNN neural unit, wxgConvolution kernel, w, for abstract information extraction layer on input picture at current timehgA convolution kernel of an image output at the last moment is taken as an abstract information extraction layer; f. oftForgetting gate, w, for PredRN network at time txf，whf，wmfRespectively inputting a picture at the current moment, inputting a picture at the last moment and performing convolution kernel in a network hidden layer state at the last moment for the forgetting gate; i.e. itInput gates for the network at time t, wxi，whi，wmiRespectively inputting a picture at the current moment, inputting a picture at the last moment and performing convolution kernel of a network hidden layer state at the last moment for the input gate; o istBeing output gates of the network, wxo，who，wmoRespectively inputting a picture at the current moment, inputting a picture at the last moment and performing convolution kernel of a network hidden layer state at the last moment for the output gate; symbol indicates convolution operation, symbol indicates dot product operation.
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