JP2017198464A - Image processor and image processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processor and an image processing method capable of detecting a cloud area while identifying snow/ice from cloud.SOLUTION: The image processor includes a snow/ice determination part 6 that calculates a difference Δρ between a reflectance ρ(band) in a visible range and a reflectance ρ(NIR) in a near infrared range, which are estimated by a reflectance estimation section 2 on the ground surface corresponding to the pixels detected by a chromatic determination part 4 or an artifact determination part 5, and detects pixels the calculated difference Δρ of which is a threshold level for difference reflectance determination or less in the pixels which are detected by the chromatic determination part 4 or the artifact determination part 5.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an image processing apparatus and an image processing method for detecting a cloud region in an observation image of the earth.

For example, an observation image of the earth observed by a multispectral sensor or a hyperspectral sensor mounted on a satellite or an aircraft may include a cloud region.
When the observation target exists in the cloud area, the observation target may not be visually recognized.
Therefore, among the multiple observation images obtained from a multispectral sensor, etc., there are a mixture of observation images that allow the observation object to be visually recognized and observation images that cannot observe the observation object. It is necessary to detect the cloud region in the observation image and select the observation image that can visually recognize the observation target.

  Patent Document 1 below discloses an image processing apparatus that detects a cloud region in an observation image using the feature that clouds have high brightness and low saturation.

Japanese Patent Laying-Open No. 2015-64753

  Since the conventional image processing apparatus is configured as described above, it is possible to detect a region having a high possibility of a cloud, but it is not possible to distinguish between snow and ice. For this reason, there has been a problem that snow and ice regions may be erroneously detected as cloud regions.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide an image processing apparatus and an image processing method capable of identifying snow and ice and clouds and detecting a cloud region.

  The image processing apparatus according to the present invention is configured to estimate a reflectance in a visible region and a reflectance in a near-infrared region on the ground surface corresponding to the pixel from pixel values of pixels constituting an observation image of the earth. A first pixel detection for detecting a pixel whose reflectance in the visible range on the corresponding ground surface estimated by the reflectance estimation unit is greater than or equal to a first threshold among the pixels constituting the estimation unit and the observation image The saturation of the pixel is calculated from the reflectance of the visible region on the ground surface corresponding to the pixel detected by the first pixel detection unit, and the pixel detected by the first pixel detection unit The visible region estimated by the second pixel detection unit for detecting pixels whose saturation is equal to or less than the second threshold, and the reflectance estimation unit on the ground surface corresponding to the pixels detected by the second pixel detection unit The difference between the reflectance of the light and the reflectance in the near infrared region is calculated and detected by the second pixel detection unit. Is among the pixels are, the difference is that as and a third pixel detector for detecting a third following pixel threshold.

  According to this invention, the difference between the reflectance in the visible region and the reflectance in the near infrared region estimated by the reflectance estimating unit on the ground surface corresponding to the pixel detected by the second pixel detecting unit is calculated. And the third pixel detection unit that detects a pixel whose difference is equal to or smaller than the third threshold among the pixels detected by the second pixel detection unit. The cloud region can be detected.

1 is a configuration diagram illustrating an image processing apparatus according to Embodiment 1 of the present invention; It is a hardware block diagram of the image processing apparatus by Embodiment 1 of this invention. FIG. 2 is a hardware configuration diagram of a computer when the image processing apparatus is realized by software, firmware, or the like. It is a flowchart which shows the image processing method which is a process sequence in case an image processing apparatus is implement | achieved by software, firmware, etc. It is explanatory drawing which shows an example of the detection pixel mapping produced by the artifact determination part. It is explanatory drawing which shows the spatial filter of 3x3 pixel. It is explanatory drawing which shows the spectral reflection characteristic of a cloud and snow and ice. It is a block diagram which shows the image processing apparatus by Embodiment 2 of this invention. It is a hardware block diagram of the image processing apparatus by Embodiment 2 of this invention. It is explanatory drawing which shows the spectral characteristic of atmospheric scattering radiance. It is explanatory drawing which shows the characteristic of the multiband in the histogram of an observation image. It is a block diagram which shows the image processing apparatus by Embodiment 3 of this invention. It is a hardware block diagram of the image processing apparatus by Embodiment 3 of this invention. It is a block diagram which shows the image processing apparatus by Embodiment 4 of this invention. It is a hardware block diagram of the image processing apparatus by Embodiment 4 of this invention.

  Hereinafter, in order to describe the present invention in more detail, modes for carrying out the present invention will be described with reference to the accompanying drawings.

Embodiment 1 FIG.
FIG. 1 is a block diagram showing an image processing apparatus according to Embodiment 1 of the present invention, and FIG. 2 is a hardware block diagram of the image processing apparatus according to Embodiment 1 of the present invention.
1 and 2, the database 1 is realized by the storage circuit 11 of FIG. 2, for example, and includes a cloud reflectance determination threshold ρ _th (first threshold) and a saturation determination threshold S _th (second ), Differential reflectance determination threshold Δρ _th (third threshold), and filter value determination threshold Fil _th (fourth threshold).
The database 1 includes a radiance conversion coefficient α (band) related to the sensitivity of the brightness value in the sensor observing the observation image of the earth, and a radiance conversion coefficient β (band) related to the offset of the brightness value in the sensor. The solar illuminance E _sun (band), the solar zenith angle θ _sun , the resolution of the observation image, and the like are stored.
The solar illuminance E _sun (band) is the illuminance on the ground surface when the solar zenith angle θ _sun is 0 degree.

The reflectance estimation unit 2 is realized by, for example, the reflectance estimation circuit 12 of FIG. 2, and from the pixel values of the pixels constituting the observation image of the earth, the visible range on the ground surface corresponding to the pixels is calculated. A process of estimating the reflectance ρ (band) and the near-infrared reflectance ρ (NIR) is performed.
The pixel values of the pixels constituting the observation image include a digital value DN (R) indicating a red component and a digital value DN (G) indicating a green component as a digital value DN (band) indicating a pixel value in the visible range. , A digital value DN (B) indicating a blue component is included. In addition, the pixel values of the pixels constituting the observation image include a digital value DN (NIR) indicating a near-infrared pixel value.

The reflectivity determination unit 3 is realized by, for example, the reflectivity determination circuit 13 in FIG. 2, and is visible on the corresponding ground surface estimated by the reflectivity estimation unit 2 among the pixels constituting the observation image. This is a first pixel detection unit that detects pixels whose area reflectance ρ (band) is greater than or equal to the cloud reflectance determination threshold ρ _th stored in the database 1.
The saturation determination unit 4 is realized by, for example, the saturation determination circuit 14 in FIG. 2, and the reflectance determination unit 3 out of the reflectance ρ (band) in the visible range estimated by the reflectance estimation unit 2. A process of calculating the saturation s of the pixel from the reflectance ρ (band) of the visible range on the ground surface corresponding to the detected pixel is performed.
In addition, the saturation determination unit 4 detects a pixel whose calculated saturation S is equal to or less than the saturation determination threshold S _th stored in the database 1 among the pixels detected by the reflectance determination unit 3. It is a pixel detection unit.

The artifact determination unit 5 is realized by, for example, the artifact determination circuit 15 in FIG. 2, and performs spatial filtering on the distribution of pixels detected by the saturation determination unit 4 using a spatial filter.
Further, the artifact determination unit 5 detects a pixel whose output value of the spatial filter is greater than or equal to the filter value determination threshold Fil _th stored in the database 1 among the pixels detected by the saturation determination unit 4. It is a pixel detection part.
In the first embodiment, the artifact determination unit 5 is provided before the snow / ice determination unit 6, but may be provided after the snow / ice determination unit 6. Further, it is not necessary to mount the artifact determination unit 5 in a situation where artifacts or the like with high brightness and low saturation are not included in the observation image.

The snow / ice determining unit 6 is realized by, for example, the snow / ice determining circuit 16 of FIG. 2, and the visible region reflectance ρ (band) and the near infrared region reflectance ρ (NIR) estimated by the reflectance estimating unit 2. Among them, the reflectance ρ (band) of the visible region on the ground surface corresponding to the pixel detected by the artifact determination unit 5 and the reflectance ρ (NIR) of the near infrared region are acquired, and the visible region A process of calculating a difference Δρ between the reflectance ρ (band) and the near-infrared reflectance ρ (NIR) is performed.
The snow and ice determination unit 6 is a third pixel that detects a pixel having a calculated difference Δρ that is less than or equal to the threshold Δρ _th for differential reflectance stored in the database 1 among the pixels detected by the artifact determination unit 5. It is a detection unit.
Note that the snow and ice determination unit 6 sets the pixel detected by the saturation determination unit 4 as a processing target when the artifact determination unit 5 is not mounted.

In FIG. 1, the database 1, the reflectance estimation unit 2, the reflectance determination unit 3, the saturation determination unit 4, the artifact determination unit 5, and the snow / ice determination unit 6, which are components of the image processing apparatus, are illustrated in FIG. 2. Assuming that the hardware shown in FIG. 1 is realized by a storage circuit 11, a reflectance estimation circuit 12, a reflectance determination circuit 13, a saturation determination circuit 14, an artifact determination circuit 15, and a snow / ice determination circuit 16. ing.
Here, the memory circuit 11 may be, for example, a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory Memory, or an EEPROM (Electrically Erasable Memory). A volatile semiconductor memory, a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD (Digital Versatile Disc), and the like are applicable.
Further, the reflectance estimation circuit 12, the reflectance determination circuit 13, the saturation determination circuit 14, the artifact determination circuit 15 and the snow and ice determination circuit 16 are, for example, a single circuit, a composite circuit, a programmed processor, and a parallel program. A processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof is applicable.

The constituent elements of the image processing apparatus are not limited to those realized by dedicated hardware, and the image processing apparatus may be realized by software, firmware, or a combination of software and firmware. .
Software and firmware are stored as programs in the memory of the computer. The computer means hardware that executes a program, and includes, for example, a CPU (Central Processing Unit), a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, a processor, a DSP (Digital Signal Processor), and the like. .

FIG. 3 is a hardware configuration diagram of a computer when the image processing apparatus is realized by software, firmware, or the like.
When the image processing apparatus is realized by software, firmware, or the like, the database 1 is configured on the memory 21 of the computer, and the reflectance estimation unit 2, the reflectance determination unit 3, the saturation determination unit 4, and the artifact determination unit 5 A program for causing the computer to execute the processing procedure of the snow / ice determination unit 6 may be stored in the memory 21, and the processor 22 of the computer may execute the program stored in the memory 21.
FIG. 4 is a flowchart showing an image processing method as a processing procedure when the image processing apparatus is realized by software, firmware, or the like.
2 shows an example in which each component of the image processing apparatus is realized by dedicated hardware, and FIG. 3 shows an example in which the image processing apparatus is realized by software, firmware, etc. Some components in the processing device may be realized by dedicated hardware, and the remaining components may be realized by software, firmware, or the like.

Next, the operation will be described.
The reflectivity estimation unit 2 acquires an observation image of the earth observed by, for example, a multispectral sensor or a hyperspectral sensor mounted on a satellite or an aircraft.
The pixel values of the pixels constituting the observation image include a digital value DN (R) indicating a red component and a digital value DN (G) indicating a green component as a digital value DN (band) indicating a pixel value in the visible range. , A digital value DN (B) indicating a blue component is included. In addition, the pixel values of the pixels constituting the observation image include a digital value DN (NIR) indicating a near-infrared pixel value.
Further, the reflectance estimation unit 2 obtains a cloud reflectance determination threshold ρ _th , radiance conversion coefficients α (band), β (band), solar illuminance E _sun (band), and solar zenith angle θ _sun from the database 1. To do.

When the reflectance estimation unit 2 acquires the observation image of the earth, the reflectance ρ (band) of the visible region on the ground surface corresponding to the pixel and the near red color are obtained from the pixel values of the pixels constituting the observation image. The reflectance ρ (NIR) of the outer region is estimated (step ST1 in FIG. 4).
The reflectance ρ (band) in the visible range on the ground surface is expressed by the following equation (1).

In Expression (1), ρ (R), ρ (G), and ρ (B) are expressed as the following Expressions (2) to (4).

In the equations (2) to (4), α (R) is the radiance conversion coefficient of the red component included in the radiance conversion coefficient α (band), and α (G) is the radiance conversion coefficient α (band). The green component radiance conversion coefficient, α (B), is the blue component radiance conversion coefficient included in the radiance conversion coefficient α (band).
β (R) is the radiance conversion coefficient of the red component included in the radiance conversion coefficient β (band), and β (G) is the radiance conversion of the green component included in the radiance conversion coefficient β (band). The coefficient β (B) is a radiance conversion coefficient of the blue component included in the radiance conversion coefficient β (band).
E _sun (R) is the solar illuminance of the red component contained in the solar illuminance E _sun (band), E _sun (G) is the solar illuminance of the green component contained in the solar illuminance E _sun (band), E _sun (B) is the solar illuminance of the blue component contained in the solar illuminance E _sun (band).
The solar zenith angle θ _sun is the zenith angle at the time when the image is observed by the sensor.

式（５）において、α（ＮＩＲ）は放射輝度変換係数α（ｂａｎｄ）に含まれている近赤外域の放射輝度変換係数、β（ＮＩＲ）は放射輝度変換係数β（ｂａｎｄ）に含まれている近赤外域の放射輝度変換係数である。
また、Ｅ_ｓｕｎ（ＮＩＲ）は太陽照度Ｅ_ｓｕｎ（ｂａｎｄ）に含まれている近赤外域の太陽照度である。 The reflectance ρ (NIR) in the near-infrared region on the ground surface is expressed as the following formula (5).

In equation (5), α (NIR) is included in the radiance conversion coefficient α (band) and the near-infrared radiance conversion coefficient β (NIR) is included in the radiance conversion coefficient β (band). This is the radiance conversion coefficient in the near infrared region.
E _sun (NIR) is solar illuminance in the near-infrared region included in solar illuminance E _sun (band).

In this Embodiment 1, although what estimates the reflectance (rho) (band) of visible region is shown by Formula (1)-(4), as shown to the following formula (6), it is shown in an observation image. The reflectance ρ (band) in the visible range may be estimated using the radiance conversion coefficients α ′ (band) and β ′ (band) of the known subject being shown.
The radiance conversion coefficients α ′ (band) and β ′ (band) of the known subject can be obtained by measuring the subject with a spectroscope, for example.

In the reflectance determination unit 3 installed in the subsequent stage of the reflectance estimation unit 2, using the reflectance ρ (band) of the visible region on the ground surface estimated by the reflectance estimation unit 2, as described later, Pixels with high brightness that may be clouds are detected, but by using the reflectance ρ (band) of the visible region on the ground surface instead of the pixel values of the pixels that make up the observed image, the solar zenith It is possible to reduce the influence of the luminance change caused by the angle θ _sun and the luminance change caused by the change of the sensitivity and offset of the sensor.

The reflectance determination unit 3 acquires the reflectance ρ (band) in the visible range estimated by the reflectance estimation unit 2 and the cloud reflectance determination threshold ρ _th stored in the database 1.
The reflectance determination unit 3 stores the reflectance ρ (band) of the visible region on the corresponding ground surface estimated by the reflectance estimation unit 2 among the pixels constituting the observation image in the database 1. A pixel having a cloud reflectance determination threshold value ρ _th or more is detected (step ST2).
That is, the reflectance determination unit 3 is visible on the ground surface corresponding to the pixel estimated by the reflectance estimation unit 2 as shown in the following equation (7) for each pixel constituting the observation image. The reflectance ρ (band) of the area is compared with the cloud reflectance determination threshold value ρ _th stored in the database 1, and the reflectance ρ (band) of the visible area is equal to or greater than the threshold value ρ _th for cloud reflectance determination. Detect a pixel.

In Expression (7), it is assumed that the threshold value ρ _th for cloud reflectance determination is set to the minimum luminance value in the reflectance in the case of a cloud.
Specifically, when the observation image is observed, if it is considered that there is no influence of atmospheric scattering, for example, a value of about 0.8 can be used as the cloud reflectance determination threshold ρ _th . When it is considered that there is an influence of atmospheric scattering, for example, a value of about 0.6 can be used as the cloud reflectance determination threshold ρ _th . Therefore, the cloud reflectance determination threshold ρ _th is appropriately set in consideration of the influence of atmospheric scattering at the time of design.
Thereby, a pixel with high possibility of a cloud can be detected from an observation image. However, at this stage, there is a possibility that a pixel relating to an artificial object or ice / snow, which is a highly reflective subject similar to a cloud, may be detected.

式（８）において、ｍａｘ｛ρ（Ｒ），ρ（Ｇ），ρ（Ｂ）｝は、ρ（Ｒ），ρ（Ｇ），ρ（Ｂ）の中で最も大きいものを選択することを意味する。
また、ｍｉｎ｛ρ（Ｒ），ρ（Ｇ），ρ（Ｂ）｝は、ρ（Ｒ），ρ（Ｇ），ρ（Ｂ）の中で最も小さいものを選択することを意味する。 The saturation determination unit 4 has a visible region reflectance ρ (band) on the ground surface corresponding to the pixel detected by the reflectance estimation unit 2 through the reflectance determination unit 3, that is, a visible region reflectance ρ (R). ), Ρ (G), ρ (B). Further, the saturation determination threshold value S _th stored in the database 1 is acquired.
As shown in the following equation (8), the saturation determination unit 4 has the reflectance ρ (R), ρ (G), ρ in the visible range on the ground surface corresponding to the pixel detected by the reflectance determination unit 3. From (B), the saturation S of the pixel is calculated.

In Expression (8), max {ρ (R), ρ (G), ρ (B)} is selected from among ρ (R), ρ (G), and ρ (B). means.
Further, min {ρ (R), ρ (G), ρ (B)} means that the smallest one among ρ (R), ρ (G), and ρ (B) is selected.

雲の色は白であり、彩度は極めて小さいことが想定されるため、彩度判定用閾値Ｓ_ｔｈとして、例えば、０．１程度の値を用いることができる。
これにより、観測画像から雲の可能性が高い高輝度かつ低彩度な画素を検出することができる。ただし、この段階では、雲と同程度の高反射かつ低彩度な被写体である人工物や氷雪についての画素も検出される可能性がある。 When the saturation determination unit 4 calculates the saturation S of each pixel detected by the reflectance determination unit 3, the calculated saturation S among the pixels detected by the reflectance determination unit 3 is stored in the database 1. Pixels that are less than or equal to the saturation determination threshold _Sth are detected (step ST3).
That is, for each pixel detected by the reflectance determination unit 3, the saturation determination unit 4 determines the saturation S of the pixel and the saturation determination stored in the database 1 as shown in the following equation (9). The threshold value S _th is compared, and pixels whose saturation S is equal to or less than the saturation determination threshold value S _th are detected.

Since the color of the cloud is white and the saturation is assumed to be extremely small, for example, a value of about 0.1 can be used as the saturation determination threshold _Sth .
As a result, it is possible to detect pixels with high luminance and low saturation that are highly likely to be clouds from the observed image. However, at this stage, there is a possibility that a pixel relating to an artificial object or ice / snow that is a highly reflective and low-saturation subject similar to a cloud may be detected.

The artifact determination unit 5 acquires the pixels detected by the saturation determination unit 4, the filter value determination threshold Fil _th stored in the database 1, and the resolution of the observation image.
For example, as shown in FIG. 5, the artifact determination unit 5 inputs a value of “1” to the pixel position detected by the saturation determination unit 4 as the detected pixel mapping of the observation image, and determines the saturation determination. A detected pixel mapping in which a value of “0” is input at the position of the pixel not detected by the unit 4 is created.
FIG. 5 is an explanatory diagram illustrating an example of the detected pixel mapping created by the artifact determination unit 5, and the resolution of the detected pixel mapping is the same as the resolution of the observed image.

After creating the detected pixel mapping, the artifact determination unit 5 performs spatial filtering on the pixel distribution detected by the saturation determination unit 4 using a spatial filter (step ST4 in FIG. 4).
That is, the artifact determination unit 5 sets each pixel in the detected pixel mapping as a target pixel in order, and calculates the filter value of the target pixel from the value of the target pixel and the values of peripheral pixels existing around the target pixel. Calculate Fil.
The following processing can be considered as spatial filtering for the distribution of pixels detected by the saturation determination unit 4.
For example, assuming that the spatial filter is a 3 × 3 pixel filter as shown in FIG. 6, the central pixel in the 3 × 3 pixel filter is the target pixel, and the eight pixels existing around the target pixel are the peripheral Assuming that the pixel is a pixel, as shown in the following equation (10), the average value of the value P _att of one _target pixel and the value P _aro (i) of eight peripheral pixels is set as the filter of the _target pixel. A process for calculating the value Fil can be considered.

The cloud region generally has a large region, and the artifact region is generally smaller than the cloud region.
Therefore, in the case of a cloud region, more than half of the nine pixels in the detection pixel mapping generally existing at the position corresponding to the 3 × 3 pixel filter are “1”. Is assumed.
On the other hand, in the case of an artifact region, in general, among nine pixels in the detection pixel mapping existing at a position corresponding to a 3 × 3 pixel filter, there are 1, 2 pixels of “1”. It is assumed that However, in the case of a large artifact, there may be three or more “1” pixels.
For example, if the number of “1” pixels is 8 out of 9 pixels in the detection pixel mapping, the filter value Fil of the target pixel calculated by Expression (10) is about 0.89.
If the number of “1” pixels is three, the filter value Fil of the target pixel calculated by Expression (10) is about 0.33.
Therefore, as the filter value determination threshold value _{Fil th,} for example, it may be a value such as 0.4 or 0.5.

Here, a spatial filter using a 3 × 3 pixel filter is shown, but the filter size is not limited to 3 × 3 pixels, and a spatial filter of any size can be used.
Also, here, an example is shown in which a spatial filter that simply calculates an average value of nine pixel values is used, but a moving average filter that calculates a moving average, a Gaussian filter that gives weight, and the like are used. May be.

これにより、雲より小さく、高輝度かつ低彩度な被写体である人工物や、ガラスなどによって正反射された太陽光の輝点を除外することができる。 When the artifact determination unit 5 performs the above spatial filtering using each pixel in the detected pixel mapping as a target pixel, a filter in which the output value of the spatial filter is stored in the database 1 among the respective pixels in the detected pixel mapping. detecting a threshold _{Fil th} or more pixels for a value determined (step ST5 in FIG. 4).
That is, for each pixel in the detection pixel mapping, the artifact determination unit 5 determines the filter value Fil of the target pixel that is the output value of the spatial filter for the pixel, and the database 1 as shown in the following equation (11). stored compares the filter value determination threshold Fil _th are filtered value Fil detects a threshold Fil _th or more pixels for determination filter value.

As a result, it is possible to exclude artifacts that are smaller than clouds, high-luminance and low-saturation subjects, and bright spots of sunlight that are regularly reflected by glass or the like.

The snow / ice determination unit 6 acquires the reflectance ρ (band) in the visible region and the reflectance ρ (NIR) in the near infrared region corresponding to the pixel detected by the artifact determination unit 5 from the reflectance estimation unit 2. In addition, the threshold value Δρ _th for differential reflectance determination stored in the database 1 is acquired.
Here, FIG. 7 is an explanatory diagram showing spectral reflection characteristics of clouds and snow and ice.
In FIG. 7, the horizontal axis indicates the wavelength and the vertical axis indicates the reflectance.
In the visible range, both clouds and snow and ice have high reflectivity, and the reflectivity is almost constant.
On the other hand, in the near infrared region, the reflectance of snow and ice gradually decreases, but the reflectance of the cloud does not substantially decrease to the short wavelength infrared region.
Therefore, if the reflectance in the near infrared region is reduced relative to the reflectance in the visible region, it is judged as snow and ice, and the reflectance in the near infrared region is almost reduced compared to the reflectance in the visible region. If not, it can be determined to be a cloud.

雪氷判定部６は、人工物判定部５により検出された画素毎に差分Δρを算出すると、人工物判定部５により検出された画素のうち、その差分Δρがデータベース１に記憶されている差分反射率判定用閾値Δρ_ｔｈ以下の画素を検出する（図４のステップＳＴ６）。
即ち、雪氷判定部６は、人工物判定部５により検出された画素毎に、下記の式（１３）に示すように、当該画素についての差分Δρと差分反射率判定用閾値Δρ_ｔｈを比較し、差分Δρが差分反射率判定用閾値Δρ_ｔｈ以下の画素を検出する。

これにより、雪氷を除去して、雲を検出することができる。
雪氷判定部６は、差分Δρが差分反射率判定用閾値Δρ_ｔｈ以下の画素から構成される画像を雲分布画像として出力する。 For each pixel detected by the artifact determination unit 5, the snow / ice determination unit 6 approximates the reflectance ρ (band) of the visible region on the ground surface corresponding to the pixel as shown in the following equation (12). A difference Δρ from the infrared reflectance ρ (NIR) is calculated.

When the snow / ice determination unit 6 calculates the difference Δρ for each pixel detected by the artifact determination unit 5, the difference reflection Δρ stored in the database 1 among the pixels detected by the artifact determination unit 5. Pixels that are equal to or lower than the threshold value Δρ _th for rate determination are detected (step ST6 in FIG. 4).
That is, for each pixel detected by the artifact determination unit 5, the snow / ice determination unit 6 compares the difference Δρ and the difference reflectance determination threshold Δρ _th for the pixel as shown in the following equation (13). , A pixel having a difference Δρ that is equal to or smaller than a differential reflectance determination threshold Δρ _th is detected.

Thereby, snow and ice can be removed and a cloud can be detected.
The snow and ice determination unit 6 outputs an image composed of pixels having a difference Δρ that is equal to or smaller than the difference reflectance determination threshold Δρ _th as a cloud distribution image.

In the first embodiment, as the reflectance ρ (band) in the visible region, for example, the reflectance at a wavelength of 600 to 700 nm, and as the reflectance ρ (NIR) in the near infrared region, for example, the reflectance at a wavelength of 1000 to 2000 nm is used. Although it is assumed to be used, it is possible to arbitrarily determine which wavelength reflectance to use at the design stage.
Further, in the case of a cloud, the reflectance in the near infrared region hardly decreases with respect to the reflectance in the visible region, and therefore, for example, a value such as 0.1 or 0.2 is set as the differential reflectance determination threshold Δρ _th. Can be used.

As apparent from the above, according to the first embodiment, the reflectance ρ (band in the visible range estimated by the reflectance estimation unit 2 on the ground surface corresponding to the pixel detected by the artifact determination unit 5 ) And the near-infrared reflectance ρ (NIR), and the difference Δρ calculated from the pixels detected by the artifact determination unit 5 is stored in the database 1 for differential reflectance determination. Since it is configured to include the snow / ice determining unit 6 that detects pixels equal to or smaller than the threshold Δρ _{th, the} effect of being able to distinguish between snow and ice and detecting a cloud region can be achieved.

Further, according to the first embodiment, spatial filtering is performed on the distribution of pixels detected by the saturation determination unit 4 using a spatial filter, and among the pixels detected by the saturation determination unit 4, Since the output value of the spatial filter is configured to include the artifact determination unit 5 that detects pixels equal to or greater than the filter value determination threshold Fil _th stored in the database 1, the artifact and the cloud are identified, There is an effect that a region can be detected.

Embodiment 2. FIG.
In the first embodiment, the observation image is input to the reflectance estimation unit 2, but the light reflected on the ground surface reaches a multispectral sensor or a hyperspectral sensor mounted on a satellite or the like. In addition, the observation image input to the reflectance estimation unit 2 may be deteriorated due to the influence of atmospheric attenuation and atmospheric scattering.
Therefore, in the second embodiment, a description will be given of correcting an observation image input to the reflectance estimation unit 2.

FIG. 8 is a block diagram showing an image processing apparatus according to Embodiment 2 of the present invention, and FIG. 9 is a hardware configuration diagram of the image processing apparatus according to Embodiment 2 of the present invention.
8 and FIG. 9, the same reference numerals as those in FIG. 1 and FIG.
The image correction unit 7 is realized by, for example, the image correction circuit 17 in FIG. 9, analyzes the histogram of each wavelength band in the input observation image, specifies the offset level of each wavelength band, and determines each wavelength band. The observation image is corrected in accordance with the offset level, and the corrected observation image is output to the reflectance estimation unit 2.

In FIG. 8, each of the database 1, the reflectance estimation unit 2, the reflectance determination unit 3, the saturation determination unit 4, the artifact determination unit 5, the snow / ice determination unit 6, and the image correction unit 7, which are components of the image processing apparatus. 9 are dedicated hardware such as the storage circuit 11, the reflectance estimation circuit 12, the reflectance determination circuit 13, the saturation determination circuit 14, the artifact determination circuit 15, the snow and ice determination circuit 16, and the image correction. The one realized by the circuit 17 is assumed.
The reflectance estimation circuit 12, the reflectance determination circuit 13, the saturation determination circuit 14, the artifact determination circuit 15, the snow and ice determination circuit 16, and the image correction circuit 17 are, for example, a single circuit, a composite circuit, a programmed processor, a parallel processor A programmed processor, ASIC, FPGA, or a combination of these is applicable.

However, the constituent elements of the image processing apparatus are not limited to those realized by dedicated hardware, and the image processing apparatus may be realized by software, firmware, or a combination of software and firmware. .
When the image processing apparatus is realized by software, firmware, or the like, the database 1 is configured on the memory 21 of the computer, and the reflectance estimation unit 2, the reflectance determination unit 3, the saturation determination unit 4, and the artifact determination unit 5 A program for causing the computer to execute the processing procedure of the snow / ice determination unit 6 and the image correction unit 7 is stored in the memory 21 of the computer shown in FIG. 3, and the processor 22 of the computer executes the program stored in the memory 21. What should I do?

Next, the operation will be described.
Other than the image correction unit 7 is the same as in the first embodiment, and therefore, the processing content of the image correction unit 7 will be described here.
FIG. 10 is an explanatory diagram showing the spectral characteristic of atmospheric scattering radiance, and FIG. 11 is an explanatory diagram showing the multiband characteristic in the histogram of the observed image.
From the visible range to the near infrared range, as shown in FIG. 10, the shorter the wavelength, the more susceptible to atmospheric scattering.
For this reason, when the histogram analysis is performed on the pixel values of the pixels constituting the observation image, for example, the B band which is a short wavelength side band is a long wavelength side band as shown in FIG. There is a tendency that the offset level becomes larger than that of the NIR band.

The image correction unit 7 analyzes the histogram of each wavelength band in the input observation image and specifies the offset level of each wavelength band.
As shown in FIG. 11, the offset level of each wavelength band is the minimum value of the image output value that is a component of each wavelength band.
In the second embodiment, the offset level of the R band, the offset level of the G band, the offset level of the B band, and the offset level of the NIR band are specified.

After specifying the offset level of each wavelength band, the image correction unit 7 subtracts the offset level of the R band from the digital value DN (R) that is the pixel value of the pixel for each pixel constituting the observation image, The G band offset level is subtracted from the G value digital value DN (G), and the B band offset level is subtracted from the B value digital value DN (B). Further, the offset level of the NIR band is subtracted from the digital value DN (NIR) in the near infrared region.
By thus subtracting the offset level, the observed image is corrected, and the corrected observed image is output from the image correcting unit 7 to the reflectance estimating unit 2.
Here, the image correction unit 7 analyzes the histogram of each wavelength band in the observed image and specifies the offset level of each wavelength band. However, the spectral characteristics of the atmospheric scattering radiance as shown in FIG. Is stored in the database 1, the offset level of each wavelength band may be estimated by regression analysis while referring to the spectral characteristics of atmospheric scattering radiance.

  As apparent from the above, according to the second embodiment, the offset level of the pixel value in each wavelength band of the observation image is specified, and the observation image is corrected in accordance with the offset level of the pixel value in each wavelength band. Since the image correction unit 7 that outputs the subsequent observation image to the reflectance estimation unit 2 is provided, the effect of atmospheric attenuation and atmospheric scattering can be reduced, and the cloud detection accuracy can be increased.

Embodiment 3 FIG.
In the first and second embodiments, the snow / ice determination unit 6 outputs an image composed of pixels having the difference Δρ equal to or less than the difference reflectance determination threshold Δρ _th as a cloud distribution image. In Embodiment 3, a description will be given of a case where the cloud distribution image is down-sampled to generate a thumbnail image that is a reduced image of the cloud distribution image.

12 is a block diagram showing an image processing apparatus according to Embodiment 3 of the present invention, and FIG. 13 is a hardware block diagram of the image processing apparatus according to Embodiment 3 of the present invention.
12 and 13, the same reference numerals as those in FIGS. 1, 2, 8, and 9 indicate the same or corresponding parts, and thus description thereof is omitted.
The reduced image generation unit 8 is realized by, for example, the reduced image generation circuit 18 of FIG. 13, and generates a thumbnail image that is a reduced image of a cloud distribution image having pixels detected by the snow and ice determination unit 6, and the thumbnails thereof. A process for outputting an image is performed.

In FIG. 12, the database 1, the reflectance estimation unit 2, the reflectance determination unit 3, the saturation determination unit 4, the artifact determination unit 5, the snow / ice determination unit 6, the image correction unit 7 and the reduction, which are constituent elements of the image processing apparatus. Each of the image generation units 8 includes dedicated hardware as shown in FIG. 13, that is, a storage circuit 11, a reflectance estimation circuit 12, a reflectance determination circuit 13, a saturation determination circuit 14, an artifact determination circuit 15, snow and ice It is assumed that the determination circuit 16, the image correction circuit 17, and the reduced image generation circuit 18 are realized.
The reflectance estimation circuit 12, the reflectance determination circuit 13, the saturation determination circuit 14, the artifact determination circuit 15, the snow and ice determination circuit 16, the image correction circuit 17, and the reduced image generation circuit 18 are, for example, a single circuit, a composite circuit, A programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof is applicable.

However, the constituent elements of the image processing apparatus are not limited to those realized by dedicated hardware, and the image processing apparatus may be realized by software, firmware, or a combination of software and firmware. .
When the image processing apparatus is realized by software, firmware, or the like, the database 1 is configured on the memory 21 of the computer, and the reflectance estimation unit 2, the reflectance determination unit 3, the saturation determination unit 4, and the artifact determination unit 5 A program for causing the computer to execute the processing procedures of the snow / ice determination unit 6, the image correction unit 7 and the reduced image generation unit 8 is stored in the memory 21 of the computer shown in FIG. 3, and the processor 22 of the computer is stored in the memory 21. The program that is running should be executed.

Next, the operation will be described.
Except for the reduced image generation unit 8, the processing is the same as in the first and second embodiments. Therefore, the processing content of the reduced image generation unit 8 will be described here.
Upon receiving the cloud distribution image from the snow / ice determination unit 6, the reduced image generation unit 8 downsamples the cloud distribution image to generate a thumbnail image that is a reduced image of the cloud distribution image, and outputs the thumbnail image. To do.
Since the image downsampling process itself is a known technique, a detailed description thereof is omitted. For example, the image downsampling can be performed by using a kernel of N × N pixels.
Thereby, the data capacity of the cloud distribution image can be reduced.

  As apparent from the above, according to the third embodiment, the reduced image generation unit 8 that generates the reduced image of the cloud distribution image having the pixels detected by the snow and ice determination unit 6 is provided. There is an effect that the capacity of data can be reduced.

Embodiment 4 FIG.
In the first and second embodiments, the snow / ice determination unit 6 outputs an image composed of pixels having the difference Δρ equal to or less than the difference reflectance determination threshold Δρ _th as a cloud distribution image. In the fourth embodiment, a description will be given of what outputs a cloud amount.

FIG. 14 is a block diagram showing an image processing apparatus according to Embodiment 4 of the present invention, and FIG. 15 is a hardware block diagram of the image processing apparatus according to Embodiment 4 of the present invention.
14 and 15, the same reference numerals as those in FIGS.
The cloud amount calculation unit 9 is realized by, for example, the cloud amount calculation circuit 19 in FIG. 15, and calculates the ratio of the number of pixels detected by the snow / ice determination unit 6 to the number of pixels constituting the observation image as the cloud amount. Perform the process.

In FIG. 14, the database 1, the reflectance estimation unit 2, the reflectance determination unit 3, the saturation determination unit 4, the artifact determination unit 5, the snow / ice determination unit 6, the image correction unit 7, and the reduction component, which are components of the image processing apparatus. Each of the image generation unit 8 and the cloud amount calculation unit 9 includes dedicated hardware as illustrated in FIG. 15, that is, a storage circuit 11, a reflectance estimation circuit 12, a reflectance determination circuit 13, a saturation determination circuit 14, and an artifact. It is assumed that the determination circuit 15, the snow / ice determination circuit 16, the image correction circuit 17, the reduced image generation circuit 18, and the cloud amount calculation circuit 19 are realized.
The reflectance estimation circuit 12, the reflectance determination circuit 13, the saturation determination circuit 14, the artifact determination circuit 15, the snow and ice determination circuit 16, the image correction circuit 17, the reduced image generation circuit 18, and the cloud amount calculation circuit 19 are, for example, a single unit. A circuit, a composite circuit, a programmed processor, a processor programmed in parallel, an ASIC, an FPGA, or a combination thereof is applicable.

However, the constituent elements of the image processing apparatus are not limited to those realized by dedicated hardware, and the image processing apparatus may be realized by software, firmware, or a combination of software and firmware. .
When the image processing apparatus is realized by software, firmware, or the like, the database 1 is configured on the memory 21 of the computer, and the reflectance estimation unit 2, the reflectance determination unit 3, the saturation determination unit 4, and the artifact determination unit 5 A program for causing the computer to execute the processing procedures of the snow / ice determination unit 6, the image correction unit 7, the reduced image generation unit 8 and the cloud amount calculation unit 9 is stored in the memory 21 of the computer shown in FIG. A program stored in the memory 21 may be executed.

Next, the operation will be described.
Since the components other than the cloud amount calculation unit 9 are the same as those in the first, second, and third embodiments, processing contents of the cloud amount calculation unit 9 will be described here.
The cloud amount calculation unit 9 identifies the number C _all of the pixels constituting the observation image from the resolution of the observation image stored in the database 1 and counts the number C _det of pixels detected by the snow and ice determination unit 6. To do.
Then, as shown in the following formula (14), the cloud amount calculation unit 9 calculates the ratio R of the number C _det of pixels detected as a cloud distribution out of the number C _all of the pixels constituting the observation image. And the cloud amount is output.

  As is apparent from the above, according to the fourth embodiment, the cloud amount calculation unit 9 that calculates the ratio of the number of pixels detected by the snow and ice determination unit 6 to the number of pixels constituting the observation image as the cloud amount is provided. Since it comprised so that it might provide, there exists an effect which can provide the information of a cloud cover.

  In the present invention, within the scope of the invention, any combination of the embodiments, or any modification of any component in each embodiment, or omission of any component in each embodiment is possible. .

  DESCRIPTION OF SYMBOLS 1 Database, 2 Reflectance estimation part, 3 Reflectivity determination part (1st pixel detection part), 4 Saturation determination part (2nd pixel detection part), 5 Artifact determination part (4th pixel detection part) , 6 Snow / ice determination unit (third pixel detection unit), 7 Image correction unit, 8 Reduced image generation unit, 9 Cloud amount calculation unit, 11 Storage circuit, 12 Reflectance estimation circuit, 13 Reflectance determination circuit, 14 Saturation determination Circuit, 15 Artifact determination circuit, 16 Snow and ice determination circuit, 17 Image correction circuit, 18 Reduced image generation circuit, 19 Cloud amount calculation circuit, 21 Memory, 22 Processor.

Claims (6)

A reflectance estimation unit that estimates the reflectance in the visible region and the reflectance in the near-infrared region on the ground surface corresponding to the pixel from the pixel values of the pixels constituting the observation image of the earth,
A first pixel detection unit that detects a pixel whose reflectance in the visible range on the corresponding ground surface estimated by the reflectance estimation unit is greater than or equal to a first threshold among the pixels constituting the observation image When,
From the visible area reflectance corresponding to the pixel detected by the first pixel detection unit, the saturation of the pixel is calculated, and among the pixels detected by the first pixel detection unit, A second pixel detection unit for detecting pixels whose saturation is equal to or less than a second threshold;
Calculating a difference between the reflectance in the visible region and the reflectance in the near infrared region estimated by the reflectance estimating unit on the ground surface corresponding to the pixel detected by the second pixel detecting unit; An image processing apparatus comprising: a third pixel detection unit that detects a pixel having the difference equal to or smaller than a third threshold among the pixels detected by the pixel detection unit.   Spatial filtering is performed on the distribution of pixels detected by the second pixel detection unit or the third pixel detection unit using a spatial filter, and the second pixel detection unit or the third pixel detection unit The image processing apparatus according to claim 1, further comprising: a fourth pixel detection unit configured to detect a pixel having an output value of the spatial filter equal to or greater than a fourth threshold among the pixels detected by the step. Specifying an offset level of the pixel value in each wavelength band of the observation image, and comprising an image correction unit for correcting the observation image according to the offset level of the pixel value in each wavelength band,
The image processing apparatus according to claim 1, wherein the observation image corrected by the image correction unit is provided to the reflectance estimation unit.   The reduced image generation part which produces | generates the reduced image of the cloud distribution image which has the pixel detected by the said 3rd pixel detection part, The any one of Claims 1-3 characterized by the above-mentioned. Image processing apparatus.   The cloud amount calculation unit for calculating, as a cloud amount, a ratio of the number of pixels detected by the third pixel detection unit to the number of pixels constituting the observation image. 5. The image processing apparatus according to claim 1. The reflectance estimation unit estimates the reflectance in the visible region and the reflectance in the near infrared region on the ground surface corresponding to the pixel from the pixel value of the pixel constituting the observation image of the earth,
Among the pixels constituting the observation image, the first pixel detection unit is a pixel whose reflectance in the visible region on the corresponding ground surface estimated by the reflectance estimation unit is greater than or equal to a first threshold value. Detect
The second pixel detection unit calculates the saturation of the pixel from the visible region reflectance corresponding to the pixel detected by the first pixel detection unit, and the first pixel detection unit Detecting pixels whose saturation is equal to or lower than the second threshold among the pixels detected by
The third pixel detection unit is configured to calculate the reflectance in the visible region and the reflectance in the near infrared region estimated by the reflectance estimation unit on the ground surface corresponding to the pixel detected by the second pixel detection unit. An image processing method for calculating a difference and detecting a pixel having the difference equal to or smaller than a third threshold among the pixels detected by the second pixel detection unit.

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