CN114862778B - Digital soil morphology measurement method based on ImagePy - Google Patents
Digital soil morphology measurement method based on ImagePy Download PDFInfo
- Publication number
- CN114862778B CN114862778B CN202210439257.1A CN202210439257A CN114862778B CN 114862778 B CN114862778 B CN 114862778B CN 202210439257 A CN202210439257 A CN 202210439257A CN 114862778 B CN114862778 B CN 114862778B
- Authority
- CN
- China
- Prior art keywords
- image
- gravel
- soil
- digital
- imagepy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002689 soil Substances 0.000 title claims abstract description 63
- 238000000691 measurement method Methods 0.000 title claims abstract description 13
- 238000000034 method Methods 0.000 claims abstract description 31
- 238000003709 image segmentation Methods 0.000 claims abstract description 11
- 238000006243 chemical reaction Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 7
- 230000004927 fusion Effects 0.000 claims abstract description 6
- 238000012216 screening Methods 0.000 claims abstract description 6
- 238000011002 quantification Methods 0.000 claims abstract description 5
- 230000009467 reduction Effects 0.000 claims abstract description 4
- 230000008569 process Effects 0.000 claims description 15
- 239000002245 particle Substances 0.000 claims description 12
- 230000004075 alteration Effects 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 5
- 239000004575 stone Substances 0.000 claims description 5
- 238000005520 cutting process Methods 0.000 claims description 4
- 239000000126 substance Substances 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 238000004140 cleaning Methods 0.000 claims description 2
- 230000002708 enhancing effect Effects 0.000 claims description 2
- 239000000463 material Substances 0.000 abstract description 2
- 230000011218 segmentation Effects 0.000 description 9
- 239000011159 matrix material Substances 0.000 description 5
- 239000003086 colorant Substances 0.000 description 4
- 238000012937 correction Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000011435 rock Substances 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000005251 gamma ray Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 230000002285 radioactive effect Effects 0.000 description 1
- 230000011514 reflex Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- 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
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- 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
- G06T2207/10024—Color 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
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20132—Image cropping
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/30—Assessment of water resources
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a digital soil morphology measurement method based on ImagePy, which aims to solve the technical problems that the RGB color space is insufficient in image processing and the method for measuring the gravel content in the prior art has limitation. The method comprises the following steps: soil profile acquisition, image acquisition, scale setting and ROI selection, HSV color model conversion, image segmentation, noise reduction, image fusion, image screening, image re-segmentation and gravel volume quantification are obtained. The invention provides a new scheme for the determination of the gravel content, namely digital quantification, and has the advantages of high accuracy of the determined gravel content, accurate result, high feasibility, low labor cost and material cost in use and high determination efficiency of the gravel content.
Description
Technical Field
The invention relates to the technical field of digital soil, in particular to a digital soil morphology measurement method based on ImagePy.
Background
The gravel in the soil is defined differently in different classification systems according to its size and shape, and mineral particles which are not less than 2mm in diameter, relatively independent and not easily broken are generally considered as the gravel, and the soil containing the gravel is called as the stony soil. The characteristics of the gravel, such as the content, the size, the spatial distribution and the like, play an important role in the hydrologic and carbon circulation process of the soil, but the method for measuring the gravel content in the prior art has limitations. Wherein:
(1) Ring cutter sampling method: for larger-sized gravels, the volume of the pit needs to be increased, and the actual operation is time-consuming and labor-consuming and has high labor intensity.
(2) The Viro drill rod inserting method: only at a gravel content of < 50% has a certain accuracy, and the measurement result is poor in accuracy when the gravel content is > 50%.
(3) Gamma ray method: correction is performed before use, but correction is difficult in places with large space variation of gravel size and content, radioactive elements are harmful to human bodies, equipment is heavy, and the correction is inconvenient to apply in places far away from roads.
(4) Resistivity tomography: the variability of the water content in the field can cause the resistivity values of fine soil and gravel to fluctuate within a certain range, thereby affecting the accuracy of the final gravel content estimation, so that the method can estimate the gravel content in the soil within a range of errors and needs further research.
(5) Ground Penetrating Radar (GPR): when measuring the gravel content of a stone soil using GPR, the reflection of electromagnetic waves by deeper rock is masked by the reflection of upper rock, so that the resulting image cannot reflect the reality of the inside of the stone soil. When the gravel content is higher, the reflection waveform is disordered due to the more reflection interfaces, so that the stone soil is not an ideal detection environment of the GPR. In addition, although the use of various commercial software can obtain GPR scan images with high accuracy, whether the accuracy thereof satisfies the discrimination between gravel and fine earth has not been effectively verified.
In image processing, the most commonly used color space is an RGB model, which is commonly used for color display and image processing, and in the form of a model of three-dimensional coordinates, RGB is the color space that we touch most, and an image is represented by three channels, red (R), green (G) and blue (B), respectively. Different combinations of these three colors may form almost all other colors.
The RGB color space is the most basic, most commonly used, hardware-oriented color space in image processing. The RGB color space uses a linear combination of three color components to represent the color, any color being related to the three components, and the three components being highly correlated, so that it is not intuitive when continuously transforming the colors, and changes are required to adjust the color of the image. The images acquired in the natural environment are easily affected by natural illumination, shielding, shadow and the like, namely are sensitive to brightness. While the three components of the RGB color space are all closely related to luminance, i.e. as soon as the luminance changes, the three components will change accordingly, without being expressed in a more intuitive way. However, the sensitivity of the human eye to these three color components is different, and in a single color, the human eye is least sensitive to red and most sensitive to blue, so that the RGB color space is a color space with poor uniformity. If the similarity of colors is measured directly by Euclidean distance, the result will deviate greatly from human vision. For a certain color, it is difficult to infer more accurate three component values to represent.
Disclosure of Invention
The invention aims to provide a digital soil morphology measurement method based on ImagePy, which aims to solve the technical problems that the method for measuring the gravel content in the prior art has limitations and the RGB color space has defects in image processing.
In order to solve the technical problems, the invention adopts the following technical scheme:
the digital soil morphology measurement method based on the ImagePy comprises the following steps:
(1) Acquiring a soil profile: digging a soil section in a given area, cutting off exposed root systems, cleaning a smooth section, and adding a scale on a vertical section;
(2) Image acquisition: under the condition of sufficient light, shooting a plurality of soil profile images;
(3) Setting a scale and selecting an ROI: introducing the image obtained in the step (2) into image processing software, correcting a lens, eliminating chromatic aberration, adjusting image exposure, improving image quality, storing and exporting, introducing the exported image into an ImagePy, calling a measuring tool, setting a scale, and cutting out an ROI;
(4) HSV color model conversion: converting the ROI from an RGB color model to an HSV color model;
(5) Image segmentation: separating small-grain size, large-grain size and medium-grain size gravels from other substances in an image in H, S, V channels respectively, wherein the hue is adjusted in H channels, the saturation is adjusted in S channels and the brightness is adjusted in V channels;
(6) Noise reduction: carrying out fuzzy processing on the image by using a Gaussian filter, and enhancing the contrast between a small particle target and the large particle gravel in the S channel by using a maximum filter;
(7) And (3) image fusion: fusing the small-grain size, large-grain size and medium-grain size stone images in the H, S, V channel;
(8) Image screening: eliminating roots, macropores and other non-gravel objects in the <2 mm-sized portion of the image using a geometric filtering algorithm;
(9) Image repartitioning: performing image segmentation again by using a UDW algorithm, and storing and exporting;
(10) Quantification of gravel volume: the gravel volume percentage is calculated from the percentage of open-ended projected area observed in the soil profile.
Preferably, in the step (1), the soil is rectangular in cross section and has a size of 1m×0.5m.
Preferably, in step (2), the number of uses isThe code single phase inverter shoots a plurality of 0.5m 2 A soil profile image of 6000 x 4000 pixels in range, the focal length of the image being 22mm and the camera lens being 1m from the soil profile.
Preferably, in step (3), the image processing software is Adobe Photoshop 2021, and uses a configuration file in the software to correct a lens, eliminate chromatic aberration, adjust image exposure, improve image quality, and save and export as a tiff format image.
Preferably, in step (3), the measuring tool is called through measurement→distance, the Scale is set through image→scale And Unit, the ROI is cut out by using a rectangular tool, and the selected size of the ROI is 0.5×0.5m.
Preferably, in step (4), the Color model conversion is performed using a Color conversion module image→color→rgb To HSV encapsulated in ImagePy.
Preferably, in step (6), the Gaussian filter is called by process→filters→Gaussian, and the Maximum filter is called by process→filters→maximum.
Preferably, in the step (7), image fusion is performed by a process→image Calculator; in step (8), the Minimum filter is invoked by process→filters→minimum.
Preferably, in step (9), the image is divided from process→hydrology→ Up And Down Watershed, and stored and exported in tiff format.
Preferably, in step (10), the percentage of gravel volume is calculated from the percentage of white areas in the soil profile binary image: rp=ra/ta×100; where RP represents the percentage by gravel volume, RA represents the projected area of the gravel, and TA represents the total projected area of the soil profile image.
Compared with the prior art, the invention has the beneficial technical effects that:
1. the invention converts two-dimensional image data of the gravel into three-dimensional data based on the ImagePy, omits sampling cost and high CT scanning cost, provides a new scheme (namely digital quantization) for measuring the content of the gravel, adopts a new algorithm, a calculation step, a measurement method and measurement parameters, and has the advantages of high accuracy of the measured content of the gravel, accurate result, high feasibility, low labor cost and material cost in use and high (instantaneity) measuring efficiency for the content of the gravel.
2. The UDW algorithm adopted by the invention is a combination of a Sobel filter and a region growing watershed algorithm for pixel segmentation. The Sobel filter is an edge detection tool that can extract the maximum derivative of the image intensity value, enhance the difference between the gravel and the soil matrix, and detect the boundary between the soil matrix and the gravel. The region growing watershed algorithm is a hybrid segmentation method that combines edge-based and region-based methods to obtain better segmentation results. The algorithm merges pixels with more similar properties with adjacent homogenous pixels until a boundary between gravel and soil matrix is identified, thereby effectively avoiding over-segmentation outside the boundary.
3. The invention applies HSV color space to the field of digital soil morphology measurement based on ImagePy, can intuitively express the tone, vividness and brightness of the color, and is convenient for color comparison. When the HSV is used for dividing the appointed color, the HSV has a larger effect, and under the HSV color space, an object with a certain color is easier to track than RGB, and the HSV is more suitable for dividing the object with the appointed color.
Drawings
FIG. 1 is a schematic representation of a cross-sectional image of soil in accordance with the present invention.
Fig. 2 is a schematic view of the soil profile image ROI cut out in the present invention.
Fig. 3 is a schematic diagram of an H-channel in image segmentation according to the present invention.
Fig. 4 is a schematic diagram of an S channel in image segmentation according to the present invention.
Fig. 5 is a schematic view of V-channels in image segmentation according to the present invention.
FIG. 6 is a binary image of a soil profile of the present invention containing only gravel.
Detailed Description
The following examples are given to illustrate the invention in detail, but are not intended to limit the scope of the invention in any way.
Example 1:
an ImagePy-based digital soil morphology measurement method, see fig. 1-6, includes the steps of:
(1) Acquiring a soil profile: after the original soil column is collected, a rectangular soil section of 1m multiplied by 0.5m is excavated in situ, exposed root systems are cut off by scissors, smooth sections are cleaned by using brushes and cutters, and a scale is added on the vertical sections so as to calibrate and measure the gravel size in the later period.
(2) Image acquisition: under the condition of sufficient light, a digital single-lens reflex camera (Sony ILCE-6000) is used for shooting a plurality of 0.5m sheets 2 A soil profile image of 6000 x 4000 pixels in range, see fig. 1. The focal length of the image is 22mm, the camera lens is 1m away from the soil section, and the image pixels and focal lengths of the soil section are different due to different shooting devices.
(3) Setting a scale and selecting an ROI: the image is imported into Adobe Photoshop 2021, the configuration file in the software is utilized to correct the lens, eliminate chromatic aberration and adjust image exposure to improve image quality, and the image is saved and exported into tiff format. The tiff Image was imported into ImagePy, and the Scale was measured And set using the measure→distance And image→scale And Unit tool. The soil profile image was then cropped to a ROI of 0.5 x 0.5m (2273 x 2273 pixels) using a rectangular tool, see fig. 2, with an image resolution of 2.2pixels/mm, screening for gravel >2mm in diameter with a resolution of > 4.4 pixels.
(4) HSV color model conversion: the ROI was converted from RGB Color model To HSV Color model using Color conversion module image→color→rgb To HSV encapsulated in ImagePy.
(5) Image segmentation: the image is divided into hue (H), saturation (S) and brightness (V) channels. The small-particle size, large-particle size, and medium-particle size gravels are separated from other substances in the image in H, S, V channels, respectively, wherein the hue is adjusted in H channels, the saturation is adjusted in S channels, and the brightness is adjusted in V channels.
H represents color information, i.e. the location of the spectral color that it is in, and small particle size gravel can be segmented in the H-channel. S is expressed as the ratio between the purity of the selected color and the maximum purity of that color, with s=0, ranging from 0 to 1, only gray scale. Since saturation is less affected by shadows or other brightness factors and there is a large contrast between shadows and the soil matrix, large-particle-size gravel with a large contrast can be segmented in the S-channel. V represents the brightness of the color, ranging from 0 to 1, with a little attention paid: there was no direct link between it and light intensity, and medium size gravel was split in V-channels after shadow removal by Image Calculator in ImagePy in experiments.
(6) Noise reduction: in order to prevent over-segmentation, a Gaussian filter is called by a Process- & gt Filters- & gt Gaussian, and details such as noise are reduced by blurring the image by using the Gaussian filter, so that segmentation accuracy is improved. The contrast between the small particle target and the large particle gravel in the S-channel is enhanced by invoking the Maximum filter through the Process → Filters → Maximum.
(7) And (3) image fusion: the small, large, medium particle size gravel images in the H, S, V channel were fused using an Image Calculator processor→image Calculator.
(8) Image screening: root, macropores, and other non-gravel objects in the <2mm sized portion of the image are eliminated using a geometric filtering algorithm by process→filters→minimum call Minimum Filters.
(9) Image repartitioning: image segmentation is performed through process→hydrology→ Up And Down Watershed, and the UDW algorithm packaged in imagePy is used for segmentation, storage and export into tiff format again.
The UDW algorithm consists of a Sobel filter and a region growing watershed algorithm. Sobel filters are commonly used to extract the horizontal (horizontal features) and vertical (vertical features) edges of gray scale images, which can increase the difference between gravel and other soil substrates and detect their boundaries. The regional growth watershed algorithm is integrated with a mixed segmentation method based on edges and regions, so that excessive segmentation of boundaries can be effectively avoided, and gravels and other soil matrixes can be completely segmented.
In the segmentation process, a pixel change gradient extracted by a Sobel filter is firstly used. And then determining a boundary and a segmentation target by using a regional watershed algorithm, and screening macropores, root systems and other non-gravel substances smaller than 2mm in the image by using a geometric filter after obtaining the binary image. And then comparing the images with the original images to check whether the gravels are completely separated from the soil matrix, and manually selecting and deleting non-gravels in the images by using a magic wand tool. Finally, a binarized image of the soil profile containing only gravel was generated, see fig. 6.
(10) Quantification of gravel volume: the volume percent is assumed to be equal to the percentage of outcrop area in the soil profile. Thus, the percentage of gravel volume is calculated from the percentage of projected area of outcrop observed, i.e., the percentage of white area in the binary image of the soil profile, i.e., RP =RP represents the percentage by gravel volume, RA represents the projected area of the gravel, and TA represents the total projected area of the soil profile image.
The invention is described in detail above with reference to the drawings and examples; however, it will be understood by those skilled in the art that various specific parameters of the above embodiments may be changed or equivalents may be substituted for related parts and structures thereof without departing from the spirit of the present invention, so as to form a plurality of specific embodiments, which are common variations of the present invention and will not be described in detail.
Claims (10)
1. The digital soil morphology measurement method based on the ImagePy is characterized by comprising the following steps of:
(1) Acquiring a soil profile: digging a soil section in a given area, cutting off exposed root systems, cleaning a smooth section, and adding a scale on a vertical section;
(2) Image acquisition: under the condition of sufficient light, shooting a plurality of soil profile images;
(3) Setting a scale and selecting an ROI: introducing the image obtained in the step (2) into image processing software, correcting a lens, eliminating chromatic aberration, adjusting image exposure, improving image quality, storing and exporting, introducing the exported image into an ImagePy, calling a measuring tool, setting a scale, and cutting out an ROI;
(4) HSV color model conversion: converting the ROI from an RGB color model to an HSV color model;
(5) Image segmentation: separating small-grain size, large-grain size and medium-grain size gravels from other substances in an image in H, S, V channels respectively, wherein the hue is adjusted in H channels, the saturation is adjusted in S channels and the brightness is adjusted in V channels;
(6) Noise reduction: carrying out fuzzy processing on the image by using a Gaussian filter, and enhancing the contrast between a small particle target and the large particle gravel in the S channel by using a maximum filter;
(7) And (3) image fusion: fusing the small-grain size, large-grain size and medium-grain size stone images in the H, S, V channel;
(8) Image screening: eliminating roots, macropores and other non-gravel objects in the <2 mm-sized portion of the image using a geometric filtering algorithm;
(9) Image repartitioning: performing image segmentation again by using a UDW algorithm, and storing and exporting;
(10) Quantification of gravel volume: the gravel volume percentage is calculated from the percentage of open-ended projected area observed in the soil profile.
2. The digital soil morphology measurement method according to claim 1, wherein in step (1), the soil section is rectangular, and the size is 1m x 0.5m.
3. The digital soil morphology measurement method according to claim 1, wherein in the step (2), a plurality of 0.5m sheets are photographed using a digital single phase inverter 2 A soil profile image of 6000 x 4000 pixels in range, the focal length of the image being 22mm and the camera lens being 1m from the soil profile.
4. The method according to claim 1, wherein in step (3), the image processing software is Adobe Photoshop 2021, and the configuration file in the software is used to correct the lens, eliminate chromatic aberration, adjust image exposure, improve image quality, and store and export images in tiff format.
5. The method according to claim 1, wherein in the step (3), the measuring tool is called by measurement→distance, the Scale is set by image→scale And Unit, the ROI is cut out by rectangular tool, and the selected size of the ROI is 0.5×0.5m.
6. The method according To claim 1, wherein in step (4), the Color model conversion is performed using a Color conversion module image→color→rgb To HSV encapsulated in ImagePy.
7. The digital soil morphology measurement method of claim 1, wherein in step (6), the Gaussian filter is called by Process → Filters → Gaussian, and the Maximum filter is called by Process → Filters → maximm.
8. The method according to claim 1, wherein in the step (7), the Image fusion is performed by a process→image Calculator; in step (8), the Minimum filter is invoked by process→filters→minimum.
9. The method according to claim 1, wherein in step (9), image segmentation is performed by process→hydrology→ Up And Down Watershed, and the image is stored and exported in tiff format.
10. The digital soil morphology measurement method of claim 1 wherein in step (10) the percentage of gravel volume is calculated from the percentage of white areas in the soil profile binary image: rp=ra/ta×100; where RP represents the percentage by gravel volume, RA represents the projected area of the gravel, and TA represents the total projected area of the soil profile image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210439257.1A CN114862778B (en) | 2022-04-25 | 2022-04-25 | Digital soil morphology measurement method based on ImagePy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210439257.1A CN114862778B (en) | 2022-04-25 | 2022-04-25 | Digital soil morphology measurement method based on ImagePy |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114862778A CN114862778A (en) | 2022-08-05 |
| CN114862778B true CN114862778B (en) | 2024-02-27 |
Family
ID=82632908
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210439257.1A Active CN114862778B (en) | 2022-04-25 | 2022-04-25 | Digital soil morphology measurement method based on ImagePy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114862778B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997022077A1 (en) * | 1995-12-11 | 1997-06-19 | Willhoit Louis E Jr | Rrocess for seismic imaging, measurement, and evaluation of three-dimensional subterranean common-impedance objects |
| RU2741268C1 (en) * | 2020-04-20 | 2021-01-22 | Федеральное государственное бюджетное учреждение науки Тихоокеанский океанологический институт им. В.И. Ильичева Дальневосточного отделения Российской академии наук (ТОИ ДВО РАН) | Mobile apparatus for determining colour characteristics of rocks |
| CN113514483A (en) * | 2021-08-17 | 2021-10-19 | 河南农业大学 | Method for detecting characteristics of soil gravels in rocky mountain areas |
| WO2021248915A1 (en) * | 2020-06-10 | 2021-12-16 | 青岛理工大学 | Color difference analysis/detection method and system for fair-faced concrete |
-
2022
- 2022-04-25 CN CN202210439257.1A patent/CN114862778B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997022077A1 (en) * | 1995-12-11 | 1997-06-19 | Willhoit Louis E Jr | Rrocess for seismic imaging, measurement, and evaluation of three-dimensional subterranean common-impedance objects |
| RU2741268C1 (en) * | 2020-04-20 | 2021-01-22 | Федеральное государственное бюджетное учреждение науки Тихоокеанский океанологический институт им. В.И. Ильичева Дальневосточного отделения Российской академии наук (ТОИ ДВО РАН) | Mobile apparatus for determining colour characteristics of rocks |
| WO2021248915A1 (en) * | 2020-06-10 | 2021-12-16 | 青岛理工大学 | Color difference analysis/detection method and system for fair-faced concrete |
| CN113514483A (en) * | 2021-08-17 | 2021-10-19 | 河南农业大学 | Method for detecting characteristics of soil gravels in rocky mountain areas |
Non-Patent Citations (4)
| Title |
|---|
| 荐圣淇 ; 赵传燕 ; 彭焕华 ; 方书敏 ; 柳逸月 ; .利用染色示踪与图像处理术研究根系对土壤大孔隙的影响.兰州大学学报(自然科学版).2011,(05),全文. * |
| 邱琳 ; 吴华山 ; 陈效民 ; 潘剑君 ; .利用染色示踪和图像处理技术对土壤大孔隙进行定量研究.土壤.2007,(04),全文. * |
| 郭梦 ; 吴克宁 ; 杜凯闯 ; .新疆地区盐土在中国土壤系统分类中的归属.土壤通报.2020,(02),全文. * |
| 陈文静 ; 蔡小舒 ; 陈孝震 ; 苏明旭 ; .基于HSV空间的岩心图像处理.中国粉体技术.2013,(02),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114862778A (en) | 2022-08-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Graham et al. | Automated sizing of coarse-grained sediments: image-processing procedures | |
| US8086030B2 (en) | Method and system for visually presenting a high dynamic range image | |
| CN105719274B (en) | Edge inspection system and method | |
| JP5914688B2 (en) | Color analysis for digital images | |
| CN110458787B (en) | Image fusion method and device and computer storage medium | |
| CN109447945A (en) | Wheat Basic Seedling rapid counting method based on machine vision and graphics process | |
| JP2006252529A (en) | Planimetric feature environment condition provision method and program thereof | |
| CN113077486B (en) | Method and system for monitoring vegetation coverage rate in mountainous area | |
| CN101976436A (en) | Pixel-level multi-focus image fusion method based on correction of differential image | |
| CN115861409B (en) | Soybean leaf area measuring and calculating method, system, computer equipment and storage medium | |
| US20130114911A1 (en) | Post processing for improved generation of intrinsic images | |
| Ye et al. | Removing shadows from high-resolution urban aerial images based on color constancy | |
| CN112435290A (en) | Leaf area image measuring method based on saturation segmentation | |
| CN114862778B (en) | Digital soil morphology measurement method based on ImagePy | |
| CN106023223A (en) | Orange fruit size describing method and organic fruit size grading method | |
| KR20210096925A (en) | Flexible Color Correction Method for Massive Aerial Orthoimages | |
| Nederbragt et al. | Image calibration, filtering, and processing | |
| JP5860970B2 (en) | Post-processing to improve eigenimage generation | |
| Harrison et al. | Earth Observation: Data, Processing and Applications. Volume 2C: Processing—Image Transformations | |
| JP3505496B2 (en) | Image processing method and image processing apparatus | |
| CN109359583B (en) | Method for extracting significant planar specific target in remote sensing image | |
| Chen et al. | Segmentation-based hierarchical interpolation filter using both geometric and radiometric features for LiDAR point clouds over complex scenarios | |
| CN111178175A (en) | Automatic building information extraction method and system based on high-view satellite image | |
| JP2007121259A (en) | Image processor, image processing program, display controller, and display control program | |
| CN113129320B (en) | Method and system for extracting culture pond boundary from aerial orthographic image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |