CN104599278B - Shallow sea sand wave information extraction method based on remote sensing image - Google Patents
Shallow sea sand wave information extraction method based on remote sensing image Download PDFInfo
- Publication number
- CN104599278B CN104599278B CN201510045849.5A CN201510045849A CN104599278B CN 104599278 B CN104599278 B CN 104599278B CN 201510045849 A CN201510045849 A CN 201510045849A CN 104599278 B CN104599278 B CN 104599278B
- Authority
- CN
- China
- Prior art keywords
- bed ripples
- remote sensing
- sensing images
- pixel
- spot
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
- G06T2207/30184—Infrastructure
Landscapes
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a shallow sea sand wave information extraction method based on a remote sensing image. The method includes the steps of obtaining a pixel inclination direction distribution diagram corresponding to the remote sensing image, determining an initial sand wave area, determining an initial sand wave planar map spot, determining a final sand wave planar map spot, determining a sand wave ripples curve and extracting sand wave length information and wave direction information. The shallow sea sand wave information extraction method based on the remote sensing image can efficiently and accurately extract sand wave information for meeting the demand of terrain change monitoring of widespread shallow sea sand wave areas by introducing the two-dimensional pixel inclination direction and through wide range coverage and high-frequency revisit capability of the remote sensing technology on the basis of light and dark stripe characteristics which are shown due to the modulation of sand waves to water flow fields and sea surface roughness and included in the remote sensing image, can be used for monitoring shallow sea sand wave terrain dynamic changes, is an innovation of the remote sensing information technology used for sand wave information extraction and terrain change monitoring and has a great practical value.
Description
Technical field
The invention belongs to remote sensing technique application and shallow water topography monitoring field, are related to a kind of extraction side of shallow sea bed ripples information
Method, it is specifically a kind of that half-tone information contained in remote sensing images can be utilized to extract the position of bed ripples, length and ripple under shallow sea water
To etc. information method.
Background technology
Sea floor surreying is the first step for exploring and studying ocean, is the most important condition for developing and protecting ocean, is also
Urgent needss of oceanographic research now.Before echo depth sounder invention, sound the depth of the water mainly by Sounding Rod and sounding bob,
Certainty of measurement is poor.After the echo depth sounder appearance twenties in 20th century, the drafting of sea chart in Modern Significance is just achieved.But
The sounding instrument of early stage is simple beam transmitting, and primary emission can only obtain the depth of water immediately below surveying vessel, thus can only realize point, line
Measurement, it is impossible to the topography and geomorphology reflected between survey line.The multibeam echosounding technology that 1970 mid-nineties 90s occurred realizes banding survey
Amount（7 times up to the depth of water of Breadth Maximum）, significantly improve depth measurement efficiency.Even so, being limited to measure the cycle length, manpower consumption
The inferior position of the aspects such as big and credit requirement height, in the neritic province domain of landform High variation large-scale Monitoring on Dynamic Change task is carried out
In, rely only on multibeam echosounding method and be still difficult to meet demand.The shallow sea bed ripples landform widely distributed for neritic province domain is moved
State variation monitoring, needs to be grasped the Back ground Informations such as position, length and the wave direction of bed ripples.How the big model of remote sensing technology is given full play to
High dynamic observing capacity is enclosed, the bed ripples information in shallow sea bed ripples area is automatically extracted, realize dynamic monitoring, for grasp landform
Evolution and feature are all of great significance.
The present invention carries out the demand of topography variation dynamic monitoring for widely distributed shallow sea bed ripples areal area, using remote sensing
Technical method is covered on a large scale and the ability that revisits of altofrequency, based on included in remote sensing images because bed ripples landform is to water body
The modulation of flow field and sea extra coarse degree and present bright dark fringe feature, introduce two-dimensional image pixel incline direction, can be efficient
Position, length and the wave direction information of shallow sea bed ripples are extracted exactly, can be used for shallow sea bed ripples landform Monitoring on Dynamic Change.From retrieval
To open source information see not yet have based on remote sensing images, shallow sea bed ripples information is carried out by two-dimensional image pixel incline direction and is carried
The relevant report for taking.
The content of the invention
It is an object of the invention to provide a kind of efficiently and accurately, convenient low consumption are carried based on the shallow sea bed ripples information of remote sensing images
Take method.
The present invention is achieved through the following technical solutions：
A kind of shallow sea bed ripples information extracting method based on remote sensing images, comprises the following steps：
（1）The two-dimentional incline direction of each pixel in the remote sensing images containing bed ripples information is calculated, remote sensing images pair are obtained
The pixel incline direction scattergram answered；
（2）Most long bright fringess are the first striped in labelling remote sensing images, and most long dark fringe is the second striped, from first
Striped to the second striped does the first boost line so as to as much as possible perpendicular to the first striped and the second striped, the boost line with just
The north to angle be designated as A0, segmentation threshold A1=A0-T, A2=A0+T, parameter T is 30-45 °, is determined according to the size of A1, A2
Span, the set of the pixel that two-dimentional incline direction is located in span in remote sensing images is preliminary bed ripples area；
（3）Preliminary bed ripples planar figure spot corresponding with preliminary bed ripples area is obtained using vectorization method on remote sensing images；
（4）Preliminary bed ripples planar figure spot to obtaining carries out area and shape is filtered, and obtains final bed ripples planar figure spot；
（5）The centrage of final bed ripples planar figure spot is extracted as bed ripples curve, bed ripples positional information is obtained；
（6）Calculate the length information that bed ripples length of a curve obtains bed ripples；
（7）Connect two end points of bed ripples curve as the second boost line with straight line, calculate the boost line and direct north
Angle α, and then obtain bed ripples wave direction β=+ 90 ° of α.
In above-mentioned technical proposal, step（1）Described in the two-dimentional inclination side for calculating each pixel on remote sensing images
To concrete grammar is as follows：
For the pixel at remote sensing images most edge, its two-dimentional incline direction is not calculated, be directly designated as null value；
For remaining pixel on remote sensing images, 8 adjacent pixels of current pixel and its surrounding are amounted to into 9 pixels
Gray value from top to bottom, is from left to right designated as successivelya、b、c、d、e、f、g、h、i, then the two-dimentional incline direction A of current pixel
Computing formula is：
Wherein, p is provisional parameter, and the codomain scope of A is 0 ~ 360, and unit is degree.
Step（2）In to step（1）Calculate the pixel directional spreding figure for obtaining and enter row threshold division.Investigate remote sensing images
Upper bed ripples texture distribution characteristicss, bed ripples typically exhibits bright dark fringe and is spaced apart, bright dark according to bed ripples terrain remote sensing imaging mechanism
Striped joint is the position of bed ripples.From the point of view of the section moved towards perpendicular to bed ripples, present on remote sensing images from background gray scale by
Gradual change is bright（It is dimmed）, reach most bright（It is most dark）Place, suddenly becomes most dark（It is most bright）, brighten then and gradually（It is dimmed）To background ash
Degree, wherein from most bright（It is most dark）Place sports most dark（It is most bright）Where be exactly bed ripples position.It can be seen that, bed ripples position
Pixel grey scale change trend with background area is contrary.Such as, bed ripples position is most bright when most secretly changing, then background area
Domain is from secretly to bright change；Conversely, when bed ripples position is most secretly to arrive most bright change, then background area is bright to dark change.Therefore,
The codomain scope of bed ripples region pixel incline direction A is different from background area.According to maximum bed ripples on remote sensing images（Visually sentence
Fixed most long bright fringess and dark fringe）Position, as far as possible perpendicular to the most long bright dark fringe from the bright fringess to the filaments of sun
Stricture of vagina draws the first boost line, determines that the boost line is initial threshold A0 with the angle of direct north, determines segmentation threshold A1 and A2,
A1=A0-T, A2=A0+T, wherein T are usually a value between 30 ~ 45 degree.Span is determined according to the value of A1 and A2, is had
Body determines that method is as follows：
1. A1 >=0 °, and during A2≤360 °, span is [A1, A2]；
During 360 ° of 2. A1 >=0 °, and A2 ＞, span is [A1,360 °] ∪ [0, A2-360 °]；
3. 0 ° of A1 ＜, and during A2≤360 °, span is [360 ° of+A1,360 °] ∪ [0, A2].
The set of the pixel that two-dimentional incline direction is located in span in remote sensing images is preliminary bed ripples area；
Step（3）Described in vectorization method be Edge track vectorization method, using GIS software
（ArcGIS）Realize.
Step（4）Described in preliminary bed ripples planar figure spot is carried out area and shape filter, concrete grammar is as follows：Retain
Major axis is 10-20 times of pixel dimension and area is the figure spot of 20-50 pixel area in preliminary bed ripples planar figure spot, obtains final
Bed ripples planar figure spot.
Step（5）Described in the final bed ripples planar figure spot of extraction centrage, using the equal point-score of Euclidean distance, its
Concrete operation step is as follows：1）In the direction for being basically perpendicular to bed ripples planar figure spot major axis, some straight line auxiliary are uniformly arranged
Line, the spacing between straight line boost line is 3-5 times of remote sensing images pixel resolution r；2）Take every straight line boost line and bed ripples face
The midpoint of shape figure spot lap, is designated as the central point of the straight line boost line, and the central point of all straight line boost lines is connected, and obtains
Obtain bed ripples curve, as bed ripples positional information.
Specifically, widely distributed shallow sea bed ripples landform can be imaged on remote sensing images, be rendered as the clearly bright filaments of sun
Between width or distribution bed ripples texture information.Its principle is：The landform that shallow sea bed ripples area rises and falls has modulated sea by hydrodynamism
Surface low field, the sea surface velocities field after being modulated changes the density spectra of surface wave by wave-current interactions, surface wave density spectra
Change causes the change of sea surface roughness, the change of sea surface roughness to cause the change of air-sea interface sun glitter scattering strength,
So as to present on remote sensing images between bright dark fringe or distribution bed ripples ripple information.The alternate place of bright dark fringe of remote sensing images is
The position for indicating bed ripples is located.And bed ripples landform acts on being formed naturally as neritic province domain through long-term Hydrodynamic Process
Looks form, is presented waveform continuous distribution, and for the bed ripples in the range of specific region is generally in arranged distribution, bed ripples trend is basic
Be close to, thus on remote sensing images bright dark fringe trend it is also close.Bed ripples corrugation patterns information and sand on above-mentioned remote sensing images
The rolling land shape regularity of distribution, can be used for shallow sea bed ripples information retrieval.
The characteristic information that the present invention is presented using shallow sea bed ripples landform in remote sensing images（Between bright dark fringe or it is distributed）With
Bed ripples topography profile rule, introduces two-dimensional image pixel incline direction, by remote sensing images gray space analytical calculation, obtains husky
The information such as the position of ripple, length and wave direction.
The invention has the beneficial effects as follows：
Shallow sea bed ripples areal area landform is generally in dynamic change, and bed ripples position and metamorphosis situation are bed ripples landform
The important content of monitoring.Though multibeam echosounding has become the Main Means of current shallow water depth measurement, by measure the cycle, manpower
Consume and fund restriction, generally cannot Jing often carry out kinetic measurement with monitoring.Remote sensing monitoring is a kind of very effective bed ripples
Topography variation dynamic monitoring means.The present invention is directed to the measurement of topography deformation demand in widely distributed shallow sea bed ripples area, using distant
Sense technical method is covered on a large scale and the ability that revisits of altofrequency, based on included in remote sensing images because bed ripples landform is to water
The modulation of body flow field and sea extra coarse degree and in the bright dark fringe feature for presenting, introduce two-dimensional image pixel incline direction, can be high
Effect extracts exactly position, length and the wave direction information of shallow sea bed ripples, can be used for shallow sea bed ripples management and landform dynamic change prison
Survey, be an innovation of the Remote Sensing for bed ripples measurement of topography deformation, with great practical value.
Description of the drawings
Fig. 1 is the Technology Roadmap based on the shallow sea bed ripples information extracting method of remote sensing images；
Fig. 2 is pixel distribution and corresponding grey scale value schematic diagram when calculating the two-dimentional incline direction of pixel；
Fig. 3 is perpendicular to the typical grayscale change hatching of bed ripples trend in remote sensing images；
Fig. 4 is the preliminary bed ripples region obtained based on pixel incline direction segmentation threshold；
Fig. 5 is the final bed ripples planar figure spot obtained through area and shape screening；
Fig. 6 is to extract centrage schematic diagram based on the equal point-score of Euclidean distance；
Fig. 7 is the bed ripples calibration curve information for extracting；
Fig. 8 is that bed ripples wave direction calculates schematic diagram.
Specific embodiment
The present invention is further elaborated below in conjunction with the accompanying drawings.
As shown in figure 1, the present invention's specifically includes following steps based on the shallow sea bed ripples information extracting method of remote sensing images：
（1）The two-dimentional incline direction of each pixel in the remote sensing images containing bed ripples information is calculated, remote sensing images pair are obtained
The pixel incline direction scattergram answered：
The remote sensing images containing bed ripples information are collected, for each pixel on remote sensing images, according to its adjacent picture elements
Gray value, with reference to landform slope aspect, introduce two-dimentional pixel incline direction, calculate and obtain pixel incline direction scattergram.Each
The two-dimentional incline direction of pixel, concrete grammar is as follows：
For the pixel at remote sensing images most edge, its two-dimentional incline direction is not calculated, be directly designated as null value；
For remaining pixel on remote sensing images, 8 adjacent pixels of current pixel and its surrounding are amounted to into 9 pixels
Gray value from top to bottom, is from left to right designated as successivelya、b、c、d、e、f、g、h、i, as shown in Fig. 2 then the two dimension of current pixel is inclined
The computing formula of tilted direction A is：
Wherein, p is provisional parameter, and the codomain scope of A is 0 ~ 360, and unit is degree.
（2）To step（1）The pixel incline direction scattergram of acquisition enters row threshold division, investigates sand ripple on remote sensing images
Reason distribution characteristicss, bed ripples typically exhibits bright dark fringe and is spaced apart, and according to bed ripples terrain remote sensing imaging mechanism, bright dark fringe connects
Locate the position for bed ripples.From the point of view of the section moved towards perpendicular to bed ripples, as shown in figure 3, presenting on remote sensing images from background gray scale
It is gradually dimmed（Brighten）, reach most dark（It is most bright）Place, suddenly becomes most bright（It is most dark）, it is then and gradually dimmed（Brighten）To background
Gray scale, wherein from most dark（It is most bright）Place sports most bright（It is most dark）Where be exactly bed ripples position.It can be seen that, bed ripples institute is in place
It is contrary to put with the pixel grey scale change trend of background area.Such as, bed ripples position is most bright when most secretly changing, then background
Region is from secretly to bright change；Conversely, when bed ripples position is most secretly to arrive most bright change, then background area is bright to dark change.Cause
This, the codomain scope of bed ripples region pixel incline direction A is different from background area.It is most long according to visually confirming on remote sensing images
Bright fringess and dark fringe（I.e. maximum bed ripples）Position, using the bright fringess as the first striped, the dark fringe as the second striped,
As far as possible perpendicular to first and second striped, the first boost line is drawn from the first striped to the second striped, determine the boost line and positive north
The angle in direction is initial threshold A0, it is determined that segmentation and threshold value A 1 and A2 so that A1=A0-T, A2=A0+T, wherein T are a threshold
It is worth scope, usually between 30 ~ 45 degree a value.Span is determined according to the value of A1 and A2, determines that method is as follows：
1. A1 >=0 °, and during A2≤360 °, span is [A1, A2]；
During 360 ° of 2. A1 >=0 °, and A2 ＞, span is [A1,360 °] ∪ [0, A2-360 °]；
3. 0 ° of A1 ＜, and during A2≤360 °, span is [360 ° of+A1,360 °] ∪ [0, A2].
The set of the pixel that two-dimentional incline direction is located in span in remote sensing images is preliminary bed ripples area, such as Fig. 4
It is shown.
（3）Preliminary bed ripples planar figure spot corresponding with preliminary bed ripples area is obtained using vectorization method on remote sensing images；
Described vectorization method can be Edge track vectorization method, using GIS software（ArcGIS）Realize.
（4）Preliminary bed ripples planar figure spot to obtaining carries out area and shape is filtered, and concrete grammar is as follows：Retain preliminary husky
Major axis is 10-20 times of pixel dimension and area is the figure spot of 20-50 pixel area in the shape figure spot of corrugated, obtains final bed ripples face
Shape figure spot, as shown in Figure 5.
（5）The centrage of final bed ripples planar figure spot is extracted as bed ripples curve using the equal point-score of Euclidean distance, is obtained
Obtain bed ripples positional information；As shown in fig. 6, its concrete operation step is as follows：1）It is being basically perpendicular to bed ripples planar figure spot major axis
Direction, is uniformly arranged some straight line boost lines, and the spacing between straight line boost line is 3-5 times of remote sensing images pixel resolution r；
2）The midpoint of every straight line boost line and bed ripples planar figure spot lap is taken, the central point of the straight line boost line is designated as, by institute
There is the central point connection of straight line boost line, obtain bed ripples curve, as shown in fig. 7, obtaining bed ripples positional information.
（6）Calculate the length information that bed ripples length of a curve obtains bed ripples；
（7）As shown in figure 8, connecting two end points of bed ripples curve as the second boost line with straight line, calculate second and aid in
The angle α of line and direct north, and then it is+90 ° of β=α to calculate the wave direction of bed ripples.Complete bed ripples information retrieval.
The foregoing is only presently preferred embodiments of the present invention, not to limit the present invention, all spirit in the present invention and
Any modification, equivalent and improvement for being made within principle etc., are all contained within protection scope of the present invention.
Claims (5)
1. a kind of shallow sea bed ripples information extracting method based on remote sensing images, it is characterised in that comprise the following steps：
（1）The two-dimentional incline direction of each pixel in the remote sensing images containing bed ripples information is calculated, remote sensing images is obtained corresponding
Pixel incline direction scattergram；
（2）Most long bright fringess are the first striped in labelling remote sensing images, and most long dark fringe is the second striped, from the first striped
The first boost line is done to the second striped, makes the first boost line as much as possible perpendicular to the first striped and the second striped, the first auxiliary
Line is designated as A0, segmentation threshold A1=A0-T, A2=A0+T with the angle of direct north, and parameter T is 30-45 °, according to the big of A1, A2
Little determination span, the set of the pixel that two-dimentional incline direction is located in span in remote sensing images is preliminary bed ripples
Area；
The described size according to A1, A2 determines that the method for span is：
1. A1 >=0 °, and during A2≤360 °, span is [A1, A2]；
During 360 ° of 2. A1 >=0 °, and A2 ＞, span is [A1,360 °] ∪ [0, A2-360 °]；
3. 0 ° of A1 ＜, and during A2≤360 °, span is [360 ° of+A1,360 °] ∪ [0, A2]；
（3）Preliminary bed ripples planar figure spot corresponding with preliminary bed ripples area is obtained using vectorization method on remote sensing images；
（4）Preliminary bed ripples planar figure spot to obtaining carries out area and shape is filtered, and obtains final bed ripples planar figure spot；
（5）The centrage of final bed ripples planar figure spot is extracted as bed ripples curve, bed ripples positional information is obtained；
（6）Calculate the length information that above-mentioned bed ripples length of a curve obtains bed ripples；
（7）Use straight line Connection Step（5）Two end points of bed ripples curve calculate the second boost line and positive north as the second boost line
The angle α in direction, the wave direction of bed ripples is+90 ° of β=α.
2. a kind of shallow sea bed ripples information extracting method based on remote sensing images as claimed in claim 1, it is characterised in that step
（1）The described two-dimentional incline direction for calculating each pixel on remote sensing images, concrete grammar is as follows：
For the pixel at remote sensing images most edge, its two-dimentional incline direction is not calculated, be directly designated as null value；
For remaining pixel on remote sensing images, 8 adjacent pixels of current pixel and its surrounding are amounted to into the gray scale of 9 pixels
Value from top to bottom, is from left to right designated as successivelya、b、c、d、e、f、g、h、i, then the calculating of the two-dimentional incline direction A of current pixel
Formula is：
Wherein, p is provisional parameter, and the codomain scope of A is 0 ~ 360, and unit is degree.
3. a kind of shallow sea bed ripples information extracting method based on remote sensing images as claimed in claim 1, it is characterised in that step
（3）Described in vectorization method be Edge track vectorization method.
4. a kind of shallow sea bed ripples information extracting method based on remote sensing images as claimed in claim 1, it is characterised in that step
（4）Described in preliminary bed ripples planar figure spot is carried out area and shape filter, concrete grammar is as follows：Retain preliminary bed ripples planar
Major axis is 10-20 times of pixel dimension and area is the figure spot of 20-50 pixel area in figure spot, obtains final bed ripples planar figure
Speckle.
5. a kind of shallow sea bed ripples information extracting method based on remote sensing images as claimed in claim 1, it is characterised in that step
（5）Described in the final bed ripples planar figure spot of extraction centrage, using the equal point-score of Euclidean distance, its concrete operation step
It is as follows：
1）In the direction for being basically perpendicular to bed ripples planar figure spot major axis, some straight line boost lines, straight line boost line are uniformly arranged
Between spacing be 3-5 times of remote sensing images pixel resolution r；
2）The midpoint of every straight line boost line and bed ripples planar figure spot lap is taken, the central point of the straight line boost line is designated as,
The central point of all straight line boost lines is connected, bed ripples curve, as bed ripples positional information is obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510045849.5A CN104599278B (en) | 2015-01-29 | 2015-01-29 | Shallow sea sand wave information extraction method based on remote sensing image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510045849.5A CN104599278B (en) | 2015-01-29 | 2015-01-29 | Shallow sea sand wave information extraction method based on remote sensing image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104599278A CN104599278A (en) | 2015-05-06 |
| CN104599278B true CN104599278B (en) | 2017-04-12 |
Family
ID=53125028
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510045849.5A Active CN104599278B (en) | 2015-01-29 | 2015-01-29 | Shallow sea sand wave information extraction method based on remote sensing image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104599278B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108230334B (en) * | 2017-12-21 | 2021-07-27 | 西安工程大学 | High-concentration wind-blown sand image segmentation method based on gray threshold |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101034471A (en) * | 2007-03-29 | 2007-09-12 | 上海大学 | Landform transformation method for sonar remote sensing digital image of underwater digit land model |
| CN101034476A (en) * | 2007-03-29 | 2007-09-12 | 上海大学 | Method for generating underwater non-shadow sonar remote sensing orthographic digital image by computer |
| CN103148842A (en) * | 2013-02-04 | 2013-06-12 | 国家海洋局第二海洋研究所 | Shallow sea sand wave area multi-beam sounding terrain reconstruction method based on remote sensing image features |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020110422A1 (en) * | 2000-12-15 | 2002-08-15 | Jobst Hulsemann | Wave ramp |
-
2015
- 2015-01-29 CN CN201510045849.5A patent/CN104599278B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101034471A (en) * | 2007-03-29 | 2007-09-12 | 上海大学 | Landform transformation method for sonar remote sensing digital image of underwater digit land model |
| CN101034476A (en) * | 2007-03-29 | 2007-09-12 | 上海大学 | Method for generating underwater non-shadow sonar remote sensing orthographic digital image by computer |
| CN103148842A (en) * | 2013-02-04 | 2013-06-12 | 国家海洋局第二海洋研究所 | Shallow sea sand wave area multi-beam sounding terrain reconstruction method based on remote sensing image features |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104599278A (en) | 2015-05-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110969624B (en) | Laser radar three-dimensional point cloud segmentation method | |
| CN109684921B (en) | Road boundary detection and tracking method based on three-dimensional laser radar | |
| CN103148842B (en) | Shallow sea sand wave area multi-beam sounding terrain reconstruction method based on remote sensing image features | |
| US10521694B2 (en) | 3D building extraction apparatus, method and system | |
| CN104361582B (en) | Method of detecting flood disaster changes through object-level high-resolution SAR (synthetic aperture radar) images | |
| CN102521835B (en) | Method for extracting point cloud data of tree height based on space three-dimensional template | |
| CN109712153A (en) | A kind of remote sensing images city superpixel segmentation method | |
| CN103292792B (en) | Actual measurement SVP reconstruction method suitable for submarine detection and pseudo-landform processing | |
| CN104005325A (en) | Pavement crack detecting device and method based on depth and gray level images | |
| CN109031301A (en) | Alpine terrain deformation extracting method based on PSInSAR technology | |
| CN105528585B (en) | Underwater vehicle detecting method based on ship wake and submarine topography internal wave models | |
| CN109781073B (en) | Shallow sea water depth remote sensing extraction method integrating sea wave characteristics and spectral characteristics | |
| Leu et al. | Remotely sensing in detecting the water depths and bed load of shallow waters and their changes | |
| CN106156758B (en) | A kind of tidal saltmarsh method in SAR seashore image | |
| CN101777181A (en) | Ridgelet bi-frame system-based SAR image airfield runway extraction method | |
| CN103729846A (en) | LiDAR point cloud data edge detection method based on triangular irregular network | |
| CN109472802A (en) | A kind of surface grid model construction method constrained certainly based on edge feature | |
| CN104197902A (en) | Method for extracting shallow sea terrain by single-shot high-resolution optical remote sensing image | |
| CN105571571A (en) | Accumulated profile space structure information analysis method based on three-dimensional laser scanning | |
| CN112633092A (en) | Road information extraction method based on vehicle-mounted laser scanning point cloud | |
| Herzog et al. | Least-cost kernel density estimation and interpolation-based density analysis applied to survey data | |
| CN115422981B (en) | Land and water classification method and system for single-frequency airborne laser sounding data and application | |
| CN104613945B (en) | Reconstruction method for terrain of shallow-sea large-sized complicated sand wave area | |
| Vetter et al. | Estimating changes of riverine landscapes and riverbeds by using airborne LiDAR data and river cross-sections | |
| CN106500594A (en) | Fusion reflected intensity and the railroad track method for semi-automatically detecting of geometric properties |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |