CN112834432A - Landslide thickness inversion method based on remote sensing technology and kinematics principle - Google Patents
Landslide thickness inversion method based on remote sensing technology and kinematics principle Download PDFInfo
- Publication number
- CN112834432A CN112834432A CN202110022045.9A CN202110022045A CN112834432A CN 112834432 A CN112834432 A CN 112834432A CN 202110022045 A CN202110022045 A CN 202110022045A CN 112834432 A CN112834432 A CN 112834432A
- Authority
- CN
- China
- Prior art keywords
- landslide
- remote sensing
- displacement
- thickness
- method based
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000005516 engineering process Methods 0.000 title claims description 19
- 238000006073 displacement reaction Methods 0.000 claims abstract description 53
- 230000003287 optical effect Effects 0.000 claims description 21
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 8
- 238000012544 monitoring process Methods 0.000 abstract description 7
- 238000012545 processing Methods 0.000 abstract description 3
- 238000013507 mapping Methods 0.000 abstract description 2
- 238000011160 research Methods 0.000 abstract 2
- 230000001133 acceleration Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- KCXVZYZYPLLWCC-UHFFFAOYSA-N EDTA Chemical compound OC(=O)CN(CC(O)=O)CCN(CC(O)=O)CC(O)=O KCXVZYZYPLLWCC-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 229960001484 edetic acid Drugs 0.000 description 1
- 238000010291 electrical method Methods 0.000 description 1
- 238000004836 empirical method Methods 0.000 description 1
- 238000005305 interferometry Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1765—Method using an image detector and processing of image signal
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Pit Excavations, Shoring, Fill Or Stabilisation Of Slopes (AREA)
- Length Measuring Devices By Optical Means (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention belongs to the field of remote sensing mapping, and particularly relates to a method for determining a relation between landslide deformation and reservoir area water level fluctuation, wherein the method comprises the steps of processing a research area by adopting an SBAS-InSAR method, and calculating to obtain landslide displacement data; extracting water level information by using the multivariate remote sensing data to obtain water level data around the concrete landslide; and finally, analyzing the deformation regulation of the displacement of the landslide in the reservoir area under the condition of water level fluctuation through modeling. The method solves the problem that the water level around the specific landslide is replaced by the single-point monitoring water level in the existing related research, analyzes the landslide deformation rule of the reservoir area under the condition of reservoir water level fluctuation from a new perspective, and provides a new idea for regional monitoring and early warning.
Description
Technical Field
The invention belongs to the field of remote sensing surveying and mapping, and particularly relates to a landslide thickness inversion method based on a remote sensing technology and a kinematics principle.
Background
Due to the development and progress of society, the human civilization is continuously making progress. The problems of water and soil loss, climate change and the like caused by large-scale human activities are increasingly prominent. Natural disasters tend to increase, and particularly disasters such as landslides in mountainous areas, landslides in loess tables and the like are caused. The hazard caused by landslide in mountainous areas is huge. In recent years, landslide geological disasters are more and more paid attention by governments and scientific researchers of various countries, and more scholars study landslide movement by using a non-contact remote sensing technology. The key scientific problems of discovering the cause mechanism of landslide, judging the landslide motion scale, recovering the landslide motion process and the like are attempted through various remote sensing means.
The current remote sensing technical means mainly comprise optical passive remote sensing and radar active remote sensing. The passive optical remote sensing is that the aircraft does not emit energy and only receives sunlight reflected by ground objects. In recent years, the popular low-altitude photogrammetry technology belongs to the category of optical remote sensing. The radar active remote sensing is that the aircraft emits electromagnetic waves and receives the electromagnetic waves reflected by ground objects in the flying process. The same relatively popular synthetic aperture radar interferometry (InSAR) technique belongs to the field of radar remote sensing.
The traditional remote sensing technology has great difficulty in monitoring landslide displacement and inverting thickness. At present, landslide in an image is extracted by an empirical method that a remote sensing image can only be interpreted by visual, and for the plane displacement of the landslide, the plane displacement of the landslide can only be analyzed qualitatively through image comparison, but the plane displacement of the landslide can not be analyzed quantitatively, and the visualization of the movement direction of the landslide can not be realized. The Synthetic Aperture Radar (Interferometric Synthetic Aperture Radar) technology has an effect on monitoring landslide to some extent. It is able to identify potential landslides and to monitor potential landslide settlement to a small extent, with monitoring deformation ranges typically within tens of millimeters. It is ineffective for severe deformation and landslides that have slid significantly because large displacements can cause the radar image to be incoherent.
Disclosure of Invention
Aiming at the problems in the background art, the invention provides a landslide thickness inversion method based on a remote sensing technology and a kinematics principle, overcomes the defects that the traditional remote sensing technology is only qualitative and difficult to quantify, and provides a new direction for solving the landslide thickness.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a landslide thickness inversion method based on remote sensing technology and kinematics principle comprises the following steps:
s1, obtaining an optical remote sensing image of the landslide area;
s2, selecting a stable area around the landslide area by combining the optical remote sensing image;
s3, extracting landslide displacement data by using the optical remote sensing image;
s4, inverting different parts of the landslide by utilizing the landslide displacement data, and determining the landslide thicknesses of different parts;
and S5, comparing the landslide thickness obtained by inversion with the landslide thickness obtained by the conventional technology, and evaluating the result accuracy.
The obtained optical remote sensing image needs to be checked for image quality according to pixel requirements, and the optical remote sensing image meeting the requirements is cut into the size same as that of a landslide range.
The selection requirement of the stable area is as follows: the horizontal displacement is less than 1 m.
When the optical remote sensing image is used for extracting landslide displacement data, parameters related to registration of the optical remote sensing image need to be set, and the parameters needing to be set comprise window size, step length and iteration times.
The landslide displacement data is extracted into displacement of a plurality of groups of segmented landslides in the horizontal and vertical directions, and the length and the direction of specific displacement are determined through vector addition and an inverse trigonometric function.
The inversion method comprises the following steps:
the method comprises the following steps: extracting deformation information of the landslide, wherein the deformation information comprises the size and the direction of displacement and a displacement field;
step two: and judging the motion mode of the landslide and calculating the thickness of the landslide.
The invention has the beneficial effects that: according to the method, the size and the direction of the landslide displacement are extracted through the optical remote sensing image, and the short plates of other remote sensing technologies are made up. Secondly, after the displacement of the secondary landslide in each stage of the landslide is obtained, abstracting the motion of the landslide into a linear acceleration and deceleration process according to the actual motion condition of the landslide, and calculating the thickness of each part of the landslide by combining a kinematic model. The extraction of landslide displacement and the inversion of thickness are streamlined and systematized.
The method solves the problem of landslide displacement extraction, and the extracted displacement has information of size and direction, so that the landslide displacement field can be conveniently visualized. After the landslide displacement is extracted, the thickness of the landslide can be obtained by combining the kinematics principle and the vector decomposition and synthesis rule. The method can obtain the displacement and the thickness of the landslide in a short time, greatly facilitates the monitoring of the landslide and can provide important reference for emergency disposal and engineering management.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the layout of the landslide body and the stability region of the embodiment
FIG. 3 is a graph of displacement of an example landslide from day 10 to day 15 of 8 months;
FIG. 4 is a displacement pattern of an embodiment landslide from 10 days 8 months to 15 days;
FIG. 5 is a motion displacement field for an example landslide from 10 to 15 months of 8;
FIG. 6 is a field of local displacement of the foot of the example landslide from 10 to 15 days 8 months;
FIG. 7 is an inverse vector relationship diagram of landslide thickness according to an embodiment.
Detailed Description
The technical scheme of the invention is further explained by specific embodiments in the following with the accompanying drawings:
example 1:
as shown in fig. 1, a method for determining a landslide displacement and a landslide sliding depth provided by an embodiment of the present invention, taking a certain landslide as a case, includes the following steps:
in the embodiment, a high-resolution optical remote sensing image, namely a 5-scene Sentinel-2 image is selected. The image shooting time is respectively 4 days in 7 and 7 months in 2019, 31 days in 7 and 31 months in 2019, 3 days in 8 and 3 months in 2019, 10 days in 8 and 10 months in 2019 and 15 days in 8 and 15 months in 2019, and the time includes the sliding process of the landslide. And preprocessing each scene image before extracting the landslide displacement. First, the study area is cropped. The width of the Sentinel-2 image is 290 kilometers, and if the Sentinel-2 image is directly processed, the efficiency is low, and the time consumption is long.
Before image correlation, the image is resized and cut to the appropriate size. The clipped image covers the whole landslide body and a stable region, and the stable region is used for subsequent compensation correction. And secondly, selecting a proper wave band of the Sentinel-2 image.
The sentinel 2 satellite carries a multispectral imager which can cover 13 spectral bands. Different wavelength bands have different wavelengths and resolutions. The total number of the wave bands is 4, the resolution is 10m, and the wave bands are respectively 2 wave bands, 3 wave bands, 4 wave bands and 8 wave bands. From these four bands and combinations thereof, suitable bands are selected because of their higher resolution. And preprocessing the image to obtain north-south displacement, east-west displacement and signal-to-noise ratio. The signal-to-noise ratio is typically used to evaluate the correlation results, with the greater the signal-to-noise ratio, the better the results. Through preliminary experiments and preliminary result statistical analysis, the appropriate band combination is determined to be a combined band in the form of an arithmetic mean of bands 2, 3, and 4. When performing formal image correlation, appropriate parameters are determined. And carrying out preliminary experiments and carrying out statistical analysis on experimental results when parameters are determined, wherein the results show that the appropriate window size is 32-32 and the step length is 1.
And performing relevant processing on the images in each period by adopting a proper wave band combination and processing parameters to respectively obtain displacement components in two directions (e/w and n/s). The magnitude of the displacement is obtained by performing a band calculation on the two displacement components in the arcgis software, and the value is the square root of the two displacement components.
The operation of extracting the landslide displacement direction is as follows, positive deformation of the displacement field in the east-west direction is extracted, and deformation corresponding to the displacement field in the north-south direction (n/s) is extracted. According to the formula
arccos(ns/deformation)*180/π
The orientation of this portion is obtained. And similarly, extracting the negative deformation of the east-west direction (e/w) displacement field, and then extracting the corresponding deformation of the north-south direction (n/s) displacement field. Finally, according to the written condition function
con(arccos(ns/deformation)*180π)>90
arccos(ns/deformation)*180/π+90
360-arccos(ns/deformation)*180/π)
And calculating the displacement direction of the other part of the landslide, and further calculating the displacement direction of the whole landslide.
For the calculation of the thickness of the landslide, the landslide is known to do uniform acceleration motion before 7 and 31 days in 2019 according to actual monitoring data, and does uniform deceleration motion between 7 and 31 days in 2019 and 8 and 15 days in 2019. Landslide started on day 7 and 16 in 2019 and stopped on day 8 and 15 in 2019. And (3) extracting the slope of the landslide by using a high-precision digital elevation model, wherein the average slope of the landslide is 21 degrees. Taking the sliding of the landslide in 8 months and 10 days to 8 months and 15 days as an example, the main sliding occurs at the toe of the slope.
The thickness inversion process will be explained below, wherein the motion of the landslide is abstracted into linear acceleration and deceleration processes according to the actual motion situation of the landslide, the landslide slides along the slope surface of 21 degrees at a certain speed in 8 months and 10 days, and the sliding is stopped until 8 months and 15 days. Based on the results of the optical image correlation: the average displacement of the bottom of the landslide is 17m, and the movement time is 5 days. The average rate of the bottom of the landslide can be found. According to the uniform deceleration motion model, the average speed of the bottom of the landslide in the stage is equal to the instantaneous speed when the sliding time is half, and then the initial speed of the landslide in the stage can be calculated. And then, the average thickness of the bottom of the landslide is 6.2m according to the vector relation. Then, the average thickness of the severely deformed part of the landslide was obtained from 3 days 8 months to 10 days 8 months. The end rate of this phase is equal to the initial rate of landslide slip from 8 months 10 days to 8 months 15 days. Then, the corresponding average thickness of the landslide was found to be 22.1m based on the optical image correlation results at this stage. Similarly, the average thickness of the middle part of the landslide was found to be 14.5m using the idea of velocity iteration. And finally, calculating the thickness of the upper part of the landslide, wherein the method is similar to the idea of calculating the bottom of the landslide, and the average thickness of the upper part of the landslide is 10.9 m. The obtained result is opposite to the sliding surface depth obtained by inverting the existing high-density electrical method, and the confidence coefficient of the result is more than 90%.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (6)
1. A landslide thickness inversion method based on remote sensing technology and kinematics principle is characterized by comprising the following steps:
s1, obtaining an optical remote sensing image of the landslide area;
s2, selecting a stable area around the landslide area by combining the optical remote sensing image;
s3, extracting landslide displacement data by using the optical remote sensing image;
s4, inverting different parts of the landslide by utilizing the landslide displacement data, and determining the landslide thicknesses of different parts;
and S5, comparing the landslide thickness obtained by inversion with the landslide thickness obtained by the conventional technology, and evaluating the result accuracy.
2. The landslide thickness inversion method based on remote sensing technology and kinematics principle as claimed in claim 1. The method is characterized in that: the obtained optical remote sensing image needs to be checked for image quality according to pixel requirements, and the optical remote sensing image meeting the requirements is cut into the size same as that of a landslide range.
3. The landslide thickness inversion method based on remote sensing technology and kinematics principle of claim 1, wherein: the selection requirement of the stable area is as follows: the horizontal displacement is less than 1 m.
4. The landslide thickness inversion method based on remote sensing technology and kinematics principle of claim 1, wherein: when the optical remote sensing image is used for extracting landslide displacement data, parameters related to registration of the optical remote sensing image need to be set, and the parameters needing to be set comprise window size, step length and iteration times.
5. The landslide thickness inversion method based on remote sensing technology and kinematics principle of claim 1, wherein: the landslide displacement data is extracted into displacement of a plurality of groups of segmented landslides in the horizontal and vertical directions, and the length and the direction of specific displacement are determined through vector addition and an inverse trigonometric function.
6. The landslide thickness inversion method based on remote sensing technology and kinematics principle of claim 1, wherein: the inversion method comprises the following steps:
the method comprises the following steps: extracting deformation information of the landslide, wherein the deformation information comprises the size and the direction of displacement and a displacement field;
step two: and judging the motion mode of the landslide and calculating the thickness of the landslide.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110022045.9A CN112834432A (en) | 2021-01-08 | 2021-01-08 | Landslide thickness inversion method based on remote sensing technology and kinematics principle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110022045.9A CN112834432A (en) | 2021-01-08 | 2021-01-08 | Landslide thickness inversion method based on remote sensing technology and kinematics principle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112834432A true CN112834432A (en) | 2021-05-25 |
Family
ID=75928597
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110022045.9A Pending CN112834432A (en) | 2021-01-08 | 2021-01-08 | Landslide thickness inversion method based on remote sensing technology and kinematics principle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112834432A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113848551A (en) * | 2021-09-24 | 2021-12-28 | 成都理工大学 | Landslide depth inversion method using InSAR lifting rail deformation data |
| CN114596495A (en) * | 2022-03-17 | 2022-06-07 | 湖南科技大学 | Sand slide identification and automatic extraction method based on Sentinel-2A remote sensing image |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102426395A (en) * | 2011-11-11 | 2012-04-25 | 铁道第三勘察设计院集团有限公司 | Slip mass volume parameter measuring method based on three-dimensional geologic remote sensing interpretation technology |
| CN103364830A (en) * | 2013-07-24 | 2013-10-23 | 北京师范大学 | Predication method of happening position of slump disaster after earthquake based on multiple factors |
| CN104700399A (en) * | 2015-01-08 | 2015-06-10 | 东北大学 | Method for demarcating large-deformation landslide displacement field based on high-resolution remote sensing image |
| CN106600574A (en) * | 2016-08-25 | 2017-04-26 | 中国科学院遥感与数字地球研究所 | Landslide extraction method based on remote-sensing image and altitude data |
-
2021
- 2021-01-08 CN CN202110022045.9A patent/CN112834432A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102426395A (en) * | 2011-11-11 | 2012-04-25 | 铁道第三勘察设计院集团有限公司 | Slip mass volume parameter measuring method based on three-dimensional geologic remote sensing interpretation technology |
| CN103364830A (en) * | 2013-07-24 | 2013-10-23 | 北京师范大学 | Predication method of happening position of slump disaster after earthquake based on multiple factors |
| CN104700399A (en) * | 2015-01-08 | 2015-06-10 | 东北大学 | Method for demarcating large-deformation landslide displacement field based on high-resolution remote sensing image |
| CN106600574A (en) * | 2016-08-25 | 2017-04-26 | 中国科学院遥感与数字地球研究所 | Landslide extraction method based on remote-sensing image and altitude data |
Non-Patent Citations (4)
| Title |
|---|
| ADAM M. BOOTH ET AL.: "Landslide velocity, thickness, and rheology from remote sensing:La Clapière landslide, France", 《GEOPHYSICAL RESEARCH LETTERS》 * |
| WENTAO YANG: "Selecting the Best Image Pairs to Measure Slope Deformation", 《SENSORS》 * |
| 叶咸 等: "滑坡深部位移监测孔合位移计算与分析", 《公路》 * |
| 殷跃平 等: "汶川地震触发大光包巨型滑坡遥感研究", 《工程地质学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113848551A (en) * | 2021-09-24 | 2021-12-28 | 成都理工大学 | Landslide depth inversion method using InSAR lifting rail deformation data |
| CN114596495A (en) * | 2022-03-17 | 2022-06-07 | 湖南科技大学 | Sand slide identification and automatic extraction method based on Sentinel-2A remote sensing image |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112834432A (en) | Landslide thickness inversion method based on remote sensing technology and kinematics principle | |
| CN105930867A (en) | Data mining method based on time series luminous remote sensing image | |
| Perpiñán et al. | Analysis and synthesis of the variability of irradiance and PV power time series with the wavelet transform | |
| Di et al. | Coastal mapping and change detection using high-resolution IKONOS satellite imagery | |
| CN104698462A (en) | Sea surface wind field fusion method for SAR (Synthetic Aperture Radar) based on variation | |
| Fan et al. | Using temporarily coherent point interferometric synthetic aperture radar for land subsidence monitoring in a mining region of western China | |
| Yang et al. | MSFusion: Multistage for remote sensing image spatiotemporal fusion based on texture transformer and convolutional neural network | |
| Mi et al. | Impact of geometric misregistration in GlobeLand30 on land-cover change analysis, a case study in China | |
| Bing et al. | Reconstructing DEM using TLS point cloud data and NURBS surface | |
| An et al. | A review on the research progress of lake water volume estimation methods | |
| Chen et al. | Leveraging Chinese GaoFen-7 imagery for high-resolution building height estimation in multiple cities | |
| CN116994156B (en) | Landslide hidden danger comprehensive remote sensing identification method, system, equipment and medium | |
| CN114265062A (en) | InSAR phase unwrapping method based on phase gradient estimation network | |
| CN102945550A (en) | Gaussian scale space-based method for constructing semantics of remote-sensing image | |
| Liang et al. | Surface slip distribution and earthquake rupture model of the Fuyun fault, China, based on high-resolution topographic data | |
| Xu et al. | Methods for the construction of DEMs of artificial slopes considering morphological features and semantic information | |
| Wang et al. | A New Remote Sensing Change Detection Data Augmentation Method based on Mosaic Simulation and Haze Image Simulation | |
| He et al. | Robust Estimation of Landslide Displacement from Multi-temporal UAV Photogrammetry-Derived Point Clouds | |
| Mu et al. | Canopy lidar point cloud data k-means clustering watershed segmentation method | |
| Ji et al. | Coupling physical model and deep learning for Near real-time wildfire detection | |
| Yu et al. | Ice flow velocity mapping in Greenland using historical images from 1960s to 1980s: Scheme design | |
| Wu et al. | Geomorphological change detection using an integrated method: A case study on the Taan River watershed, Taiwan | |
| Nico et al. | Tools for the real time visualization and analysis of Ground-based SAR data: application to the monitoring of landslides | |
| Lei et al. | Research and application of environmental conservation monitoring based on multi-scale remote sensing image technology | |
| Li et al. | DIFFERENTIAL SAR TOMOGRAPHY OF LARGE-SCALE WATER CONSERVANCY PROJECTS UNDER STEEP TERRAIN–THE CASE STUDY OF LAXIWA HYDROPOWER STATION |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210525 |