CN110930064A - Method for extracting space-time probability of Mars dust storm and evaluating landing safety - Google Patents

Method for extracting space-time probability of Mars dust storm and evaluating landing safety Download PDF

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CN110930064A
CN110930064A CN201911248745.9A CN201911248745A CN110930064A CN 110930064 A CN110930064 A CN 110930064A CN 201911248745 A CN201911248745 A CN 201911248745A CN 110930064 A CN110930064 A CN 110930064A
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李勃
凌宗成
李晨帆
姚佩雯
张江
陈剑
曹海军
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Abstract

The invention discloses a method for extracting space-time probability of Mars dust storm and evaluating landing safety, which comprises the following steps: step 1, identifying a dust storm object based on RGB (red, green and blue) color remote sensing images; step 2, analyzing the time probability of the Mars dust storm; step 3, analyzing the space probability of the Mars dust storm; and 4, evaluating the safety of the Mars landing area. The invention has the advantages that: the Mars dust storm object can be accurately identified and the area extraction is carried out; the problem that the regularity of repeated occurrence of dust storms in a plurality of Mars years is neglected in the prior art is solved; the problem that the characteristics of the dust storm space distribution rule are ignored in the prior art is solved; the appropriate landing time and safe area are selected for the Mars mission.

Description

Method for extracting space-time probability of Mars dust storm and evaluating landing safety
Technical Field
The invention relates to the technical field of planet remote sensing and planet meteorology, in particular to a method for extracting space-time probability of Mars dust storm and evaluating landing safety based on remote sensing images.
Background
Mars is the most similar star in the solar system to the Earth. The likelihood of a spark is greatest if the solar system is extraterrestrial. Remote sensing and in-place detection of mars have profound influence on water source and life trace search, and development of mars detection tasks has great strategic significance on aspects of scientific and technological, economic and social development and the like of China.
Since the 60's of the 20 th century, several Mars landing probes were launched in tandem in the United states and the former Soviet Union. The early landing detector does not consider weather, terrain and other relevant factors, so that the landing success rate is very low. The first lander, Mars No. 2, is engulfed by global storm when landing, and Mars No. 3, also causes the destruction of the communication system because of the storm. The 'courage number' and 'opportunity number' landing time of the American space navigation agency in 2004 are in summer in the southern hemisphere, and when a larger dust storm than expected is encountered, the landing places are respectively 10.1km and 24.6km away from the center of the preselected landing ellipse. In 2018, in 6 months, Mars ' global dust storm causes the ' opportunity number ' to lose contact with the ground. Therefore, the probability of occurrence of a dust storm in a Mars pre-selection landing area is related to whether a landing task can be successful and influences the landing precision and the subsequent normal operation condition of the detector. The detection and research of the climate law and the topographic features of mars is continuous, but the dust storm is still a hotspot and a difficulty of research. The united states space agency and the national space aviation agency are both expected to develop new mars detection missions in 2020. The landing zone device in China is expected to realize the soft landing of the surface of the mars and the patrol of the mars vehicle in 2021. These mars missions all require pre-research for landing zone storm occurrence probability analysis and safety evaluation.
The implementation and results of the previous mars surface landing mission have three problems:
(1) the predecessors calculate the dust storm time probability only considering the average dust storm area percentage of Mars days, but not considering the repeated occurrence probability of dust storms among Mars years. The two Mars days are assumed, the two Mars days have dust storm every year, and the coverage area of the Mars days is small; the latter has only once a year, but the coverage area is large. The latter calculated dust storm probability is greater than the former, which is clearly unreasonable.
(2) The predecessors only consider the time probability of the occurrence of the storm, neglect the spatial probability and distribution characteristics of the storm in the preselected landing area, and need to consider the safe landing position and area in the preselected landing area.
(3) The appropriate time and the safe landing area of the Mars landing task are evaluated and selected without considering the space-time distribution rule of the dust storm of the preselected landing area, so that the safety guarantee is provided for the Mars vehicle to follow-up patrol the Mars surface.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for extracting space-time probability of a Mars dust storm and evaluating landing safety, and solves the defects in the prior art.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a method for extracting space-time probability of Mars dust storm and evaluating landing safety comprises the following steps:
step 1, identifying a pre-selected landing area storm;
the data used by the invention is a mars surface remote sensing image shot by a mars orbit detector, which is an RGB color remote sensing image consisting of Red (Red, R), Green (Green, G) and Blue (Blue, B) wave bands, wherein the wavelength range of the R wave band is 580-525 nm, the wavelength range of the G wave band is 505-525nm, and the wavelength range of the B wave band is 400-450 nm. Different ground objects on the surface of the mars have different degrees of absorption and reflection of sunlight, sand and dust in the RGB color remote sensing image usually appear yellow, exposed rock is black, and cloud (water vapor) is white. Mars dust storm and cloud (water vapor) are often associated and have similar colors, which affect each other, so that the B and R bands need to be distinguished. The cloud (water vapor) has strong absorption capacity to red light, weak reflection at the R wave band and dark color. The reflectivity of sand and dust in a dust storm is enhanced as the wavelength is lengthened, and the red light absorption capacity is weaker than the blue light absorption capacity, so that the color tone is bright in an R wave band and is darker than cloud (water vapor) in a B wave band. In addition, the dust storm generally has special texture characteristics such as feather shape, pebble shape and the like, and the Mars dust storm with a special form can be found by comparing a plurality of RGB color remote sensing images of continuous Mars days. The dust storm can be identified and extracted by combining the two points. The specific steps for identifying the dust storm object based on the RGB color remote sensing image are as follows:
the input RGB color remote sensing image set is I (I, j), the space range covers the preselected landing area, and the time interval is one Mars day. One mars year MY is the time taken by mars to orbit the sun for one week, assuming that there are n years of RGB color remote sensing images (i ═ 1,2, …, n). One Mars day Ls represents that the included angle from Mars to the sun changes to 1 degree, the included angle from the spring equinox of the northern hemisphere corresponds to 0 degree, the spring equinox, the autumn equinox and the winter equinox are respectively 90 degrees, 180 degrees and 270 degrees, the sun longitude represents the Mars seasonal change, and j is 1,2, …,360 (unit:degrees). The subimages corresponding to the red, green and blue bands are I (I, j)R，I(i,j)GAnd I (I, j)B. Taking the RGB remote-sensing color image I (I, j) of the jth mars day of the ith mars year as an example to identify a dust storm object:
(1) and (5) extracting a difference region. On the RGB remote sensing image, the ground feature presents consistency on the image in a short period, and the obvious change area can be regarded as a possible dust storm area. And identifying the difference and the change between the ground objects of the preselected landing area in the three continuous Mars remote sensing images I (I, j-1), I (I, j), I (I, j +1) of the ith Mars year, extracting a change area and vectorizing the change area into a polygonal object. Identified polygon object set D0(i,j,Id0) Is represented by (Id)0Representing polygon object number (Id)0＝1,2,…,m0ij)。
(2) Storm and cloud (water vapor) differentiation. Comparing R waveband subimage I (I, j) R and B waveband subimage I (I, j) of RGB color remote sensing imageBThe color tone is bright and dark, if the ks th polygon object D (I, j, ks) is in I (I, j)RBrighten in the image, and in I (I, j)BIf the image is dark, judging the polygonal object as a dust storm; otherwise, cloud (water vapor) is formed. Thereby generating a storm polygonSet of objects D (i, j, Id), where D is D0Id represents the serial number of the object in the dust storm polygon (Id 1,2, …, m)ij) Calculating the area of each of the dust storm polygon objects generates a set of dust storm polygon areas A (i, j, Id).
Step 2, analyzing the time probability of the Mars dust storm;
the calculation formula of daily average dust storm probability of the Mars landing area consists of two parts. Firstly, calculating the area percentage P of the dust storm object identified by the jth Mars day landing zone in n Mars years1(j) Then calculating the probability P of repeated occurrence of the dust storm in n Mars yearsE1(j) Finally, the results of the two steps are integrated to obtain the time probability P of the occurrence of the daily dust storm of the preselected landing areaT(j)。
(1) Mars day average dust storm coverage area percentage P1(j) And (4) calculating. Assume that the area of the Mars pre-landing zone is ATThe set of the dust storm polygons identified in the ith Mars year and the jth Mars day is D (i, j, Id), and the total number is mijA dust storm polygon (Id ═ 1, 2.., mij) Where the area of the kth storm is A (i, j, k). Dividing the area of the kth storm by the total landing zone area yields the percentage area of the storm polygon
Figure BDA0002308415490000044
Assuming that the total number of the dust storm polygons identified by the same Mars day j in n Mars years is M, the number of the dust storm polygons is
Figure BDA0002308415490000045
Then the sum P of the weighted area percentages of all identified dust storm objects for the jth Mars day of the n Mars years1(j) Comprises the following steps:
Figure BDA0002308415490000041
wherein ,
Figure BDA0002308415490000042
Af(i, j) is the dust of the jth Mars day of the ith Mars yearSum of the percent exposed area.
(2) Probability of repeated occurrence of a storm PE1(j) And (4) calculating. Whether a dust storm occurs in the jth Mars day of the ith Mars year can be identified by Is (i, j). If the storm occurs, Is (i, j) Is 1, otherwise Is (i, j) Is 0. Then the probability of the j-th Mars daily storm of n Mars years occurring repeatedly is:
Figure BDA0002308415490000043
(3) time probability P of occurrence of a dust stormT(j) And (4) calculating. The average dust storm probability of the jth Mars day of the Mars landing zone can be obtained by multiplying the weighted average of the percentage of the covered area of the Mars day dust storm and the repeated occurrence probability of the dust storm:
PT(j)＝P1(j)×PE1(j) (4)
step 3, analyzing the space probability of the Mars dust storm;
the probability of occurrence of a Mars storm is not uniform from area to area, taking into account the non-uniformity of the spatial distribution of Mars storms within the preselected landing zone. The preselected landing zone polygon is therefore divided into a uniformly distributed square grid of length L on a side, p in total, and the grid dataset is grid (g), g being 1,2, …, p. And calculating the annual average probability of the occurrence of the dust storms in different grids, wherein the annual average probability result of the dust storms of all grids is the spatial distribution characteristic of the dust storms in the whole landing area.
Taking the g grid as an example, the annual average occurrence probability of the dust storm is calculated. Suppose that the set of storm polygons identified in the g grid in the ith Mars year is Dg(i,Idg) In total of migIndividual dust storm polygon object (Id)g＝1,2,...,mig) In which the k isgThe area of each dust storm is A (i, k)g). Percentage of dust storm area in ith Mars year g grid
Figure BDA0002308415490000051
Suppose that the total number of the dust storm polygons identified in n Mars years in the grid g is MgThen, then
Figure BDA0002308415490000052
Then the annual weighted storm area percentage for grid g is
Figure BDA0002308415490000053
If the ith Mars year in grid g has a storm, Is (i, g) Is 1, otherwise Is (i, g) Is 0. Probability P of repeated occurrence of dust storm in grid g in n Mars yearsE2(g) Comprises the following steps:
Figure BDA0002308415490000054
annual weighted storm area percentage P of grid g1(g) Multiplying the repeated occurrence probability of grid g dust storms in n Mars years to obtain the annual average dust storm occurrence probability P of the grid gs(g) The concrete formula is as follows:
Ps(g)＝P1(g)×PE2(g) (6)
step 4, evaluating the safety of the Mars landing area;
and according to the time probability and the spatial distribution of the occurrence of the landing area dust storm calculated in the previous step, the tasks of the Mars landing area can be evaluated and selected in time and space. In time, a strong and long-lasting dust storm may interfere with a mission of a mars landing zone, and therefore, a continuous mars day with a low probability of occurrence of the dust storm should be selected as a preferred time period of the landing zone. Suppose the daily average probability threshold of a Mars dust storm is PaMars days exceeding this threshold are not suitable for safe landing of Mars landers. Selecting a set of consecutive Mars days T less than the thresholdSAs a safe landing zone time. Spatially, if the annual average dust storm occurrence probability of a grid is high, the dust storm in the grid will generate a certain image to a lander or a grounded train, and therefore the grid is not suitable for being used as a safety landing area grid for a train mission. Suppose the annual average probability threshold of Mars dust storms for the grid is PbLanding zone grids exceeding this threshold are not suitable for safe landing of Mars lander. Dividing the mesh into security meshes (P) according to the thresholdS(g)<Pb) And an insecure mesh (P)S(g)>Pb) Merging adjacent safety grids to generate a polygon set S of the safety areaS. And then selecting a region with less flat stones and impact pits in the safe region polygon set as a safe region polygon set Ss' of comprehensive morphology and dust storm factors according to topographic data of a Mars pre-selected landing region.
Compared with the prior art, the invention has the advantages that:
according to the reflectivity difference of the dust storm and cloud (water vapor) in red and blue wave bands, the Mars dust storm object can be accurately identified and the area extraction is carried out; the probability of repeated occurrence of the dust storm and the percentage of the coverage area are integrated, the time probability of the dust storm in the preselected landing area is calculated, and the problem that the regularity of the repeated occurrence of the dust storm in a plurality of Mars years is ignored in the prior art is solved; dividing the preselected landing area into regular grids, calculating the annual average probability of the dust storm in the grids, and researching the spatial distribution characteristics and rules of the dust storm, thereby solving the problem that the characteristics of the spatial distribution rules of the dust storm are ignored in the prior art; and finally, evaluating the safety of the preselected landing area according to the space-time distribution rule of the dust storm, and selecting proper landing time and a safety area for the Mars mission.
Drawings
FIG. 1 is a Mars surface topography;
FIG. 2 is an RGB color remote sensing image map;
FIG. 3 is a statistical chart of daily mean storm probability in units of Mars days;
FIG. 4 is a graph of annual mean probability of dust storms in the study area.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings by way of examples.
1 research area and Mars remote sensing image
The example of the present invention focuses on the christmas (Chryse) pre-selected landing zone (black polygon) for the Mars survey mission of 2020 in China, where the area of interest is a 1600km circle at the center of the christmas region with latitude and longitude ranges of about (0 ° -60 ° S, -60 ° E-0 °), as shown in FIG. 1.
The example data source of the invention is RGB color remote sensing image covering the Mars surface, remote sensing image shot by Mars observer number camera on Mars global explorer number, total data of 4 Mars years.
2 Mars landing mission safety analysis
(1) Research area dust storm identification
As shown in fig. 2, (a), (b) and (c) respectively identify a dust storm based on RGB color remote sensing images of different mars days in the study area for corresponding blue band and red band images. As shown in fig. 2(b) and (c), blue and red band images corresponding to RGB color remote sensing images with a mars time MY of 27 and an Ls of 203.6 °. Where white arrows point to the identified storm and black arrows point to the cloud (water vapor). In a 1600km circle in a Cris region, 1172 dust storm objects are identified in 4 Mars year RGB color remote sensing images.
(2) Study area dust storm time probability analysis
The mean daily probability of a dust storm in the study area is calculated according to the mean daily probability formulae (1) to (4), and the result is shown in fig. 3, wherein the abscissa in fig. 3 is Mars day Ls and the ordinate is mean daily probability of a dust storm. The mean daily probability of a storm in the study area is at most 0.21, at Ls 228 °. Among them, the probability of sustained occurrence of a storm between Ls 177 ° -239 ° and Ls 288 ° -5 ° is high, with an average of 0.095 and 0.041, and thus, is not suitable as a landing time for a mars mission. And the time is between Ls 239 ° -288 ° and Ls 5 ° -177 °, which can be used as a suitable landing time for the mars mission.
(3) Spatial probability analysis of dust storm in research area
The study area was divided into grids of 0.5 ° × 0.5 °, and the annual average probability of storm was calculated for each grid by equations (6) to (7), and the results of spatial distribution of annual average probability of storm for the study area are shown in fig. 4. The spatial probability of the dust storm in the research area is between 0 and 10.8 percent. And selecting a flat area without impact pits and with low probability of dust storm space as a safe landing area of the Mars mission by combining contour line data generated by topographic data of the research area.Three suitable landing zones are extracted, as shown by the black dashed boxes in fig. 4, the safe landing zones 1 and 2 are in the west part of the kris area, the landing zone 3 is in the east part of the kris area, and the areas are 65856km respectively2，84744km2And 70242km2The mean values of the spatial probability of a storm are 0.45%, 0.26% and 0.03%, respectively.
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the manner in which the invention is practiced, and it is to be understood that the scope of the invention is not limited to such specifically recited statements and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (1)

1. A method for extracting space-time probability of Mars dust storm and evaluating landing safety is characterized by comprising the following steps:
step 1, identifying a dust storm object based on RGB color remote sensing images, which comprises the following specific steps:
the input RGB color remote sensing image set is I (I, j), the space range covers a preselected landing area, and the time interval is one Mars day; one mars year MY is the time taken by mars to orbit the sun for one week, and it is assumed that there are n years of RGB color remote sensing images (i ═ 1,2, …, n); one Mars day Ls represents that the included angle from Mars to the sun changes to 1 degree, the included angle from the spring equinox of the northern hemisphere corresponds to 0 degree, the spring equinox, the autumn equinox and the winter equinox are respectively 90 degrees, 180 degrees and 270 degrees, the sun longitude represents the Mars seasonal change, and j is 1,2, …,360 (unit: °); the subimages corresponding to the red, green and blue bands are I (I, j)R，I(i,j)GAnd I (I, j)B(ii) a Taking the RGB remote-sensing color image I (I, j) of the jth mars day of the ith mars year as an example to identify a dust storm object:
(1) extracting a difference region; on the RGB color remote sensing image, the ground features present consistency on the image in a short period, and the obvious change area can be regarded as a possible dust storm area; identifying three continuous Mars remote sensing images I (I, j-1) and I (I, j) of the ith Mars year,the difference and the change between ground objects of the preselected landing area in I (I, j +1) are extracted to form a change area, and the change area is vectorized into a polygonal object; identified polygon object set D0(i,j,Id0) Is represented by (Id)0Representing polygon object number (Id)0＝1,2,…,m0ij)；
(2) Dust storm and cloud (water vapor) differentiation; comparing R waveband subimage I (I, j) R and B waveband subimage I (I, j) of RGB color remote sensing imageBThe color tone is bright and dark, if the ks th polygon object D (I, j, ks) is in I (I, j)RBrighten in the image, and in I (I, j)BIf the image is dark, judging the polygonal object as a dust storm; otherwise, cloud (water vapor) is formed; thereby generating a set of storm polygon objects D (i, j, Id), where D is D0Id represents the serial number of the object in the dust storm polygon (Id 1,2, …, m)ij) Calculating the area of each dust storm polygon object to generate a dust storm polygon area set A (i, j, Id);
step 2, analyzing the time probability of the Mars dust storm;
the daily average dust storm probability calculation formula of the Mars landing area consists of two parts; firstly, calculating the area percentage P of the dust storm object identified by the jth Mars day landing zone in n Mars years1(j) Then calculating the probability P of repeated occurrence of the dust storm in n Mars yearsE1(j) Finally, the results of the two steps are integrated to obtain the time probability P of the occurrence of the daily dust storm of the preselected landing areaT(j)；
(1) Mars day average dust storm coverage area percentage P1(j) Calculating; assume that the area of the Mars pre-landing zone is ATThe set of the dust storm polygons identified in the ith Mars year and the jth Mars day is D (i, j, Id), and the total number is mijA dust storm polygon (Id ═ 1, 2.., mij) Wherein the area of the kth storm is A (i, j, k); dividing the area of the kth storm by the total landing zone area yields the percentage area of the storm polygon
Figure FDA0002308415480000021
Assuming that the total number of the dust storm polygons identified by the same Mars day j in n Mars years is M, the number of the dust storm polygons is
Figure FDA0002308415480000022
Then the sum P of the weighted area percentages of all identified dust storm objects for the jth Mars day of the n Mars years1(j) Comprises the following steps:
Figure FDA0002308415480000023
wherein ,
Figure FDA0002308415480000024
Af(i, j) is the sum of the percentage of the dust storm area on the jth Mars day of the ith Mars year;
(2) probability of repeated occurrence of a storm PE1(j) Calculating; whether the dust storm occurs in the jth Mars day of the ith Mars year can be identified by Is (i, j); if the storm occurs, Is (i, j) Is 1, otherwise Is (i, j) Is 0; then the probability of the j-th Mars daily storm of n Mars years occurring repeatedly is:
Figure FDA0002308415480000025
(3) time probability P of occurrence of a dust stormT(j) Calculating; the average dust storm probability of the jth Mars day of the Mars landing zone can be obtained by multiplying the weighted average of the percentage of the covered area of the Mars day dust storm and the repeated occurrence probability of the dust storm:
PT(j)＝P1(j)×PE1(j) (4)
step 3, analyzing the space probability of the Mars dust storm;
dividing a preselected landing zone polygon into uniformly distributed square grids, wherein the side length is L, the number of the squares is p, the grid data set is grid (g), and g is 1,2, … and p; calculating the annual average probability of the occurrence of the dust storms in different grids, wherein the annual average probability result of the dust storms of all the grids is the spatial distribution characteristic of the dust storms in the whole landing area;
take the g grid as an example to calculate the dust storm thereinAnnual average probability of occurrence; suppose that the set of storm polygons identified in the g grid in the ith Mars year is Dg(i,Idg) In total of migIndividual dust storm polygon object (Id)g＝1,2,...,mig) In which the k isgThe area of each dust storm is A (i, k)g) (ii) a Percentage of dust storm area in ith Mars year g grid
Figure FDA0002308415480000031
Suppose that the total number of the dust storm polygons identified in n Mars years in the grid g is MgThen, then
Figure FDA0002308415480000032
Then the annual weighted storm area percentage for grid g is
Figure FDA0002308415480000033
If the ith Mars year in the grid g has a dust storm, Is (i, g) Is 1, otherwise Is (i, g) Is 0; probability P of repeated occurrence of dust storm in grid g in n Mars yearsE2(g) Comprises the following steps:
Figure FDA0002308415480000034
annual weighted storm area percentage P of grid g1(g) Multiplying the repeated occurrence probability of grid g dust storms in n Mars years to obtain the annual average dust storm occurrence probability P of the grid gs(g) The concrete formula is as follows:
Ps(g)＝P1(g)×PE2(g) (6)
step 4, evaluating the safety of the Mars landing area;
according to the time probability and the space distribution of the occurrence of the landing area dust storm calculated in the previous step, the tasks of the Mars landing area can be evaluated and selected in time and space; in time, a dust storm with high intensity and long duration can interfere with tasks of Mars landing areas, so that a section of continuous Mars day with low probability of occurrence of the dust storm is selectedA landing zone preferred time period; suppose the daily average probability threshold of a Mars dust storm is PaMars days exceeding this threshold are not suitable for safe landing of Mars lander; selecting a set of consecutive Mars days T less than the thresholdSAs a safe landing zone time; in space, if the annual average dust storm occurrence probability of one grid is higher, the dust storm in the grid can generate certain images to a lander or a landed mars, so that the grid is not suitable for being used as a safe landing area grid of a mars task; suppose the annual average probability threshold of Mars dust storms for the grid is PbLanding zone grids exceeding the threshold are not suitable for safe landing of a Mars lander; dividing the mesh into security meshes (P) according to the thresholdS(g)<Pb) And an insecure mesh (P)S(g)>Pb) Merging adjacent safety grids to generate a polygon set S of the safety areaS(ii) a And then selecting a region with less flat stones and impact pits in the safe region polygon set as a safe region polygon set Ss' of comprehensive morphology and dust storm factors according to topographic data of a Mars pre-selected landing region.
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