CN110930064B - Mars storm space-time probability extraction and landing safety evaluation method - Google Patents

Mars storm space-time probability extraction and landing safety evaluation method Download PDF

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CN110930064B
CN110930064B CN201911248745.9A CN201911248745A CN110930064B CN 110930064 B CN110930064 B CN 110930064B CN 201911248745 A CN201911248745 A CN 201911248745A CN 110930064 B CN110930064 B CN 110930064B
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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 storm and evaluating landing safety, which comprises the following steps: step 1, identifying a dust storm object based on RGB color remote sensing images; step 2, analyzing the probability of the Mars dust storm time; step 3, analyzing the space probability of the Mars dust storm; and 4, evaluating safety of the Mars landing zone. The invention has the advantages that: the Mars storm object can be accurately identified and the area is extracted; the problem that the prior art ignores the regularity of repeated occurrence of a plurality of Mars year dust storms is solved; the problem that the prior art ignores the characteristic of the space distribution rule of the dust storm is solved; an appropriate landing time and safety zone is selected for the Mars mission.

Description

Mars storm space-time probability extraction and landing safety evaluation method
Technical Field
The invention relates to the technical fields of planetary remote sensing and planetary meteorology, in particular to a method for extracting space-time probability of Mars storm and evaluating landing safety based on remote sensing images.
Background
Mars are the most similar stars in the solar system to the earth. The probability of sparks is greatest if the solar system is living extraterrestrial. The remote sensing and the in-place detection of the Mars have profound effects on water source and vital trace searching, and the development of Mars detection tasks has great strategic significance on aspects of science, technology, economy, social development and the like in China.
From the 60 s of the 20 th century to date, a number of Mars landing detectors have been launched in the united states and so on. Limited to engineering and scientific levels, early landing probes did not take into account weather, terrain, and other relevant factors, and therefore landing success was very low. The first lander, "Mars 2" land is initially engulfed by a global storm, and "Mars 3" also causes the communication system to be destroyed by the storm. The "courage number" and "opportunity number" of the united states space agency in 2004 land for the summer season in the southern hemisphere, encountered larger than expected dust storms, with landing sites offset from the preselected center of the landing ellipse by 10.1km and 24.6km, respectively. In 6 months 2018, the Mars global storm caused "opportunity number" to lose contact with the ground. Thus, the probability of occurrence of a storm in the Mars preselected landing zone is related to whether the landing mission is successful and affects the landing accuracy, as well as the subsequent normal operating conditions of the detector. The climate rules and the morphological characteristics of the Mars are continuously and deeply detected and studied, but the dust storm is still a hot spot and a difficult point of the study. Both the united states space agency and the chinese national space aviation agency are expected to develop new spark detection tasks in 2020. The landing zone of our country is expected to realize soft landing of Mars surface and tour of Mars in 2021. All the spark tasks need to be studied in advance for analysis of probability of occurrence of landing zone dust storm and safety evaluation.
The prior implementation process and result of the Mars surface landing task have the following three problems:
(1) The former calculation of the time probability of the storm only considers the average area percentage of the storm on the day of the Mars, and does not consider the repeated occurrence probability of the storm between Mars. Assuming two Mars days, the former has a storm every year, and the coverage area is smaller; while the latter only appears a storm once a few years, but its coverage area is large. It is clearly unreasonable to calculate the latter dust storm probability to be greater than the former.
(2) The former considers only the time probability of occurrence of a storm, ignoring the spatial probability and distribution characteristics of the storm in the preselected landing zone, and the safe landing position and area in the preselected landing zone need to be considered.
(3) The proper time and safe landing area of the Mars landing task are evaluated and selected without considering the space-time distribution rule of the comprehensive preselected landing area dust storm, and the safety guarantee is provided for the subsequent patrol of the Mars surface of the Mars vehicle.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for extracting space-time probability of Mars storm and evaluating landing safety, which solves the defects in the prior art.
In order to achieve the above object, the present invention adopts the following technical scheme:
a method for extracting space-time probability of Mars dust storm and evaluating landing safety comprises the following steps:
step 1, pre-selecting landing zone dust storm identification;
the data used in the invention is a Mars surface remote sensing image shot by a Mars orbit detector, which is an RGB color remote sensing image composed of Red (Red, R), green (Green, G) and Blue (Blue, B) wave bands, wherein the wavelength range of the R wave band is 580-620nm, the wavelength range of the G wave band is 505-525nm, and the wavelength range of the B wave band is 400-450nm. The sunlight is absorbed and reflected by different ground objects on the surface of the Mars to different degrees, sand and dust usually appear yellow in RGB color remote sensing images, exposed rock is black, and cloud (water vapor) is white. Mars dust and cloud (water vapor) often appear with similar colors and affect each other, so that the two needs to be distinguished by the B and R bands. The cloud (water vapor) has strong red light absorption capacity, weak reflection in the R wave band and dark tone. The reflectance of dust in a dust storm becomes stronger as the wavelength becomes longer, and the red light absorption capacity is weaker than that for blue light, so that the color tone is bright in the R-band and darker than the cloud (water vapor) in the B-band. In addition, the storm generally has special texture characteristics such as feather shape, pebble shape and the like, and the Mars storm with special shape can be found by comparing a plurality of RGB color remote sensing images of continuous Mars days. The two points can be combined to identify and extract the dust storm. 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 zone, and the time interval is one Mars day. One Mars year MY is the time taken for Mars to orbit the sun for one week, assuming a total of n years of RGB color remote sensing images (i=1, 2, …, n). One Mars day Ls represents the change of the solar pinch angle of Mars to the sun as 1 DEG, corresponding to 0 DEG from the spring of northern hemisphere, 90 DEG, 180 DEG and 270 DEG from summer to autumn and from winter to winter respectively, and Mars season is represented by solar longitudeSection change, j=1, 2, …,360 (unit: °). The sub-images corresponding to the three red, green and blue wavebands are I (I, j) R ，I(i,j) G And I (I, j) B . Taking an RGB color remote sensing image I (I, j) of the jth Mars day of the ith Mars year as an example, a storm object is identified:
(1) And extracting a difference region. On RGB color remote sensing images, features show consistency in the images in a short period, and the obvious change areas can be regarded as possible storm areas. And identifying the differences and changes between features of a preselected landing zone in three Mars remote sensing images I (I, j-1), I (I, j) and I (I, j+1) which are continuous in the ith Mars year, extracting a change area and vectorizing the change area into a polygonal object. D for identified polygon object set 0 (i,j,Id 0 ) Representation, where Id 0 Representing the polygon object number (Id) 0 ＝1,2,…,m 0ij )。
(2) Dust storm and cloud (water vapor) discrimination. Comparing R-band sub-image I (I, j) R and B-band sub-image I (I, j) of RGB color remote sensing image B The hue is bright and dark if the ksth polygon object D (I, j, ks) is I (I, j) R Light up in the image, but in I (I, j) B Judging that the polygonal object is a dust storm when the image is dark; otherwise, it is cloud (water vapor). Thereby generating a set of storm polygon objects D (i, j, id), wherein D is D 0 Id represents the storm polygon object number (id=1, 2, …, m ij ) Calculating the area of each storm polygon object generates a storm polygon area set a (i, j, id).
Step 2, analyzing the probability of the Mars dust storm time;
the calculation formula of the daily average dust storm probability of the Mars landing zone consists of two parts. First, calculating the area percentage P of the storm object identified by the jth Mars day landing zone in n Mars years 1 (j) Then calculate the probability P of repeated occurrence of the storm in n Mars years E1 (j) Finally, the results of the two steps are combined to obtain the time probability P of occurrence of the solar storm in the preselected landing zone T (j)。
(1) Mars daily average dust storm coverage area percentage P 1 (j) And (5) calculating. Assuming that the area of the Mars preselected landing zone is A T In the j-th Mars day of the i-th Mars yearThe set of identified storm polygons is D (i, j, id), m being the sum of ij The number of the storm polygon objects (id=1, 2, m ij ) Wherein the area of the kth storm is a (i, j, k). Dividing the area of the kth storm by the area of the entire landing zone to obtain the area percentage of the storm polygon
Figure GDA0004141298920000041
Assuming that the total number of the storm polygons identified by the same Mars day j in n Mars years is M, +.>
Figure GDA0004141298920000042
Then the sum P of the weighted area percentages of all identified storm objects for the j-th Mars day of the n Mars years 1 (j) The method comprises the following steps: />
Figure GDA0004141298920000043
wherein ,
Figure GDA0004141298920000044
A f (i, j) is the sum of the percentage of dust storm areas for the jth Mars day of the ith Mars year.
(2) Probability of repeated occurrence of dust storm P E1 (j) And (5) calculating. Whether a storm occurs in the j-th Mars day of the i-th Mars year can be identified by Is (i, j). If a storm occurs, is (i, j) =1, otherwise Is (i, j) =0. The probability of the j-th Mars daily storm of n Mars years to occur repeatedly is:
Figure GDA0004141298920000045
(3) Probability of time of occurrence of a storm P T (j) And (5) calculating. The average probability of a storm for the j-th Mars day of the Mars landing zone can be multiplied by the weighted average of the Mars day storm coverage area percentages and the probability of a storm recurrence:
P T (j)＝P 1 (j)×P E1 (j) (4)
step 3, analyzing the space probability of the Mars dust storm;
the probability of occurrence of a storm is different in different areas, taking into account the non-uniformity of the spatial distribution of Mars storms within the preselected landing zone. The preselected landing zone polygon is thus divided into uniformly distributed square grids of side length L, p total, grid dataset Grid (g), g=1, 2, …, p. And calculating the annual average probability of occurrence of the dust storm in different grids, wherein the annual average probability result of the dust storm of all grids is the spatial distribution characteristic of the dust storm of the whole landing zone.
Taking the g-th grid as an example, the average occurrence probability of the storm years in the grid is calculated. Suppose that the set of storm polygons identified in the g-grid in the ith Mars year is D g (i,Id g ) Together with m ig Individual storm polygon objects (Id) g ＝1,2,...,m ig ) Wherein the kth g The areas of the dust storms are A (i, k g ). Percentage of storm area in g-grid of the ith Mars year
Figure GDA0004141298920000051
Assume that the total number of recognized storm polygons in n Mars years in grid g is M g Then
Figure GDA0004141298920000052
Then the annual weighted storm area percentage of grid g is
Figure GDA0004141298920000053
If a storm occurs in the ith Mars year in grid g, is (i, g) =1, otherwise Is (i, g) =0. Probability P of repeated occurrence of a storm in grid g in n Mars years E2 (g) The method comprises the following steps:
Figure GDA0004141298920000054
annual weighted storm area percentage P of grid g 1 (g) Multiplying the repeated occurrence probability of the grid g storm in n Mars years to obtain the annual average storm occurrence probability P of the grid g s (g) The specific formula is as follows:
P s (g)＝P 1 (g)×P E2 (g) (6)
step 4, safety evaluation of a Mars landing zone;
the spark landing zone tasks can be evaluated and selected both temporally and spatially based on the time probability and spatial distribution of landing zone dust storms calculated in the previous steps. In time, a strong and long-lasting storm can interfere with the task of the spark landing zone, so that a continuous spark day with a small probability of occurrence of the storm should be selected as the landing zone preferred time period. Assume that the daily average probability threshold of the Mars storm is P a Mars days exceeding this threshold are not suitable for safe landing of Mars landers. Selecting a set T of consecutive Mars days less than the threshold S As a safe landing zone time. Spatially, if the annual average probability of occurrence of a storm in a grid is large, the storm in the grid will generate a certain image against a land device or a landed Mars vehicle, and thus is unsuitable as a safe landing zone grid for Mars tasks. Assume that the annual average probability threshold of Mars storm for grid is P b Landing zone grids exceeding this threshold are not suitable for safe landing of Mars landers. Dividing the mesh into safety meshes (P S (g)<P b ) And unsafe grid (P) S (g)>P b ) Merging adjacent safety grids to generate a polygon set S of a safety area S . And then selecting the area with less flat stone and impact pits in the polygon set of the safe area as a polygon set Ss' of the safe area with comprehensive morphology and storm factors according to the topographic data of the Mars preselected landing area.
Compared with the prior art, the invention has the advantages that:
according to the reflectivity difference of the storm and cloud (water vapor) in red and blue wave bands, the Mars storm object can be accurately identified and the area extraction can be carried out; the probability and coverage area percentage of repeated occurrence of the dust storm are synthesized, the time probability of the dust storm in the preselected landing area is calculated, and the problem that the prior art ignores the regularity of repeated occurrence of the dust storm in a plurality of Mars is solved; dividing a preselected landing zone into regular grids, calculating the average probability of a storm year in the grids, researching the spatial distribution characteristics and rules of the storm, and solving the problem that the prior art ignores the spatial distribution rule characteristics of the storm; and finally, carrying out safety evaluation of the preselected landing zone according to the space-time distribution rule of the storm, and selecting proper landing time and safety zone for the Mars task.
Drawings
FIG. 1 is a diagram of a Mars surface topography;
FIG. 2 is a diagram of RGB color remote sensing images;
FIG. 3 is a statistical graph of daily average dust storm probability in Mars days;
fig. 4 is a graph of the average probability of a year of a storm within a study area.
Detailed Description
The invention will be described in further detail below with reference to the accompanying drawings and by way of examples in order to make the objects, technical solutions and advantages of the invention more apparent.
1 research area and Mars remote sensing image
The example of the invention focuses on the Kryse (black polygon) pre-selected landing zone (black polygon) of Mars detection task in China 2020, the research zone is a circle of 1600km in the center of the Kryss zone, and the longitude and latitude range is about (0-60 DEG S, -60 DEG E-0 DEG), as shown in figure 1.
The data source of the embodiment of the invention is RGB color remote sensing image covering the Mars surface, and is the remote sensing image shot by Mars observer number camera on Mars global explorator number, which is the data of 4 Mars years.
2 Mars landing mission safety analysis
(1) Study area storm identification
As shown in fig. 2, (a), (b) and (c) are respectively used for carrying out storm recognition on RGB color remote sensing images corresponding to blue wave band and red wave band images based on different Mars days in a research area. Fig. 2 (b) and (c) show blue and red band images corresponding to RGB color remote sensing images with a Mars time of my=27 and ls=203.6 °. Wherein white arrows point to identified dust storms and black arrows point to clouds (water vapor). In the 1600km circle of the kris area, 1172 storm objects are totally identified in the RGB color remote sensing images of 4 Mars years.
(2) Analysis of time probability of dust storm in research area
According to the average probability formulas (1) - (4), the average probability of the storm days in the research area is calculated, and the result is shown in fig. 3, wherein the abscissa in fig. 3 is the Mars Ls, and the ordinate is the average probability of the storm days. The average probability of a storm day in the investigation region is at most 0.21, at ls=228°. Wherein, ls=177 ° -239 ° and ls=288 ° -5 ° have a high probability of continuously occurring a storm, and the average values are 0.095 and 0.041, and thus are not suitable as landing time for a Mars mission. And the time is between ls=239 ° -288 ° and ls=5 ° -177 °, and can be used as a suitable landing time for a spark task.
(3) Spatial probability analysis of a storm in a research area
The study area is divided into grids of 0.5 degrees multiplied by 0.5 degrees, the average probability of the storm years of each grid is calculated by formulas (6) - (7), and the spatial distribution result of the average probability of the storm years of the study area is shown in figure 4. The spatial probability of the dust storm in the investigation region is between 0% and 10.8%. In combination with contour data generated by the topographic data of the research area, a flat area without an impact pit and with low probability of a dust storm space is selected as a safe landing area of a Mars task. Three suitable landing zones are extracted altogether, as shown in the black dashed box of fig. 4, the safe landing zones 1 and 2 are in the west of the kris zone, the landing zone 3 is in the east of the kris zone, and the areas are 65856km respectively 2 ，84744km 2 And 70242km 2 The average values of the spatial probabilities of the dust storms are 0.45%,0.26% and 0.03%, respectively.
Those of ordinary skill in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the practice of the invention and that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.

Claims (1)

1. A method for extracting space-time probability of Mars dust storm and evaluating landing safety, which 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 the preselected landing zone, and the time interval is one Mars day; one Mars year MY is the time taken for Mars to orbit around the sun for one week, assuming a total of n years of RGB color remote sensing images (i=1, 2, …, n); one Mars day Ls represents a Mars to sun change in pinch angle of 1 °, corresponding to 0 °, summer to autumn and winter to 90 °, 180 ° and 270 °, respectively, starting from northern hemisphere spring fraction, and the Mars change in season represented by solar longitude, j=1, 2, …,360 (unit: °); the sub-images corresponding to the three red, green and blue wavebands are I (I, j) R ，I(i,j) G And I (I, j) B The method comprises the steps of carrying out a first treatment on the surface of the Taking RGB color remote sensing images of the jth Mars of the ith Mars as an example, a storm object is identified:
(1) Extracting a difference region; on RGB color remote sensing images, the features show consistency in the images in a short period, and obvious change areas can be regarded as possible storm areas; identifying differences and changes among features of preselected landing areas in three Mars remote sensing images I (I, j-1), I (I, j) and I (I, j+1) which are continuous in the ith Mars year, extracting a change area and vectorizing the change area into a polygonal object; d for identified polygon object set 0 (i,j,Id 0 ) Representation, where Id 0 Representing the polygon object number (Id) 0 ＝1,2,…,m 0ij )；
(2) Dust storm and cloud discrimination; r band sub-image I (I, j) of RGB color remote sensing image is compared R And B-band sub-image I (I, j) B The hue is bright and dark if the ksth polygon object D (I, j, ks) is I (I, j) R Light up in the image, but in I (I, j) B Judging that the polygonal object is a dust storm when the image is dark; otherwise, cloud is formed; thereby generating a set of storm polygon objects D (i, j, id), wherein D is D 0 Id represents a storm polygon pairLike sequence number (id=1, 2, …, m ij ) Calculating the area of each storm polygon object to generate a storm polygon area set A (i, j, id);
step 2, analyzing the probability of the Mars dust storm time;
the calculation formula of the daily average dust storm probability of the Mars landing zone consists of two parts; first, calculating the area percentage P of the storm object identified by the jth Mars day landing zone in n Mars years 1 (j) Then calculate the probability P of repeated occurrence of the storm in n Mars years E1 (j) Finally, the results of the two steps are combined to obtain the time probability P of occurrence of the solar storm in the preselected landing zone T (j)；
(1) Mars daily average dust storm coverage area percentage P 1 (j) Calculating; assuming that the area of the Mars preselected landing zone is A T The set of storm polygons identified in the jth Mars day of the ith Mars year is D (i, j, id), taken together with m ij The number of the storm polygon objects (id=1, 2, m ij ) Wherein the area of the kth storm is a (i, j, k); dividing the area of the kth storm by the area of the entire landing zone to obtain the area percentage of the storm polygon
Figure FDA0004095437920000021
Assuming that the total number of the storm polygons identified by the same Mars day j in n Mars years is M, +.>
Figure FDA0004095437920000022
Then the sum P of the weighted area percentages of all identified storm objects for the j-th Mars day of the n Mars years 1 (j) The method comprises the following steps:
Figure FDA0004095437920000023
wherein ,
Figure FDA0004095437920000024
A f (i, j) is the storm face of the jth Mars day of the ith Mars yearSum of product percentages;
(2) Probability of repeated occurrence of dust storm P E1 (j) Calculating; whether a storm occurs in the jth Mars day of the ith Mars year can be identified by Is (i, j); if a storm occurs, is (i, j) =1, otherwise Is (i, j) =0; the probability of the j-th Mars daily storm of n Mars years to occur repeatedly is:
Figure FDA0004095437920000025
(3) Probability of time of occurrence of a storm P T (j) Calculating; the average probability of a storm for the j-th Mars day of the Mars landing zone can be multiplied by the weighted average of the Mars day storm coverage area percentages and the probability of a storm recurrence:
P T (j)＝P 1 (j)×P E1 (j) (4)
step 3, analyzing the space probability of the Mars dust storm;
dividing the polygon of the preselected landing zone into uniformly distributed square grids, wherein the side length of each square Grid is L, p grids are provided, the Grid data set is Grid (g), and g=1, 2, … and p; calculating the annual average probability of the occurrence of the dust storm in different grids, wherein the annual average probability result of the dust storm of all grids is the spatial distribution characteristic of the dust storm of the whole landing zone;
taking the g-th grid as an example to calculate the average occurrence probability of the storm years in the grid; suppose that the set of storm polygons identified in the g-grid in the ith Mars year is D g (i,Id g ) Together with m ig Individual storm polygon objects (Id) g ＝1,2,...,m ig ) Wherein the kth g The areas of the dust storms are A (i, k g ) The method comprises the steps of carrying out a first treatment on the surface of the Percentage of storm area in g-grid of the ith Mars year
Figure FDA0004095437920000031
Assume that the total number of recognized storm polygons in n Mars years in grid g is M g Then->
Figure FDA0004095437920000032
Then the annual weighted storm area percentage of grid g is +.>
Figure FDA0004095437920000033
If a storm occurs in the ith Mars year in grid g, is (i, g) =1, otherwise Is (i, g) =0; probability P of repeated occurrence of a storm in grid g in n Mars years E2 (g) The method comprises the following steps:
Figure FDA0004095437920000034
annual weighted storm area percentage P of grid g 1 (g) Multiplying the repeated occurrence probability of the grid g storm in n Mars years to obtain the annual average storm occurrence probability P of the grid g s (g) The specific formula is as follows:
P s (g)＝P 1 (g)×P E2 (g) (6)
step 4, safety evaluation of a Mars landing zone;
the task of the Mars landing zone can be evaluated and selected in time and space according to the time probability and the space distribution of the occurrence of the landing zone storm calculated in the previous step; in time, a strong and long-lasting dust storm can interfere with the task of the Mars landing zone, so that a continuous Mars day with small dust storm occurrence probability is selected as the preferable time period of the landing zone; assume that the daily average probability threshold of the Mars storm is P a Mars days exceeding this threshold are not suitable for safe landing of Mars landers; selecting a set T of consecutive Mars days less than the threshold S As a safe landing zone time; spatially, if the annual average probability of occurrence of a storm for a grid is large, the storm in the grid will have a certain influence on the land device or the Mars after landing, and thus is not suitable for a safe landing zone grid for a Mars mission; assume that the annual average probability threshold of Mars storm for grid is P b Landing zone grids exceeding this threshold are not suitable for safe landing of Mars landers; the grid is scored according to the threshold valueIs divided into a safety grid, P S (g)<P b And unsafe grids, P S (g)>P b Merging adjacent safety grids to generate a polygon set S of a safety area S The method comprises the steps of carrying out a first treatment on the surface of the And then selecting the area with less flat stone and impact pits in the polygon set of the safe area as a polygon set Ss' of the safe area with comprehensive morphology and storm factors according to the topographic data of the Mars preselected landing area.
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