阅读说明：本技术 一种海冰灾害的遥感监测评估方法 (Remote sensing monitoring and evaluating method for sea ice disaster ) 是由 胡静雯 王其翔 吴志宏 董文隆 张聿柏 宿子琪 于 2021-07-13 设计创作，主要内容包括：本发明属于视觉检测技术领域,公开一种海冰灾害的遥感监测评估方法,包括以下步骤：对卫星数据进行预处理；分区进行云检测阈值设置和判识,进行云掩膜处理；利用红绿蓝三通道的遥感观测值生成一幅真彩色合成图；根据极轨卫星和海洋卫星的L1级遥感监测数据,通过对原始DN值进行波段运算,获取可见光通道的反射率和红外通道的亮温值,结合NDSI的计算公式,获得NDSI阈值,得到初步的海冰信息二值化结果；根据初步的海冰信息二值化结果,多卫星判识文件融合后,手动勾选生成海冰包络线,计算海冰密集度、覆盖面积、覆盖范围、外缘线距离海岸距离；将海冰外缘线导出,导入,进行多时次过程分析。本发明可以及时预警,采取防控策略,降低灾害带来的损失。(The invention belongs to the technical field of visual detection, and discloses a remote sensing monitoring and evaluating method for sea ice disasters, which comprises the following steps: preprocessing satellite data; carrying out cloud detection threshold setting and judgment on the subareas, and carrying out cloud mask processing; generating a true color synthetic image by using the remote sensing observation value of red, green and blue channels; according to L1-level remote sensing monitoring data of polar orbit satellites and ocean satellites, performing band operation on an original DN value to obtain the reflectivity of a visible light channel and the brightness temperature value of an infrared channel, and combining with a calculation formula of NDSI to obtain an NDSI threshold value to obtain a preliminary sea ice information binarization result; according to the preliminary sea ice information binarization result, after the multi-satellite identification files are fused, manually selecting to generate a sea ice envelope line, and calculating sea ice density, coverage area, coverage range and distance from an outer edge line to a coast; and (4) leading out and leading in the sea ice outer edge line, and carrying out multiple time process analysis. The invention can early warn in time, and reduce the loss caused by disasters by adopting a prevention and control strategy.)

1. A remote sensing monitoring and evaluation method for sea ice disasters is characterized by comprising the following steps:

preprocessing satellite data, including data projection conversion, multiple data splicing and atmospheric correction, and synthesizing into a color image and outputting the color image;

step (b), carrying out cloud detection threshold setting and judgment on the partitions, and carrying out cloud mask processing;

step (c), generating a true color synthetic image by using the remote sensing observation value of red, green and blue channels;

step (d), according to L1 level remote sensing monitoring data of polar orbit satellites and ocean satellites, performing band operation on an original DN value to obtain the reflectivity of a visible light channel and the brightness temperature value of an infrared channel, and combining with a calculation formula of NDSI to obtain an NDSI threshold value to obtain a preliminary sea ice information binarization result;

step (e), according to the preliminary sea ice information binarization result, after the multi-satellite identification files are fused, manually selecting to generate a sea ice envelope line, and calculating sea ice density, coverage area, coverage range and distance from the outer edge line to the coast;

and (f) leading out and leading in the sea ice outer edge line, and carrying out multiple time process analysis.

2. The remote sensing monitoring and evaluation method for sea ice disasters according to claim 1,

the atmospheric correction comprises the following specific steps:

firstly, reading L1 level data of a multi-channel scanning imaging radiometer;

then, atmospheric correction is carried out on the original reflectivity data, and finally a color synthetic image is output; in the atmosphere correction process, highly correcting the standard atmospheric molecular scattering optical thickness by using the geographic longitude and latitude information of the pixel to obtain the aerosol optical thickness of the pixel; the optical thickness, the solar zenith angle and the satellite zenith angle are jointly calculated to obtain the atmospheric transmittance and the atmospheric absorptivity, the optical thickness is calculated to obtain the atmospheric albedo S, and the optical thickness and the observation geometry solar zenith angle, the satellite zenith angle and the relative azimuth angle of the pixel are jointly calculated to obtain the atmospheric Rayleigh scattering reflectivity; then, correcting the influence of atmospheric scattering on the pixel by utilizing the three quantities of the atmospheric transmittance and the absorption rate, the atmospheric albedo and the atmospheric Rayleigh scattering reflectivity to obtain the ground reflectivity; the corrected reflectivity data is synthesized into a color image and output.

3. The remote sensing monitoring and evaluation method for sea ice disasters according to claim 1,

the specific steps of cloud detection threshold setting, identification and cloud mask processing comprise:

the setting of the cloud detection threshold value adopts different threshold value judging methods according to different satellite data, and a single-channel threshold value or a multi-channel threshold value method is respectively adopted for judging; for MODIS data, the reflectivity of channels of 0.620-0.670 is more than 15, the brightness temperature of channels of 10.78-11.28 is less than 285, and the reflectivity difference of channels of 0.841-0.876 and channels of 0.620-0.670 is less than 0 bit with cloud; for the FY3D MERSI data, clouds of 0.47 channel reflectivities greater than 20 and 10.8 channel light temperatures less than 285 and 0.55 and 0.47 channel reflectivities different by less than 0 were used; the threshold parameters for HY1C CZI data are cloud with 0.65 channel value less than 0.2 and 0.46 channel value less than 0.12; for HY1C COCTS data, cloud data with 0.49 channel value greater than 0.23 and 0.52 channel value greater than 0.25 are used;

and carrying out cloud elimination on the judged cloud pixels and carrying out mask elimination.

4. The remote sensing monitoring and evaluation method for sea ice disasters according to claim 1,

and selecting an interested area based on a visual interpretation lasso by utilizing the channel identification, and setting an NDSI threshold in different areas.

5. The remote sensing monitoring and evaluation method for sea ice disasters according to claim 4,

the specific process of setting the NDSI threshold by regions is as follows: and selecting an interested area based on a visual interpretation lasso, selecting all sea ice pixels in the area, acquiring the minimum NDSI value in all the sea ice pixels, namely the NDSI threshold value of the segmented sea ice and seawater, and segmenting the sea ice and the seawater in other areas according to the same method.

6. The remote sensing monitoring and evaluation method for sea ice disasters according to claim 1,

the NDSI threshold is obtained by calculating the snow differential index of the non-masked areas.

7. The remote sensing monitoring and evaluation method for sea ice disasters according to claim 1,

the sea ice density calculation steps are as follows:

step (e1), based on the sea ice identification result, calculating the sea ice density of the sea ice pixels pixel by pixel;

step (e2), taking 51 × 51 windows with sea ice pixels as the center, counting the number of the sea ices in the windows, and considering the sea ice density of the pixels to be 0 when the number of the sea ice pixels in the windows is less than or equal to 10;

and (e3) when the number of the sea ice pixels in the window is more than 10, counting the probability density function of the sea ice pixels, wherein the minimum value is 0, the number of the histograms is 121, the interval is 0.02, performing 5-step sliding average on the histograms, calculating the reflectivity corresponding to the maximum value of the probability density function after sliding to serve as the reflectivity value of the pure sea ice pixels, and then calculating the sea ice density of the pixels.

8. The remote sensing monitoring and evaluation method for sea ice disasters according to claim 7,

the sea ice density is calculated as follows:

IceCon＝(R-R_water)/(R_ice-R_water)

wherein, IceCon is sea ice density, R is MODIS fourth band/FY 3D sixth band reflectivity, and R _ ice and R _ water are reflectivities of pure ice and pure water pixels.

9. The remote sensing monitoring and evaluation method for sea ice disasters according to claim 1,

the multi-satellite identification file fusion method comprises the following steps: and aiming at the judgment results of different loads, performing fusion of a maximum judgment result.

Technical Field

The invention relates to the technical field of visual detection, in particular to a remote sensing monitoring and evaluating method for sea ice disasters.

Background

Sea ice appears in the coastal sea area in the northern part of the yellow sea in winter every year, and the ice condition is different due to different climatic conditions. In the extremely cold winter, ports and channels along the coast in the north of the yellow sea can be blocked by sea ice, and the offshore production is forced to stop, thereby causing serious economic loss. Regarding the observation means of sea ice, at present, a fixed observation station, coastal sea ice investigation and ice breaker sailing investigation are mainly relied on to obtain certain sea ice information, but the prior art means is difficult to obtain real-time and large-area observation data, which are important bases for early warning and forecasting of sea ice disasters.

The sea ice identification monitoring method based on satellite remote sensing in the prior art is low in precision, and the marine disaster monitoring system is low in automation degree.

Disclosure of Invention

The embodiment of the invention provides a remote sensing monitoring and evaluating method for sea ice disasters, which aims to solve the problems of low precision and low automation degree of a sea ice identification monitoring method in the prior art. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

According to the first aspect of the embodiment of the invention, a remote sensing monitoring and evaluating method for sea ice disasters is provided.

In some optional embodiments, a remote sensing monitoring and evaluation method for sea ice disasters comprises the following steps:

preprocessing satellite data, including data projection conversion, multiple data splicing and atmospheric correction, and synthesizing into a color image and outputting the color image;

step (b), carrying out cloud detection threshold setting and judgment on the partitions, and carrying out cloud mask processing;

step (c), generating a true color synthetic image by using the remote sensing observation value of red, green and blue channels;

step (d), according to L1 level remote sensing monitoring data of polar orbit satellites and ocean satellites, performing band operation on an original DN value to obtain the reflectivity of a visible light channel and the brightness temperature value of an infrared channel, and combining with a calculation formula of NDSI to obtain an NDSI threshold value to obtain a preliminary sea ice information binarization result;

step (e), according to the preliminary sea ice information binarization result, after the multi-satellite identification files are fused, manually selecting to generate a sea ice envelope line, and calculating sea ice density, coverage area, coverage range and distance from the outer edge line to the coast;

and (f) leading out and leading in the sea ice outer edge line, and carrying out multiple time process analysis.

Optionally, the atmospheric correction comprises the following specific steps:

firstly, reading L1 level data of a multi-channel scanning imaging radiometer;

then, atmospheric correction is carried out on the original reflectivity data, and finally a color synthetic image is output; in the atmosphere correction process, highly correcting the standard atmospheric molecular scattering optical thickness by using the geographic longitude and latitude information of the pixel to obtain the aerosol optical thickness of the pixel; the optical thickness, the solar zenith angle and the satellite zenith angle are jointly calculated to obtain the atmospheric transmittance and the atmospheric absorptivity, the optical thickness is calculated to obtain the atmospheric albedo S, and the optical thickness and the observation geometry solar zenith angle, the satellite zenith angle and the relative azimuth angle of the pixel are jointly calculated to obtain the atmospheric Rayleigh scattering reflectivity; then, correcting the influence of atmospheric scattering on the pixel by utilizing the three quantities of the atmospheric transmittance and the absorption rate, the atmospheric albedo and the atmospheric Rayleigh scattering reflectivity to obtain the ground reflectivity; the corrected reflectivity data is synthesized into a color image and output.

Optionally, the specific steps of the cloud detection threshold setting, the judging and the cloud mask processing include:

the setting of the cloud detection threshold value adopts different threshold value judging methods according to different satellite data, and a single-channel threshold value or a multi-channel threshold value method is respectively adopted for judging; for MODIS data, the reflectivity of channels of 0.620-0.670 is more than 15, the brightness temperature of channels of 10.78-11.28 is less than 285, and the reflectivity difference of channels of 0.841-0.876 and channels of 0.620-0.670 is less than 0 bit with cloud; for the FY3D MERSI data, clouds of 0.47 channel reflectivities greater than 20 and 10.8 channel light temperatures less than 285 and 0.55 and 0.47 channel reflectivities different by less than 0 were used; the threshold parameters for HY1C CZI data are cloud with 0.65 channel value less than 0.2 and 0.46 channel value less than 0.12; for HY1C COCTS data, cloud data with 0.49 channel value greater than 0.23 and 0.52 channel value greater than 0.25 are used;

and carrying out cloud elimination on the judged cloud pixels and carrying out mask elimination.

Optionally, the channel identification is used to select the region of interest based on the visual interpretation lasso, and the NDSI threshold is set in different regions.

Optionally, the specific process of setting the NDSI threshold in different areas is as follows: and selecting an interested area based on a visual interpretation lasso, selecting all sea ice pixels in the area, acquiring the minimum NDSI value in all the sea ice pixels, namely the NDSI threshold value of the segmented sea ice and seawater, and segmenting the sea ice and the seawater in other areas according to the same method.

Optionally, the NDSI threshold is obtained by calculating a snow differential index for non-masked areas.

Optionally, the sea ice density calculation step is as follows:

step (e1), based on the sea ice identification result, calculating the sea ice density of the sea ice pixels pixel by pixel;

step (e2), taking 51 × 51 windows with sea ice pixels as the center, counting the number of the sea ices in the windows, and considering the sea ice density of the pixels to be 0 when the number of the sea ice pixels in the windows is less than or equal to 10;

and (e3) when the number of the sea ice pixels in the window is more than 10, counting the probability density function of the sea ice pixels, wherein the minimum value is 0, the number of the histograms is 121, the interval is 0.02, performing 5-step sliding average on the histograms, calculating the reflectivity corresponding to the maximum value of the probability density function after sliding to serve as the reflectivity value of the pure sea ice pixels, and then calculating the sea ice density of the pixels.

Alternatively, the formula for calculating sea ice density is as follows:

IceCon＝(R-R_water)/(R_ice-R_water)

wherein, IceCon is sea ice density, R is MODIS fourth band/FY 3D sixth band reflectivity, and R _ ice and R _ water are reflectivities of pure ice and pure water pixels.

Optionally, the fusing of the multi-satellite identification files comprises the following steps: and aiming at the judgment results of different loads, performing fusion of a maximum judgment result.

The technical scheme provided by the embodiment of the invention has the following beneficial effects:

the embodiment of the invention adopts a plurality of remote sensing satellites at home and abroad to dynamically monitor the sea ice, can early warn in time, adopts a prevention and control strategy, reduces the loss caused by disasters, and can play an important role in disaster monitoring and ecological risk evaluation in coastal areas.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.

Fig. 1 is a schematic overall structure diagram illustrating a remote sensing monitoring and evaluation method for sea ice disasters according to an exemplary embodiment.

Detailed Description

The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the embodiments herein includes the full ambit of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like, herein are used solely to distinguish one element from another without requiring or implying any actual such relationship or order between such elements. In practice, a first element can also be referred to as a second element, and vice versa. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a structure, device or apparatus that comprises the element. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein, as used herein, are defined as orientations or positional relationships based on the orientation or positional relationship shown in the drawings, and are used for convenience in describing and simplifying the description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may include, for example, mechanical or electrical connections, communications between two elements, direct connections, and indirect connections via intermediary media, where the specific meaning of the terms is understood by those skilled in the art as appropriate.

Herein, the term "plurality" means two or more, unless otherwise specified.

Herein, the character "/" indicates that the preceding and following objects are in an "or" relationship. For example, A/B represents: a or B.

Herein, the term "and/or" is an associative relationship describing objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.

Fig. 1 shows an alternative embodiment of the remote sensing monitoring evaluation method for sea ice disasters of the invention.

In this alternative embodiment, the method comprises the steps of:

and (a) preprocessing satellite data, including data projection conversion, multi-data splicing and atmospheric correction. Wherein the data is projection-converted and only the specified area can be selected for projection. And splicing a plurality of pieces of data, and realizing projection splicing of data for multiple times, so that the attention area can be better judged. And performing atmospheric correction on the data to improve the accuracy of judgment.

The atmospheric correction method comprises the following specific steps:

firstly, reading in L1 level data of a multichannel scanning imaging radiometer, then carrying out atmospheric correction on the original reflectivity data, and finally outputting a color synthetic graph. In the atmosphere correction process, the standard atmospheric molecular scattering optical thickness is highly corrected by using the geographic longitude and latitude information (lat, lon) of the pixel, and the aerosol optical thickness tau of the pixel is obtained. Optical thickness and solar zenith angle mu_sSatellite zenith angle mu_vThe atmospheric transmittance (T (mu)) is obtained by joint calculation_s)，T(μ_v) And atmospheric absorption rate T_gCalculating the optical thickness to obtain the atmospheric albedo S, the optical thickness and the observed geometric solar zenith angle mu of the pixel_sSatellite zenith angle mu_vAnd calculating the atmospheric Rayleigh scattering reflectivity rho by using the relative azimuth angle delta phi_ray. Then, the influence of atmospheric scattering on the pixel is corrected by utilizing the three quantities of the atmospheric transmittance and the absorption rate, the atmospheric albedo and the atmospheric Rayleigh scattering reflectivity to obtain the ground reflectivity rho_ac. The corrected reflectivity data is synthesized into a color image and output.

Step (b), carrying out cloud detection threshold setting and judgment on the partitions, and carrying out cloud mask processing;

the specific steps of cloud detection threshold setting, identification and cloud mask processing comprise: the setting of the cloud detection threshold value adopts different threshold value judging methods according to different satellite data, and a single-channel threshold value or a multi-channel threshold value method is respectively adopted for judging, wherein for MODIS data, 0.620-0.670 channel reflectivity is more than 15, 10.78-11.28 channel brightness temperature is less than 285, and 0.841-0.876 channel and 0.620-0.670 channel reflectivity difference is less than 0 bit with cloud; for the FY3D MERSI data, clouds of 0.47 channel reflectivities greater than 20 and 10.8 channel light temperatures less than 285 and 0.55 and 0.47 channel reflectivities different by less than 0 were used; the threshold parameters for HY1C CZI data are cloud with 0.65 channel value less than 0.2 and 0.46 channel value less than 0.12; clouds with 0.49 channel values greater than 0.23 and 0.52 channel values greater than 0.25 were used for the HY1C COCTS data. And carrying out cloud elimination on the judged cloud pixels, carrying out mask elimination treatment, and adjusting an independent threshold value of the concerned area.

And (c) performing image enhancement processing on the true color synthetic image. Optionally, a true color synthetic image is generated by using the remote sensing observation value of red, green and blue channels.

And (d) according to L1-level remote sensing monitoring data of polar orbit satellites and ocean satellites, performing band operation on the original DN value to obtain the reflectivity of a visible light channel and the brightness temperature value of an infrared channel, and combining with a calculation formula of NDSI to obtain an NDSI threshold value to obtain a preliminary sea ice information binarization result.

In some embodiments, the NDSI threshold is obtained by calculating a snow differential index for non-masked areas.

The reflectivity of the sea ice and the water body in visible light and near infrared wave bands is obviously different, and the reflectivity of the sea ice is higher than that of the sea water; in a thermal infrared band (3-14 um), the temperature of the sea ice is lower than that of the sea water, and small change of the temperature can cause large change of radiation, so that the sea ice can be identified through thermal infrared radiation difference between the ice surface and the water surface.

Alternatively, for MODIS data, where Snow covered sea ice has similar reflectivity to Snow, Snow covered sea ice regions can be identified using methods for identifying Snow, defining Normalized Snow index (NDSI):

NDSI＝(R4-R6)/(R4+R6)

wherein, R4 and R6 are respectively the reflectivity of MODIS wave band 4 (0.58-0.68 um) and wave band 6 (1.55-1.64 um).

For the sea ice with accumulated snow, the snow index can be utilized to distinguish the snow, and when NDSI is larger than NDSITH, the sea ice is identified as a sea ice area, wherein NDSITH is a threshold value for identifying the snow ice, and is a value capable of filtering the sea ice, seawater and mist, and the value range of NDSITH is as follows: -0.2 to 0.8.

When the sea ice has no accumulated snow cover or the thickness of the sea ice is less than 10cm, the reflectivity of the sea ice is low, the sea ice cannot be detected by using the NDSI index, or no visible light reflection information exists, the sea ice identification only can utilize temperature information, and the embodiment of the invention utilizes an IST (ice Surface temperature) algorithm of EOS/MODIS infrared splitting window wave band calculation ice Surface temperature:

IST＝a+bT11+c(T11-T12)+d(T11-T12)(secq-1)

wherein, T11 and T12 are the brightness temperature of the wave band 31(11um) and the wave band 32(12um) of MODIS respectively, q is the scanning angle calculated from nadir point, and a, b, c and d are regression coefficients.

Since the temperature of seawater is generally higher than 271.4K, it can be judged as sea ice when IST < 271.4.

In the ocean, the types of ground objects are less, mainly sea ice and sea water, and the spectral characteristics of clouds and the sea ice are similar, so in the process of identifying the sea ice coverage, the clouds and the sea water are main factors for interfering the sea ice identification, the cloud and sea water information is required to be removed when the sea ice coverage is determined, and the rest is the sea ice.

Optionally, for the MERSI data, according to reflection spectrum characteristics of sea ice, cloud and sea water in visible light and near infrared bands, respectively taking the ratio of reflectivity of a MERSI channel 1 and a channel 2 and a normalized ice and snow index (NDSI) as discrimination indexes of the sea water and the cloud, successively generating a sea water mask and a cloud mask through image segmentation, and finally removing sea water and cloud information in the image. Wherein the normalized snow index (NDSI) is defined as:

NDSI＝(R2-R6)/(R2+R6)

where R2, R6 are the reflectivities of the MERSI channel 2 and channel 6, respectively. Near 1.6um, the cloud reflectivity is different greatly for sea ice and sea water, and NDSI can highlight the reflection characteristic difference between the cloud and the sea ice and sea water and eliminate the influence of atmospheric radiation and instruments to a certain extent.

For the COCTS data, 5 and 6 channels are selected for cloud detection. For a pixel on the satellite image, if the albedo of the COCTS 5 channel is greater than 23% or the albedo of the COCTS 6 channel is greater than 25%, the pixel is considered as a cloud-covered point.

And for the CZI data, 1 channel and 3 channels are selected for cloud detection. For a pixel on a satellite image, CZI is considered a cloud covered point if its 1 and 3 channel albedos are less than 26% and 23%.

After cloud detection, ice water identification is carried out on the sea area without cloud coverage. For the COCTS data, the albedo of the channel 5 can be adopted for ice water identification, and for an image element which is not covered by the cloud on the satellite image, if the albedo of the COCTS channel 5 is greater than 14%, the image element is considered to be ice.

For the CZI data, the albedo of channels 2 and 4 can be used for ice water identification. And for an image element which is not covered by the cloud on the satellite image, judging as ice if the 2-channel albedo of the image element is greater than 12.6% or the 4-channel albedo of the image element is greater than 5.8%.

In other embodiments, the NDSI threshold is set in zones using a channel determination to select a zone of interest based on a visual interpretation lasso.

Optionally, the step of setting the NDSI threshold in different regions is to distinguish sea ice from sea water, select sea ice in a certain region, compare the NDSI values of sea ice in the region and sea water outside the region, find a critical value, set the critical value as the NDSI threshold, and divide sea ice and sea water by the threshold. Optionally, the specific process of setting the NDSI threshold in different areas is as follows: and selecting an interested area based on a visual interpretation lasso, selecting all sea ice pixels in the area, acquiring the minimum NDSI value in all the sea ice pixels, namely the NDSI threshold value of the segmented sea ice and seawater, and segmenting the sea ice and the seawater in other areas according to the same method.

And (e) according to the preliminary sea ice information binarization result, after the multi-satellite identification files are fused, manually selecting to generate a sea ice envelope line, and calculating the sea ice density, the coverage area, the coverage range and the distance from the outer edge line to the coast.

The sea ice concentration steps are as follows:

step (e1), based on the sea ice identification result, calculating the sea ice density of the sea ice pixels pixel by pixel;

step (e2), taking 51 × 51 windows with sea ice pixels as the center, counting the number of the sea ices in the windows, and considering the sea ice density of the pixels to be 0 when the number of the sea ice pixels in the windows is less than or equal to 10;

and (e3) when the number of the sea ice pixels in the window is more than 10, counting the probability density function of the sea ice pixels, wherein the minimum value is 0, the number of the histograms is 121, the interval is 0.02, performing 5-step sliding average on the histograms, calculating the reflectivity corresponding to the maximum value of the probability density function after sliding to serve as the reflectivity value of the pure sea ice pixels, and then calculating the sea ice density of the pixels.

The sea ice density is calculated as follows:

IceCon＝(R-R_water)/(R_ice-R_water)

wherein, IceCon is sea ice density, R is MODIS fourth band/FY 3D sixth band reflectivity, and R _ ice and R _ water are reflectivities of pure ice and pure water pixels.

Optionally, the fusing of the multi-satellite identification files comprises the following steps: aiming at the identification results of different loads, performing fusion of a maximum identification result;

optionally, the coverage area is: the number of sea ice pixels is the area of the pixels.

Optionally, the distribution area is: the number of sea ice pixels is the area of the pixels.

Optionally, the step of calculating the distance from the outer edge line to the coast is: the number of the pixels at the shortest distance between the outer edge of the sea ice and a specific coast is the pixel resolution.

And (f) leading out and leading in the sea ice outer edge line, and carrying out multiple time process analysis. Optionally, the sea ice coverage area and the distribution area in nearly ten days are counted, and the area change trend analysis is performed.

The embodiment of the invention adopts a plurality of remote sensing satellites at home and abroad to dynamically monitor the sea ice phenomenon, such as AQUA/MODIS, TERRA/MODIS, HY1C/COCTS, HY1C/CZI, FY3D/MERSI, H8/AHI and the like, can early warn in time, adopts prevention and control strategies, effectively early warns the sea ice, and reduces the loss caused by disasters.

Optionally, the MODIS sea ice identification and the sea ice density generation are performed, the data source is L1 orbit data or projection data of AQUA/MODIS and TERRA/MODIS, and the sea ice information of the MODIS data is generated by calculating an snow index and partitioning a partition threshold.

Optionally, the FY3D sea ice identification and sea ice density generation are performed by generating the sea ice information of FY3D data by calculating the snow index and partition threshold segmentation, wherein the data source is L1 orbit data or projection data of FY 3D/MERSI.

Optionally, HY1C sea ice identification and sea ice density generation are performed, the data source is L1 orbit data or projection data of HY1C/COCTS and HY1C/CZI, and sea ice information of HY1C data is generated by calculating snow index and partition threshold segmentation.

Optionally, the H8 sea ice identification and sea ice density generation are performed by generating the sea ice information of the H8 data by calculating the snow index and partitioning the partition threshold, wherein the data source is L1 orbit data or projection data of H8/AHI.

Optionally, the sea ice identification is generated by fusion, sea ice identification results of various satellite data are obtained, equal longitude and latitude projection is performed, and the sea ice identification results are fused by time and space matching.

The embodiment of the invention adopts a plurality of remote sensing satellites at home and abroad to dynamically monitor the sea ice, can early warn in time, and adopts a prevention and control strategy to reduce the loss caused by disasters.

The present invention is not limited to the structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.