阅读说明：本技术 一种基于多源遥感卫星的遥感影像处理方法 (Remote sensing image processing method based on multi-source remote sensing satellite ) 是由 李君� 吕新杰 姜峥超 于 2019-11-20 设计创作，主要内容包括：本发明公开了一种基于多源遥感卫星的遥感影像处理方法,由以下步骤组成：图像预处理；使用ENVI工具对遥感影像进行辐射定标、大气校正；研究区域掩膜裁剪；根据研究区域建立感兴趣区域进行裁剪；读取影像,针对影像每个波段进行去云；水体提取；根据研究区域建立感兴趣区域进行裁剪；水体叶绿素反演；利用对叶绿素a最敏感的波段计算区域A的叶绿素指数；水体悬浮物反演。本发明考虑了遥感影像含有薄云的情况,增加了去云步骤,使用多源卫星影像或者同源多时相卫星影像进行多幅图像合成达到去云的目的；本发明同时集成了遥感影像水体提取、水体叶绿素反演、水体悬浮物反演三种功能,可自动化完成,无需人工干涉。(The invention discloses a remote sensing image processing method based on a multi-source remote sensing satellite, which comprises the following steps: preprocessing an image; carrying out radiometric calibration and atmospheric correction on the remote sensing image by using an ENVI tool; cutting a mask of a research area; establishing an interested area according to the research area for cutting; reading an image, and removing clouds of each wave band of the image; extracting a water body; establishing an interested area according to the research area for cutting; performing water body chlorophyll inversion; calculating the chlorophyll index of the area A by using the wave band most sensitive to chlorophyll a; and (5) inverting the water body suspended matters. The method considers the situation that the remote sensing image contains thin clouds, adds a cloud removing step, and uses a multi-source satellite image or a homologous multi-temporal satellite image to synthesize a plurality of images so as to achieve the aim of cloud removal; the invention integrates three functions of remote sensing image water body extraction, water body chlorophyll inversion and water body suspended matter inversion at the same time, can be automatically completed, and does not need manual interference.)

1. A remote sensing image processing method based on multi-source remote sensing satellites is characterized by comprising the following steps: the method comprises the following steps:

i, preprocessing an image; carrying out radiometric calibration and atmospheric correction on the remote sensing image by using an ENVI tool;

II, cutting a mask of a research area; establishing an interested area according to the research area for cutting;

reading the image, and removing clouds in each wave band of the image; utilizing images of different satellite sources in the same area or images of the same satellite in the same area and different time phases; comparing and analyzing the plurality of images, screening and removing abnormal values according to the reflectivity of each pixel point, and averaging the rest values to be used as the reflectivity of the point;

IV, water body extraction, reading remote sensing image wave band information, firstly calculating an image normalized vegetation index, establishing a mask to extract an area A with the image normalized vegetation index less than 0 for reservation to be processed, introducing a normalized water index establishment formula ①:

NDWI ═ G-NIR)/(G + NIR), equation ①

According to the correlation between each wave band of the remote sensing image and the water body, calculating the water body index by utilizing the wave band with high correlation; the NDWI is a normalized water index, G is a green light wave band of the remote sensing image, and NIR is a near infrared wave band of the remote sensing image; calculating the NDWI value of the area A to obtain an image water body extraction result and visualizing the image water body extraction result;

and V, inverting water chlorophyll, calculating the chlorophyll index of the area A by using the wave band most sensitive to chlorophyll a based on the result of water extraction in the step IV, and inverting to obtain a water chlorophyll result, wherein the calculation of the chlorophyll index of the area A is shown as a formula ②:

chlorophyll ═ NIR/R, equation ②

Wherein, Chlorophyl is Chlorophyll index, NIR is near infrared band of remote sensing image, and R is red light band of remote sensing image;

VI, inverting the suspended solids of the water body, calculating the concentration of the suspended sediment in the area A by utilizing the wave band which is most sensitive to the suspended sediment on the sea/river based on the result of the step III, and inverting to obtain the result of the suspended sediment of the water body, wherein the calculation of the concentration of the suspended sediment is shown as a formula ③:

Suspended＝119.62(R/G)^6.0823equation ③

The Suspended sediment concentration is adopted as the Suspended sediment concentration, R is the red light wave band of the remote sensing image, and G is the green light wave band of the remote sensing image.

2. The remote sensing image processing method based on the multisource remote sensing satellite according to claim 1, characterized in that: and D, respectively carrying out correlation analysis on the chlorophyll concentration of the water body measured in the step V and the suspended sediment concentration measured in the step VI and each wave band of the remote sensing image, selecting the wave band with a higher correlation coefficient to carry out regression analysis, and determining the chlorophyll and suspended matter concentration inversion function.

Technical Field

The invention relates to a remote sensing image processing method, in particular to a remote sensing image processing method based on a multi-source remote sensing satellite.

Background

In the aspect of remote sensing image processing, the prior art mostly does not consider the cloud removal problem, or directly uses the function of the software ENVI to remove the cloud, but the result is not good, and the cloud place can be changed into full black, so that the accuracy of the image remote sensing image effect graph is improved. In addition, the prior art uses an ENVI software band calculator to calculate the index and then invert. The operation is manually carried out step by step until the operation is finished, so that the operation difficulty is improved, and the deviation is easy to occur.

Disclosure of Invention

In order to overcome the defects of the technology, the invention provides a remote sensing image processing method based on a multi-source remote sensing satellite.

In order to solve the technical problems, the invention adopts the technical scheme that: a remote sensing image processing method based on a multi-source remote sensing satellite comprises the following steps:

i, preprocessing an image; carrying out radiometric calibration and atmospheric correction on the remote sensing image by using an ENVI tool;

II, cutting a mask of a research area; establishing an interested area according to the research area for cutting;

reading the image, and removing clouds in each wave band of the image; utilizing images of different satellite sources in the same area or images of the same satellite in the same area and different time phases; comparing and analyzing the plurality of images, screening and removing abnormal values according to the reflectivity of each pixel point, and averaging the rest values to be used as the reflectivity of the point;

IV, water body extraction, reading remote sensing image wave band information, firstly calculating an image normalized vegetation index, establishing a mask to extract an area A with the image normalized vegetation index less than 0 for reservation to be processed, introducing a normalized water index establishment formula ①:

NDWI ═ G-NIR)/(G + NIR), equation ①

According to the correlation between each wave band of the remote sensing image and the water body, calculating the water body index by utilizing the wave band with high correlation; the NDWI is a normalized water index, G is a green light wave band of the remote sensing image, and NIR is a near infrared wave band of the remote sensing image; calculating the NDWI value of the area A to obtain an image water body extraction result and visualizing the image water body extraction result;

and V, inverting water chlorophyll, calculating the chlorophyll index of the area A by using the wave band most sensitive to chlorophyll a based on the result of water extraction in the step IV, and inverting to obtain a water chlorophyll result, wherein the calculation of the chlorophyll index of the area A is shown as a formula ②:

chlorophyll ═ NIR/R, equation ②

Wherein, Chlorophyl is Chlorophyll index, NIR is near infrared band of remote sensing image, and R is red light band of remote sensing image;

VI, inverting the suspended solids of the water body, calculating the concentration of the suspended sediment in the area A by utilizing the wave band which is most sensitive to the suspended sediment on the sea/river based on the result of the step III, and inverting to obtain the result of the suspended sediment of the water body, wherein the calculation of the concentration of the suspended sediment is shown as a formula ③:

Suspended＝119.62(R/G)^6.0823equation ③

The Suspended sediment concentration is adopted as the Suspended sediment concentration, R is the red light wave band of the remote sensing image, and G is the green light wave band of the remote sensing image.

And further, performing correlation analysis on the chlorophyll concentration of the water body measured in the step V and the suspended sediment concentration measured in the step VI and each wave band of the remote sensing image respectively, selecting the wave band with higher correlation coefficient to perform regression analysis, and determining the chlorophyll and suspended matter concentration inversion function.

The ENVI tool is a complete remote sensing image processing platform, and the application of software processing technology in collection covers input/output of image data, image scaling, image enhancement, correction, orthorectification, mosaic, data fusion and various transformations, information extraction, image classification, knowledge-based decision tree classification, integration with GIS, DEM and terrain information extraction, radar data processing and three-dimensional display analysis.

Normalized vegetation index (NDVI), one of the important parameters reflecting crop growth and nutritional information. NDVI ═ (NIR-R)/(NIR + R), NIR near infrared band, R red band, -1 ═ NDVI ═ 1.

And (4) normalizing the water index (NDWI) and highlighting water body information in the remote sensing image.

The method considers the situation that the remote sensing image contains thin clouds, adds a cloud removing step, and uses a multi-source satellite image or a homologous multi-temporal satellite image to synthesize a plurality of images so as to achieve the aim of cloud removal; the invention integrates three functions of remote sensing image water body extraction, water body chlorophyll inversion and water body suspended matter inversion at the same time, can be automatically completed, and does not need manual interference.

Detailed Description

The present invention will be described in further detail with reference to specific embodiments.

A remote sensing image processing method based on a multi-source remote sensing satellite comprises the following steps:

i, preprocessing an image; carrying out radiometric calibration and atmospheric correction on the remote sensing image by using an ENVI tool;

II, cutting a mask of a research area; establishing an ROI (region of interest) according to the research region for cutting;

reading the image, and removing clouds in each wave band of the image; utilizing images of different satellite sources in the same area or images of the same satellite in the same area and different time phases; comparing and analyzing the plurality of images, screening and removing abnormal values (namely large reflectivity values) aiming at the reflectivity of each pixel point, and averaging the rest values to be used as the reflectivity of the point; the purpose of multiple images is to better read the spectral reflectance of a point in a close time period and use it as a composite, improving data accuracy.

IV, water body extraction, reading remote sensing image wave band information, firstly calculating an image normalized vegetation index, establishing a mask to extract an area A with the image normalized vegetation index less than 0 for reservation to be processed, introducing a normalized water index establishment formula ①:

NDWI ═ G-NIR)/(G + NIR), equation ①

According to the correlation between each wave band of the remote sensing image and the water body, calculating the water body index by utilizing the wave band with high correlation; the NDWI is a normalized water index, G is a green light wave band of the remote sensing image, and NIR is a near infrared wave band of the remote sensing image; calculating the NDWI value of the area A to obtain an image water body extraction result and visualizing the image water body extraction result;

and V, inverting water chlorophyll, calculating the chlorophyll index of the area A by using the wave band most sensitive to chlorophyll a based on the result of water extraction in the step IV, and inverting to obtain a water chlorophyll result, wherein the calculation of the chlorophyll index of the area A is shown as a formula ②:

chlorophyll ═ NIR/R, equation ②

Wherein, Chlorophyl is Chlorophyll index, NIR is near infrared band of remote sensing image, and R is red light band of remote sensing image;

VI, inverting the suspended solids of the water body, calculating the concentration of the suspended sediment in the area A by utilizing the wave band which is most sensitive to the suspended sediment on the sea/river based on the result of the step III, and inverting to obtain the result of the suspended sediment of the water body, wherein the calculation of the concentration of the suspended sediment is shown as a formula ③:

Suspended＝119.62(R/G)^6.0823equation ③

The Suspended sediment concentration is adopted as the Suspended sediment concentration, R is the red light wave band of the remote sensing image, and G is the green light wave band of the remote sensing image.

And D, respectively carrying out correlation analysis on the chlorophyll concentration of the water body measured in the step V and the suspended sediment concentration measured in the step VI and each wave band of the remote sensing image, selecting the wave band with a higher correlation coefficient to carry out regression analysis, and determining the chlorophyll and suspended matter concentration inversion function.

Compared with the prior art, the invention has the advantages that:

a. when the remote sensing image contains clouds, most researchers do not consider the cloud removing operation, or directly use an ENVI cloud removing plug-in to remove the clouds, but the effect is poor; according to the scheme, a plurality of images are synthesized, abnormal values are eliminated, and then an average value is obtained, so that the spectral information of the images is more accurate;

b. the remote sensing image water extraction, the water chlorophyll inversion and the water suspended matter inversion are integrated, the required effect diagram can be simply and quickly obtained, manual interference is not needed, and the accuracy is improved.

The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions or substitutions within the technical scope of the present invention.