Remote sensing image processing flow

1. Noise reduction processing Due to the sensor, periodic noise will appear in some acquired remote sensing images, and we must eliminate or weaken it before using it. (1) Except for periodic noise and sharp noise, periodic noise is generally superimposed on the original image to form periodic interference patterns with different amplitudes, frequencies and phases. It forms a series of peaks or bright spots, which are most prominent in some spatial frequency positions. Generally, it can be eliminated by bandpass or notch filtering. In order to eliminate the peak noise, especially when it is not parallel to the scanning direction, Fourier transform filtering is generally more convenient.

(2) Remove bad lines and stripes from remote sensing images. Stripes parallel to the scanning direction often appear in remote sensing images, and there are some stripe noises unrelated to radiation signals, which are generally called bad lines. Fourier transform and low-pass filtering are generally used to eliminate or weaken.

2. Thin cloud processing Due to the weather, thin clouds appearing in some remote sensing images can be weakened. 3. Shadow processing Because of the sun's altitude angle, some images will have mountain shadows, which can be eliminated by the ratio method. Usually, the remote sensing images we get are generally secondary products. In order to make their positioning accurate, geometric precision correction must be carried out before using remote sensing images, and orthorectification must also be carried out in areas with large topographic relief. Under special circumstances, it is necessary to correct the atmosphere of remote sensing images, which is not described here. 1. Image registration means that two data sources in the same area can display and perform mathematical operations in the same geographical coordinate system, and the geographical coordinates of one data source must first be registered with the geographical coordinates of the other data source. This process is called registration. The registration of (1) image to raster image will register a remote sensing image to another image or raster map in the same area, so that it can be displayed in space. (2) Registration of images to vector graphics

A remote sensing image is registered in the vector map of the same area, so that it can be superimposed and displayed in space. 2. Geometric coarse correction This correction is aimed at the cause of geometric distortion. Before providing data to users, the ground receiving station has corrected the geometric distortion of the image according to the running posture, sensor performance index, atmospheric state and solar altitude angle received at the same time. 3. Precise geometric correction In order to accurately locate remote sensing data, it is necessary to accurately locate remote sensing data in a specific geographical coordinate system. This process is called geometric fine correction. (1) image correction uses remote sensing images with accurate geographical coordinates and projection information to correct the original remote sensing images so that they have accurate geographical coordinates and projection information. (2) Image-to-map (raster or vector) uses a scanned topographic map or vector topographic map with accurate geographical coordinates and projection information to correct the original remote sensing image so that it has accurate geographical coordinates and projection information. In order to make the ground information contained in remote sensing images more readable and the interested objects more prominent, it is necessary to enhance the remote sensing images. 1. color synthesis In order to make full use of the advantages of color in remote sensing image interpretation and information extraction, multi-spectral images often need color synthesis to obtain color images. Color images can be divided into true color images and false color images. | 2. Histogram transformation

The random distribution obtained by counting the number of pixels of each brightness of each image is the histogram of the image. Generally speaking, the random distribution of pixel brightness should be normal in an image containing a large number of pixels. Histogram is non-normal distribution, which means that the brightness distribution of the image is too bright, too dark or too concentrated, and the contrast of the image is small. In order to improve the image quality, it is necessary to adjust the histogram to normal distribution.

3. Density segmentation classifies gray-scale images according to the gray-scale values of pixels, and then endows the gray-scale images with different colors, so that the original gray-scale images become pseudo-color images and image enhancement is realized.

4. Gray inversion Gray inversion is to stretch the gray range of the image to the dynamic range of the display device (such as 0 ~ 255) to a saturated state, and then invert it, so that the positive image and the negative image can be interchanged. 1. Image mosaic, also known as image mosaic, is a technical process of splicing two or more digital images (possibly obtained under different photographic conditions) together to form a complete image. Usually, each image is geometrically corrected first, and they are planned into a unified coordinate system, then they are cut to remove the overlapping parts, and then the cut images are spliced to form a large-format image.

2. Image Homogenization In practical application, the remote sensing images we use for image mosaic often come from remote sensing data of different sensors and phases, and there are often inconsistent colors when doing image mosaic. At this time, it is necessary to combine the actual situation and overall coordination to homogenize the colors of the images involved in stitching. The characteristics of target objects in remote sensing images reflect the differences of electromagnetic radiation of objects in remote sensing images. According to the characteristics of ground objects in remote sensing images, the process of identifying the types, properties, spatial positions, shapes and sizes of ground objects is remote sensing information extraction.