Image Data refers to the collection of gray values of each pixel represented by numerical values.
An image in the real world is generally represented by the intensity and spectrum (color) of each light point on the image. When converting image information into data information, the image must be decomposed into many small areas, which are called pixels, and can be represented by a numerical value. Color images are usually represented by red, green, and blue trichromatic components.Sequentially extracting the information of each pixel can use a discrete array to represent a continuous image.stayGeographic Information SystemGenerally refers to grid data.
Image Data refers to the collection of gray values of each pixel represented by numerical values.
The image in the real world is generally represented by the intensity and spectrum (color) of each point of light on the image. When the image information is converted into data information, the image must be decomposed into many small areas, which are called pixels. A value can be used to represent its gray level. Color images are often represented by red, green, and blue trichromatic components.Sequentially extracting the information of each pixel can use a discrete array to represent a continuous image.stayGeographic Information SystemGenerally refers to grid data.
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File management and database management are usually used for image data management.Although the file based management mode has mature technology, simple structure and low maintenance cost, it has poor security, does not support distributed management, poor reliability, poor scalability, weak metadata management and limited image content query function.Database based management is the main way of remote sensing image data storage and management.
In the organization and management of remote sensing image data, data block management technology can reduce the amount of data transmitted on the network, and facilitate data compression and operation processing;Data compression technologyCan improveData transmission efficiency, reducing the demand for storage space.
Content basedImage retrieval technologyWhen applied to the general multimedia field and the medical field, it is not necessary to study the block organization strategy of data, because image objects often exist as independent units, and there is generally no spatial correlation between objects. Content based image retrieval is a similar matching between the query image and the overall characteristics of the image objects stored in the target image library,Feature library and image library are one-to-one correspondence.In content-based remote sensing image retrieval, the target image is often spatially seamless and contains large images of multiple complex targets, while the query image generally contains only one or a few texture features and is small in size. It is meaningless to compare the similarity between the texture features of the query image and the overall texture features of the target image,Content based remote sensing image retrieval is actually the similarity comparison between the query image and the local area of the target image.
Remote sensing image compression technology is an effective means to solve the problems of massive remote sensing image storage, organization and management, as well as network transmission.The traditional compression technology has shortcomings in compression ratio, decompression speed, or quality method, which seriously hindersHigh resolution remote sensingThe promotion and use of image data.[1]
