Optical remote sensing image decomposition algorithm

Abstract

The invention belongs to the field of remote sensing image processing and particularly relates to an optical remote sensing image decomposition algorithm. The optical remote sensing image decomposition algorithm comprises the following steps: step 1, reading the stored original image data; step 2, performing logarithmic transformation; taking logarithm of F(x, y), and F (x, y)=1g(F(x, y)); step 3, performing wavelet decomposition; carrying out discrete wavelet transformation, wherein the wavelet basis is haar wavelet; step 4, performing inverse transformation; using zero to substitute {Hp, t} for wavelet inverse transformation with low-frequency subgraphs Lt to obtain low-frequency image Li in the image f spatial domain; step 5, conducting fractal computation; step 6, performing cyclic judgment; if Di is less than or equal to Dd, actuating step 7, otherwise, adding 1 to i, and then actuating the step 3 (wavelet decomposition); step 7, performing optimal dimension judgment; step 8, realizing image generation. The optical remote sensing image decomposition algorithm has the effect of solving the problem that the traditional image decomposition algorithm cannot accurately judge the optimal decomposition dimension.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows us to use operations like multiplication or addition to manipulate images captured through different types of cameras. It decomposes them based upon their characteristics such as size, shape, texture etc., which makes it easier than previous methods due to its ability to calculate both numerical values and spectral properties simultaneously with each other. By doing this we aimed at improving image resolution and identification capabilities while reducing errors caused by factors like uneven ground surfaces.

Problems solved by technology

Technics: Remote Sensing (RS) technology can be useful for studying materials that are distributed over large areas or surfaces due to their geometry. Current methods involve analyzing high resolution imagery data from multiple small sections taken by satellites orbit around Earth. These techniques have limitations such as requiring laborious experimental comparisons between observed and simulated samples which may result in errors when determining decompositions based solely upon physical properties like heights or width dimensions alone.

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