Wave band correction change detection method of remote sensing image fuzzy clustering and system thereof

Abstract

The invention provides a wave band correction change detection method of remote sensing image fuzzy clustering and a system thereof. The method comprises the following steps: step1, carrying out pretreatment of a multi-band remote sensing image, which means filtering and enhancement processing of the multi-band remote sensing image; step 2, carrying out single band separation on the multi-band remote sensing image after the pretreatment and acquiring a single-band remote sensing image; step 3, constructing a single band difference image of two time-phase single-band remote sensing images; step 4, constructing a multi-band combined image based on two time-phase multi-band remote sensing images after the pretreatment and acquiring a mutual neighborhood information content of each pixel of the multi-band combined image; step 5, correcting the mutual neighborhood information content of the pixels of the multi-band combined image and acquiring a correction image; step 6, using a fuzzy C-means method to carry out change detection on the correction image. By using the method and the system of the invention, detection precision is high, an anti-noise interference ability is good and an automation degree is high.

Description

technical field [0001] The invention belongs to the technical field of photogrammetry and remote sensing image application, and in particular relates to a method and system for detecting changes in band correction for fuzzy clustering of remote sensing images. Background technique [0002] Remote sensing image change detection is to detect the time-changing information of the ground objects in the area by analyzing the remote sensing images of the same area in different phases. With the rapid development of aerospace technology, remote sensing observation data has been more and more widely used due to its characteristics of real-time, fast, wide coverage, and high temporal and spatial resolution. How to effectively extract the change information in massive data and use it for the prevention of various natural disasters faced by the environment, agriculture, ecosystems and human beings has become a hot issue in remote sensing application research. In recent years, many schol...

AI Technical Summary

Problems solved by technology

[0003] At present, the commonly used remote sensing image change detection methods include direct comparison method and post-classification comparison method. The former is easy to operate and fast, but it can only quantitatively describe whether the target area has changed, and it is difficult to determine the nature of the change.

The latter can provide change type information, but detection accuracy is affected by the error propagation of separate classifications

There is a phenomenon of mixed pixels in medium and high-resolution remote sensing images, and the traditional "hard" classification method cannot obtain high accuracy. Fuzzy C-means clustering (FCM) is a soft clustering algorithm that uses membership degrees to make classes and classes There is no clear boundary between them, and it is effective to deal with mixed pixels, but there are defects such as over-dependence on the initial cluster center, difficulty in automatically determining the number of categories, and sensitivity to isolated point noise data.

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