A remote sensing image unsupervised segmentation evaluation method and device

Abstract

The invention belongs to the technical field of remote sensing image processing, and discloses a remote sensing image unsupervised segmentation evaluation method and device. The method comprises the steps of obtaining a plurality of segmentation objects of a remote sensing image; according to the area of each segmentation object, weighting the average variance of all wavebands of each segmentationobject to obtain an area weighted variance; obtaining the relative heterogeneity of each segmentation object according to the local spectrum difference and the average spectrum difference of each segmentation object; obtaining total heterogeneity according to the number of the segmentation objects, the waveband number of the remote sensing image and the relative heterogeneity of each segmentationobject; and obtaining an evaluation result according to the area weighting variance and the total heterogeneity. The device comprises an obtaining module, an area weighted variance obtaining module,a relative heterogeneity obtaining module, an overall heterogeneity obtaining module and an evaluation result obtaining module. According to the method, the over-segmentation or under-segmentation phenomenon caused by a traditional evaluation method can be avoided, so that the image evaluation method is optimized, the evaluation effect is more remarkable, and the evaluation precision is higher.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, in particular to a remote sensing image non-supervised segmentation evaluation method and device. Background technique [0002] With the rapid development of high-resolution remote sensing technology, smaller geographical objects on the ground, such as roads, houses, and cars, can be seen clearly from remote sensing images. However, in the obtained high-resolution remote sensing images, such a phenomenon often occurs: different geographical objects have the same spectral reflectance, but the same geographical object has different spectral reflectance in remote sensing images. Due to the existence of such problems, traditional pixel-based image analysis methods perform poorly in practical applications. The object-oriented technology (GEOBIA) is being used more and more commonly as a new method of remote sensing analysis because of its low sensitivity to the spectral varian...

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Problems solved by technology

This may lead to biased segmentation results, i.e. over-segmented or under-segmented results, due to differences in the properties of the homogeneity and heterogeneity assessment criteria

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