A Space-borne Hyperspectral Image Segmentation and Clustering Method Based on Space-Spectral Information Combination

Abstract

The invention discloses a method for segmenting and clustering space-borne hyperspectral images combined with space-spectrum information, including preprocessing the acquired space-borne hyperspectral images, removing bands with serious radiation quality problems and bands with serious atmospheric absorption effects, Obtain the reflectance image; determine the complexity level of the image, and determine the segmentation parameters according to the complexity level; select part or all of the bands in the reflectance image for principal component transformation, retain the first N principal components after transformation and perform normalization processing; Perform spatial dimension filtering on the image; perform principal component transformation on the reflectance image after spatial dimension filtering, retain the first N principal components after transformation and perform normalization processing; image initial segmentation based on seed point region growth and SAD, Obtain the initial blob segmentation result and blob adjacency graph; split and merge blobs; calculate the spectral and texture features of blobs, construct a set of spatial spectral features of blobs, and use the K-means method to cluster blobs to obtain Image clustering result graph.

Description

technical field [0001] The invention relates to the technical field of segmentation and clustering of space-borne hyperspectral images, in particular to a method for segmentation and clustering of space-borne hyperspectral images combined with space-spectral information. Background technique [0002] Compared with traditional surveying and mapping technology, remote sensing has the advantages of large detection range, fast data acquisition, short repeated observation cycle, and less restriction by ground conditions. Among them, spaceborne hyperspectral data contains hundreds of spectral channels, which contain rich spectral information. Compared with ground and aerial remote sensing, hyperspectral satellite remote sensing can obtain macro-scale ground feature information quickly, in a large range, and at high frequencies, greatly improving the quantitative investigation and monitoring capabilities of natural resource elements. [0003] Segmentation and clustering of remote ...

AI Technical Summary

Problems solved by technology

Pixel-based segmentation is relatively simple and easy to implement, and is suitable for relatively simple images with a small amount of data; edge detection-based segmentation is mainly used for contour extraction, and it is still difficult to apply to images with multiple bands and indistinct edge information; The region-based method mainly uses the similarity of pixel features in the region to segment the image. This type of method mainly includes two methods: region growth and region splitting and merging, represented by the watershed algorithm. This type of algorithm is easy to extend to multi-band, but the cost Higher; while the physical model-based segmentation method can identify shadows, light spots, etc., and can obtain the boundaries of objects in high-resolution images, but is limited by the harsh model conditions

For hyperspectral data, the spatial spectral information joint segmentation and clustering algorithm that combines spectral information with spatial features such as shape and texture can improve the segmentation and clustering effect to a certain extent, but most of the existing algorithms are suitable for high spatial resolution. , airborne hyperspectral images with relatively simple types of ground objects and a large number of pixels of ground objects, while for spaceborne hyperspectral images with complex types of ground objects, few pixels of ground objects, large number of bands, and high data redundancy Hyperspectral imagery applications are very limited

Images