High-spatial resolution remote-sensing image bag-of-word classification method based on linear words

Abstract

The invention discloses a high-spatial resolution remote-sensing image bag-of-word classification method based on linear words, which includes first dividing images to be classified into a practice sample and a classification sample. Steps for the practice sample include collecting linear characteristics of the practice image and calculating linear characteristic vector; utilizing K-Means++ arithmetic to generate linear vision word list in cluster mode; segmenting practice images and obtaining linear vision word list column diagram of each segmentation spot block on the base; and conducting class label on the spot block and putting the classification and linear vision word column diagram in storage. After sample practice, steps for the classification sample include collecting linear characteristics of the images to be classified, segmenting the images to be classified, calculating linear characteristics vector on the base, obtaining linear vision word list column diagram of each segmentation spot block and selecting an SVM classifier to classify the images to be classified to obtain classification results. The high-spatial resolution remote-sensing image bag-of-word classificationmethod utilizes linear characteristics to establish bag-of-word models and is capable of obtaining better high spatial resolution remote sensing image classification effect.

Description

technical field [0001] The invention relates to a remote sensing image classification method, in particular to a high-spatial-resolution remote sensing image bag-of-words classification method based on linear words, and belongs to the field of remote sensing image processing and information extraction. Background technique [0002] Remote sensing image classification is an important task of remote sensing image information extraction. With the emergence and wide application of high spatial resolution remote sensing images, remote sensing images can provide more and more detailed spatial structure information and surface texture information of ground objects, and the edges of ground objects are also clearer. On the one hand, the rich detailed information of ground features enhances the role of remote sensing images in the monitoring, planning, and management of ground features. "The phenomenon has become more common, and remote sensing images can reflect more and more types ...

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Problems solved by technology

However, this classification method only divides the image evenly into blocks, and classifies on the basis of blocks, which cannot describe the shape of the ground objects well. At the same time, the SIFT point feature used in this paper is not suitable for the mean value of remote sensing images. stable area

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