High-spatial resolution remote-sensing image bag-of-word classification method based on linear words
A high spatial resolution, remote sensing image technology, applied in the field of remote sensing image processing and information extraction, can solve the problems that the shape of the ground objects cannot be well described, and the SIFT point feature is not suitable for the area where the mean value of the remote sensing image is stable.
Inactive Publication Date: 2013-07-31
NANJING NORMAL UNIVERSITY
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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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[0137] Data preparation: The training sample data and classification data used in this embodiment are all high-resolution remote sensing images of the Lushan area taken by the commercial ground imaging satellite GeoEye-1. The image contains 4 bands, R: 655-690 nm, G: 510-580nm, B: 450-510nm, NIR: 780-920nm, the image spatial resolution is 2m.
[0138] 1. Training phase
[0139] The first step is to extract the straight line features of the training image, and on this basis, calculate the straight line feature vector
[0140] (a) Obtain the phase line of the training image, and set the parameters as follows: Gaussian filter coefficient is 0.5, phase grouping gradient amplitude difference limit is 1, and the shortest line length is 10. The result is Image 6 .
[0141] (b) Calculate the feature vector of the straight line.
[0142] The linear features used in this embodiment include 15-dimensional features including the density, length, length entropy, angle, angle entropy, contrast, co...
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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 word bag classification method of high spatial resolution remote sensing images based on straight line words, belonging to the field of remote sensing image processing and information extraction. Background technique [0002] Remote sensing image classification is an important task for remote sensing image information extraction. With the emergence and wide application of high spatial resolution remote sensing images, the spatial structure information and surface texture information that remote sensing images can provide are becoming more and more detailed, and the edges of the features are also clearer. On the one hand, the rich detailed information of ground features enhances the role of remote sensing images in ground feature monitoring, planning, management, etc., but at the same time, the high spatial resolution remote sensing image makes the "same matter with dif...
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IPC IPC(8): G06K9/66
Inventor 顾礼斌汪闽
Owner NANJING NORMAL UNIVERSITY