Building extraction method based on binary feature classifier

A feature classification and extraction method technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of high cost and low efficiency, and achieve the effect of fast speed, no interaction in the algorithm process, and fast and accurate extraction

Active Publication Date: 2017-09-26
CHONGQING GEOMATICS & REMOTE SENSING CENT
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This type of method usually requires manual operation by personne

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  • Building extraction method based on binary feature classifier
  • Building extraction method based on binary feature classifier
  • Building extraction method based on binary feature classifier

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Embodiment Construction

[0055] The specific embodiments and working principles of the present invention will be further described in detail below with reference to the accompanying drawings.

[0056] Such as figure 1 As shown, a method for building extraction based on a binary feature classifier, specifically follows the following steps:

[0057] Step 1: Down-sampling the original orthophotos respectively, thereby down-sampling the original orthophotos to one meter per pixel;

[0058] Step 2: Convert the down-sampled orthoimage from the RGB color space to the LAB color space to make better use of the texture details and color features in the original image;

[0059] Step 3: Perform SLIC (simple linear iterative clustering) hyper-segmentation processing on the image in the LAB color space. The specific processing steps are:

[0060] Step 3-1: Initialize the seed points, distribute the seed points evenly in the image according to the set number of super pixels, and calculate the distance L between adjacent seed...

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Abstract

The invention discloses a building extraction method based on a binary feature classifier. The building extraction method comprises the following steps: firstly, an original image is subjected to downsampling and transformed into LAB color space, then SLIC super segmentation is implemented in the LAB color space, intersections are obtained and subjected to dichotomy by the binary feature classifier, building intersections and non-building intersections are obtained, and a building area is obtained; then an extracted building area image is transformed into the LAB color space, SLIC super segmentation is implemented in the LAB color space, new intersections are obtained and subjected to trichotomy by the binary feature classifier, building interior points, building edge points and non-building points are obtained, and a building contour is obtained. The building extraction method has the remarkably effects as follows: rapid and accurate extraction of the building contour is realized; compared with the traditional method, the method is higher in precision and speed, and interaction is avoided in an algorithmic process.

Description

Technical field [0001] The invention relates to the technical field of building remote sensing image processing, and specifically, is a building extraction method based on a binary feature classifier. Background technique [0002] With the introduction of concepts such as digital cities and smart municipalities, business needs such as urban planning, land use change monitoring, and 3D building modeling have grown rapidly, and building extraction is the core technology that supports the realization of these businesses. Therefore, how to extract buildings accurately and quickly from high-resolution remote sensing images has become a current hot technology field. [0003] Most of the researches on building extraction methods suitable for remote sensing images are based on image segmentation methods. This type of method usually requires manual operation by personnel with professional experience, which is inefficient and costly. [0004] It can be seen that there is an urgent need for a...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/176G06V10/44G06F18/24
Inventor 胡艳丁忆李朋龙徐永书李静罗鼎李胜段松江王斌王岚姜紫薇郑中吴寒曾远文魏文杰
Owner CHONGQING GEOMATICS & REMOTE SENSING CENT
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