Hyperspectral image target detection method based on tension linear discrimination analysis dimension reduction

A linear discriminant analysis and hyperspectral image technology, applied in the field of hyperspectral image target detection, can solve the problems of inability to mine 3D data information and low detection accuracy

Active Publication Date: 2017-11-03
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that the existing hyperspectral image target detection method does not fully consider the characteristics of spatial constraint enhancement under high-resolution conditions, and cannot carry out information mining from the whole 3D data, and the detection accuracy is low. Hyperspectral Image Target Detection Method Based on Tensor Linear Discriminant Analysis Dimensionality Reduction

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  • Hyperspectral image target detection method based on tension linear discrimination analysis dimension reduction
  • Hyperspectral image target detection method based on tension linear discrimination analysis dimension reduction
  • Hyperspectral image target detection method based on tension linear discrimination analysis dimension reduction

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specific Embodiment approach 1

[0030] Specific implementation mode one: combine figure 1 To illustrate this embodiment, the specific process of the hyperspectral image target detection method based on tensor linear discriminant analysis dimensionality reduction in this embodiment is as follows:

[0031] Step 1: Select and divide the tensor block of the hyperspectral image to be detected, and obtain the empty X-empty Y-spectrum third-order target tensor block, empty X-empty Y-spectrum third-order background tensor block and empty X- Empty Y-spectrum third-order test sample tensor block to be detected;

[0032] Step 2: Set the size of the target tensor block, background tensor block and test sample tensor block to be tested in each dimension after projection, and use the target tensor block and background tensor block obtained in step 1 to train and obtain the target The three-dimensional projection matrix of the tensor block, the background tensor block and the test sample tensor block to make the target t...

specific Embodiment approach 2

[0036] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in the first step, the hyperspectral image to be detected is selected and divided into tensor blocks, and the third-order target tensor of empty X-empty Y-spectrum is obtained Block, empty X-empty Y-spectrum third-order background tensor block and empty X-empty Y-spectrum third-order test sample tensor block to be detected; the specific process is:

[0037] Given a 3×3 window, convert the hyperspectral image to be detected into a third-order tensor form, slide the sampling window, and determine it as empty X-empty when the value of the truth map corresponding to the center point of the sampling window is 1 Block of Y-spectral third-order target tensors from which n is arbitrarily sampled 1 get an empty X-empty-Y-spectrum third-order target tensor block When the values ​​of all points in the window corresponding to the truth map are 0, it is determined to be an empty X-empty...

specific Embodiment approach 3

[0039] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that in the step two, the target tensor block, the background tensor block and the test sample tensor block to be detected are set in each dimension after projection The size of the target tensor block and the background tensor block obtained in step 1 are trained to obtain the projection matrix in the three dimensions of the target tensor block, the background tensor block and the test sample tensor block to be detected, so that the target tensor Blocks and background tensor blocks have maximum separability in the projected subspace; the specific process is:

[0040] Step 21, the target tensor block obtained in step 1 and the background tensor block As two-class training tensor samples Set the target tensor block, the background tensor block and the test sample tensor block to be tested. The size of the projected dimension of each dimension of the block is take...

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Abstract

The invention provides a hyperspectral image target detection method based on tension linear discrimination analysis dimension reduction. The objective of the invention is to solve problems that in the current hyperspectral image target detection method, characteristics of spatial constraint enhancement under the condition of high scores are not fully considered, information excavation cannot be performed on the whole three-dimensional information and the detection precision is quite low. The method comprises steps of 1, acquiring three-order target tension blocks, three-order background tension blocks and three-order to-be-detected test sample tension blocks; 2, allowing sub-space after the projection of the target tension blocks, the background tension blocks and the to-be-detected test sample tension blocks to have the biggest separability; 3, projecting the target tension blocks, the background tension blocks and the to-be-detected test sample tension blocks to a tension sub-space with the biggest separability; 4, calculating the total distance from each to-be-detected test sample to the background and the target; and 5, setting a threshold value and if the gray scale value is larger than the threshold value, determining the pixel of the central point as the target, or else, determining pixel of the central point as the background. According to the invention, the method is applicable to image processing field.

Description

technical field [0001] The invention relates to a hyperspectral image target detection method. Background technique [0002] The hyperspectral image sensor can obtain the reflected radiation information of ground objects through hundreds of spectral channels, and its band range covers from visible light to near infrared and even long wave infrared. Hyperspectral images contain spatial information, reflection or radiation information, and spectral information of ground objects at the same time, and its characteristic is usually called "map-spectrum integration". Moreover, hyperspectral image data provides nearly continuous spectral sampling information, which can record small reflection differences of ground objects in the spectrum, which can be used as the basis for classification and detection of ground objects. It has important theoretical significance and application value to study the new technology of hyperspectral image target detection. In the military aspect, it ca...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06T7/00
CPCG06T7/0002G06T2207/20068G06T2207/20021G06T2207/10032G06V20/194G06V20/13G06V10/757G06V2201/07G06F18/22
Inventor 谷延锋谭苏灵
Owner HARBIN INST OF TECH
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