34 results about "Multinomial logistic regression" patented technology

The invention discloses a method for classifying hyperspectral images on the basis of a combination of unmixing and adaptive end member extraction. The method is used for solving the technical problem of large errors of an existing method for classifying hyperspectral images on the basis of spectral unmixing. The technical scheme includes that the method comprises steps of roughly classifying the images, and extracting end member sets of various categories by the aid of a confusion matrix; linearly spectrally unmixing training samples in the various categories by the aid of the acquired end member sets, and acquiring optimal classification results by the aid of a probability classifier with an abundance value optimized on the basis of multinomial logistic regression; updating the end member sets of the various categories according to the classification results; iterating the procedure, and continuously optimizing the classifier so that the classification accuracy is improved. The method has the advantage that as shown by test results, the average accuracy of tests on a simulated data set, the average accuracy of tests on data of a true hyperspectral data set AVIRIS Indian Pine and the average accuracy of tests on data of a true hyperspectral data set ROSIS Pavia University are 81.98%, 62.19% and 82.38% respectively.

The present invention discloses a sketch structure-based mean field variational Bayes synthetic aperture radar (SAR) image segmentation method. The method mainly solves the problem in the prior art that the SAR image is not segmented accurately. The method comprises a first step of sketching the SAR image, so as to obtain a sketch of the SAR image; a second step of dividing pixel sub-spaces of the SAR image according to an area chart of the SAR image; a third step of segmenting based on the pixel sub-spaces of a hybrid gathering structure of a mean field variational Bayes deduction network model; a fourth step of segmenting based on an independent target of a sketch line gathering feature; a fifth step of segmenting based on a line target of a visual semantic rule; a sixth step of segmenting based on a homogeneous area pixel sub-space of a polynomial logic regression prior model; and a seventh step of combining segmentation results, so as to obtain a segmentation result of the SAR image. Through adoption of the method, a good segmentation effect of the SAR image is obtained and can be used for semantic segmentation of the SAR image.

The invention discloses a curvelet filter and convolutional structure learning-based SAR image segmentation method, and mainly solves the problem of inaccurate SAR image segmentation in the prior art. The method is implemented by the steps of (1) performing SAR image sketching to obtain a sketch; (2) according to a regional chart of an SAR image, dividing pixel sub-spaces of the SAR image; (3) constructing a curvelet filter set; (4) building a convolutional structure learning model; (5) by adopting a curvelet filter and convolutional structure learning model-based SAR image segmentation method, segmenting pixel sub-spaces of a ground object adopting a mixed aggregation structure; (6) performing sketch line collection feature-based independent target segmentation; (7) performing visual semantic rule-based line target segmentation; (8) segmenting pixel sub-spaces of a homogeneous region by adopting a polynomial-based logic regression priori model; and (9) combining segmentation results to obtain an SAR image segmentation result. According to the method, good segmentation result of the SAR image is obtained; and the method can be used for semantic segmentation of the SAR image.

The invention discloses a partition method for a multiple-sclerosis damage area. The method comprises the following steps that preprocessing is carried out on a multi-sequence nuclear magnetic resonance image; manually marked training samples are learnt through a multinomial logistic regression method; classification is carried out on all pixels in the image after the preprocessing according to learnt parameters so that the possibility that all the pixels belong to different tissues can be obtained; an objective function is established through cooperation of classification probability and total variation regular terms, and the intermediate partition result of the multiple-sclerosis damage area is obtained by minimizing the objective function through a continuous max-flow algorithm; final damage area partitioning results are obtained by removing false positive areas according to the space distribution situations of the multiple-sclerosis damage. The partition method can improve the partition precision of the multiple-sclerosis damage area, and can be applied to effective detection of lesion areas in a brain nuclear magnetic resonance image.

The invention discloses an SAR image segmentation method based on a deconvolution network and sketch direction constraint. The invention mainly solves the problem that existing segmentation technologies are inaccurate in SAR image segmentation. The method comprises the implementation steps of 1, sketching an SAR image, thus acquiring a sketch; 2, dividing pixel sub-spaces of the SAR image according to an area chart of the SAR image; 3, training the deconvolution network; 4, clustering filter directions; 5, segmenting the pixel sub-space with a hybrid aggregation structure by using the SAR image segmentation method based on the deconvolution network and the sketch direction constraint; 6, performing independent target segmentation based on a sketch line gathering characteristic; 7, performing line target segmentation based on a visual semantic rule; 8, segmenting the homogeneous area pixel sub-space by using a polynomial-based logistic regression prior model; and 9, combining segmentation results, thus acquiring an SAR image segmentation result. According to the method provided by the invention, the good segmentation effect of the SAR image is acquired, and thus the method can be used for performing semantic segmentation on the SAR image.