In order to overcome errors caused by season, shooting angle, and other factors in multitemporal remote sensing image change detection, a method based on old-temporal vector data and new-temporal image data is proposed under the premise that changes of images are normally less than the unchanged ones. Getting the object through incremental segmentation under the constraints of the previous vector data, we extract its textural and spectral features to get the dataset by the transform of principal component analysis. After this, the isolation forest method is used to calculate the object’s change index, and the change threshold is obtained by the Bayes method. We conduct two experiments. The effectiveness of the proposed method was verified by comparing image–image and vector–image change detection methods as well as Mahalanobis distance and isolation forest change methods for which the accuracy rate of experiment 1 is 92.35% and that of experiment 2 is 93.18%.
This paper proposes a method of hyperspectral image dimensionality reduction based on automatic subspace partition, k-means clustering based on mutual information and adaptive band selection. This method first automatic subspace division method is used to determine the initial subspace, in various initial subspace through the mutual information between image variance and band and K - means to determine the clustering center and clustering center from two adjacent band selection and their mutual information between the difference between the absolute minimum band as a boundary to delimit the molecular space, and then in the subspace of division of each band is obtained by applying the method of adaptive band selection index, get the biggest index of each subspace of band and from big to small order according to the index, at last in the first three band is the selection of bands. OMIS hyperspectral data were used to conduct experiments, and this method has a higher classification accuracy than the previous band selection methods.
According to the characteristics of fish-eye camera, such as large field of view and super short focal length, the traditional camera calibration algorithm based on the small hole imaging model cannot achieve the calibration. This paper proposed a fish-eye camera calibration optimization based on the traditional Kannala model. Firstly, the camera imaging model and distortion type of the fish-eye camera are studied, and on the basis of the traditional Kannala model, the piecewise polynomial approximation model is established to realize the original model optimization. Then, the intrinsic parameters and distortion coefficients of the camera are obtained according to the traditional Kannala model and the optimization model,and the distortion correction images are obtained by intrinsic parameters and distortion coefficients. Finally, the advantages of this algorithm are quantitatively and qualitatively analyzed by using the re-projection error and the multiview stereo vision 3D reconstruction of the distorted correction image. The results indicate that the camera parameters and distortion coefficients were obtained by calibration to correct the original image and to carry out 3D reconstruction of multi-view stereo vision, and the reverse projection error analysis and 3D reconstruction visualization of the camera check are proved to be effective in the calibration of the optimized model camera.
This paper proposes a method of building a semantic segmentation method for high-resolution remote sensing images of conditional random fields. Through a large number of actual data operations comparison, U-Net semantic segmentation model is selected as the improved basic model in many deep convolutional neural network models. In order to improve the singularity of the upsampling operation, the U-Net semantic segmentation model is improved as follows: First, the model's crop-copy connection structure is changed to the pyramid pooling layer, and then the multi-scale representation feature image is used, and the multi-scale is used. The resampling of the feature image and the fine bilinear interpolation yield the maximum response at different scales. The improved U-Net model extracts more complete image features. The rough segmentation results are used as the initial input values of the fully connected conditional random fields (CRFs). The global pixel potential energy is inferred through the fully connected graph, and the feature images are refined. Target matching. Finally, the image features are input to the sigmoid classifier for analysis. The results show that the CRF-SUNet model with introduced conditional random field has high segmentation precision, and the boundary of the segmented building is clear, smooth and complete.
This paper proposes a method of semiautomatic right-angle building extraction from very high resolution (VHR) aerial images, based on graph cuts with star shape constraint and regularization. In the proposed method, first, the image block containing the target building is obtained by a seed line from humans. Next, the image block is preprocessed by bilateral filtering and then the simple linear iterative clustering oversegmentation method was used to segment the image into small regions. Then regions that contain the pixels belonging to the seed line are merged as the foreground area, and the background area is automatically composed by annular regions near the imagery boundary. With the obtained foreground and background areas, the graph cuts model with the star shape prior is used to acquire the building object. Finally, we defined a method of detecting the building orientation and proposed a regularizing building method based on the corners and the building orientation. The experiments performed on two VHR aerial images demonstrate the accuracy and stability of the proposed method.
With the purpose of guarantee the computer information exchange security between internal and external network (trusted network and un-trusted network), A non-contact Reading code method based on machine vision has been proposed. Which is different from the existing network physical isolation method. By using the computer monitors, camera and other equipment. Deal with the information which will be on exchanged, Include image coding ,Generate the standard image , Display and get the actual image , Calculate homography matrix, Image distort correction and decoding in calibration, To achieve the computer information security, Non-contact, One-way transmission between the internal and external network , The effectiveness of the proposed method is verified by experiments on real computer text data, The speed of data transfer can be achieved 24kb/s. The experiment shows that this algorithm has the characteristics of high security, fast velocity and less loss of information. Which can meet the daily needs of the confidentiality department to update the data effectively and reliably, Solved the difficulty of computer information exchange between Secret network and non-secret network, With distinctive originality, practicability, and practical research value.
KEYWORDS: 3D modeling, Clouds, 3D image processing, Cameras, Data modeling, Calibration, Atomic force microscopy, Orthophoto maps, Photogrammetry, Image processing
This paper outlines a low-cost, user-friendly photogrammetric technique with nonmetric cameras to obtain excavation site digital sequence images, based on photogrammetry and computer vision. Digital camera calibration, automatic aerial triangulation, image feature extraction, image sequence matching, and dense digital differential rectification are used, combined with a certain number of global control points of the excavation site, to reconstruct the high precision of measured three-dimensional (3-D) models. Using the acrobatic figurines in the Qin Shi Huang mausoleum excavation as an example, our method solves the problems of little base-to-height ratio, high inclination, unstable altitudes, and significant ground elevation changes affecting image matching. Compared to 3-D laser scanning, the 3-D color point cloud obtained by this method can maintain the same visual result and has advantages of low project cost, simple data processing, and high accuracy. Structure-from-motion (SfM) is often used to reconstruct 3-D models of large scenes and has lower accuracy if it is a reconstructed 3-D model of a small scene at close range. Results indicate that this method quickly achieves 3-D reconstruction of large archaeological sites and produces heritage site distribution of orthophotos providing a scientific basis for accurate location of cultural relics, archaeological excavations, investigation, and site protection planning. This proposed method has a comprehensive application value.
KEYWORDS: 3D image reconstruction, 3D modeling, 3D image processing, Visualization, Feature extraction, Data modeling, Image retrieval, Image compression, Image processing, Visual process modeling
Image matching is the main flow of a three-dimensional reconstruction. With the development of computer processing technology, seeking the image to be matched from the large date image sets which acquired from different image formats, different scales and different locations has put forward a new request for image matching. To establish the three dimensional reconstruction based on image matching from big data images, this paper put forward a new effective matching method based on visual bag of words model. The main technologies include building the bag of words model and image matching. First, extracting the SIFT feature points from images in the database, and clustering the feature points to generate the bag of words model. We established the inverted files based on the bag of words. The inverted files can represent all images corresponding to each visual word. We performed images matching depending on the images under the same word to improve the efficiency of images matching. Finally, we took the three-dimensional model with those images. Experimental results indicate that this method is able to improve the matching efficiency, and is suitable for the requirements of large data reconstruction.
In order to retrieve the positioning image efficiently and quickly from a large number of different images to realize the three-dimensional spatial positioning, in this article, based on photogrammetry and computer vision theory, a new method of three-dimensional positioning of big data image under the bag of words model guidance is proposed. The method consists of two parts: image retrieving and spatial positioning. First, complete image retrieval by feature extraction, K-means clustering, bag of words model building and other processes, thus improve the efficiency of image matching. Second, achieve interior and exterior orientation element through image matching, building projection relationship and calculating the projection matrix, and then the spatial orientation is realized. The experimental result showed that the proposed method can retrieve the target image efficiently and achieve spatial orientation accurately, which made a beneficial exploration for achieving space positioning based on big data images.
This paper presents a novel method for hyperspectral image classification based on the minimum noise fraction (MNF) and an approach combining support vector machine (SVM) and linear discriminant analysis (LDA). A new SVM/LDA algorithm is used for the classification. First, we use MNF method to reduce the dimension and extract features of the image, and then use the SVM/LDA algorithm to transform the extracted features. Next, we train the result of transformation, optimize the parameters through cross-validation and grid search method, then get a optimal hyperspectral image classifier. Finally, we use this classifier to complete classification. In order to verify the proposed method, the AVIRIS Indian Pines image was used. The experimental results show that the proposed method can solve the contradiction between the small amount of samples and high dimension, improve classification accuracy compared to the classical SVM method.
The robust and rapid matching of oblique UAV images of urban area remains a challenge until today. The method
proposed in this paper, Nicer Affine Invariant Feature (NAIF), calculates the image view of an oblique image by making
full use of the rough Exterior Orientation (EO) elements of the image, then recovers the oblique image to a rectified
image by doing the inverse affine transform, and left over by the SIFT method. The significance test and the left-right
validation have applied to the matching process to reduce the rate of mismatching. Experiments conducted on oblique
UAV images of urban area demonstrate that NAIF takes about the same time as SIFT to match a pair of oblique images
with a plenty of corresponding points and an extremely low mismatching rate. The new algorithm is a good choice for
oblique UAV images considering the efficiency and effectiveness.
Camera calibration is essential to obtaining three-dimensional information from two-dimensional
image, this paper combines the method of photogrammetry and computer vision, put forward a kind
of camera self-calibration based on hierarchical reconstruction and bundle adjustment. The
projective reconstruction is obtained by SVD of the measurement matrix, Kruppa equation are
deduced for calculating the camera parameters, then upgrade projective reconstruction to Euclidean
reconstruction. Executing overall optimization to solve the inner orientation elements of the camera
and the lens distortion parameters by bundle adjustment .Characteristics of this method is simple, not
requested to build the field of high-precision control, just around the target for three or more images,
the inner orientation elements of the camera and distortion parameters are solving ,achieving the
camera self-calibration.
In this paper, based on the theory of down-scaling, we propose the methods for linear mixed model disaggregate mixed
pixels in coarse resolution images. Exploiting information about within mixed pixel each component fractional cover
derived from high spatial resolution classification map, Sub-pixel reflectance for the different land-cover classes are
calculated by solving a linear system of equations for each pixel of a coarse resolution image and producing the subpixel
level NDVI time series curve of different component. Results showed that application of the algorithm provided
good estimates of sub-pixel NDVI time series even for poorly represented land-cover classes. The main advantage of the
proposed technique is that could analysis the land-use and vegetation biomass change better.
KEYWORDS: 3D modeling, 3D image processing, Data modeling, Photogrammetry, Digital photography, 3D image reconstruction, Cameras, Calibration, 3D acquisition, Digital imaging
In this paper, we create the geospatial data of three-dimensional (3D) modeling by the combination of digital
photogrammetry and digital close-range photogrammetry. For large-scale geographical background, we make the
establishment of DEM and DOM combination of three-dimensional landscape model based on the digital
photogrammetry which uses aerial image data to make "4D" (DOM: Digital Orthophoto Map, DEM: Digital Elevation
Model, DLG: Digital Line Graphic and DRG: Digital Raster Graphic) production. For the range of building and other
artificial features which the users are interested in, we realize that the real features of the three-dimensional
reconstruction adopting the method of the digital close-range photogrammetry can come true on the basis of following
steps : non-metric cameras for data collection, the camera calibration, feature extraction, image matching, and other
steps. At last, we combine three-dimensional background and local measurements real images of these large geographic
data and realize the integration of measurable real image and the 4D production.The article discussed the way of the
whole flow and technology, achieved the three-dimensional reconstruction and the integration of the large-scale threedimensional
landscape and the metric building.
A new method for 3D rigid motion estimation from binocular sequence image of computer system in intelligence vision supervises is proposed in this paper. The appealing feature of this method is that it combines stereo vision and movement vision in computer vision literature, using the ideas in digital photogrammetry theory, fulfilling movement object location, measure and tracking by the object side image analyze method. It includes camera calibration; stereo-movement double matching restrict; calculation of objects movement parameters; pan-tilt unit movement control etc contents. The experimental result that pan-tilt unit movement control, which obtains object moves with uniform velocity and uniform acceleration in the straight line from the real binocular sequence images by the mentioned method, are presented.
The sequence image 3D movement analysis is method that estimates 3D movement parameter from 2D image sequence or 3D "image" (object side) sequence. In theory, monocular and binocular sequence image all can fulfill the three dimensions movement analyses, but there are distinctions in the complexity of computing and accuracy of computing result. In order to compare the accuracy of estimates 3D movement parameter from 2D image sequence or 3D "image" sequence, the article uses ideas of "relative orientation" and "space similitude transform" in photogrammetry for reference, presents an approach that connects the image data with real three dimensions space by making use of the result of calibration and other additional conditions to unify the computing result of monocular and binocular sequence image to object side coordinate system which origin point is one fixed point in object side, this make it possible to compare their results. The experiment results of real data, which use the method, are given.
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