The invention discloses a multi-scale remote-sensing image segmentation method based on the local homogeneity index. The segmentation method comprises image quantization based on the bisecting K-means clustering, multi-scale segmentation based on surface feature context information and region merging based on SSIM and D-S evidence theory. According to the invention, coarse image quantization in JSEG is improved; the multi-scale segmentation based on the surface feature context information and the SSIM and the region merging strategy based on the D-S evidence theory are provided; by carrying out segmentation experiments on multiple groups of different sensor types of high-definition remote sensing images, it can be proved that the boundary of objectives can be precisely positioned via the disclosed algorithm; and the segmentation method is provided with quite high segmentation precision.

Multi-scale remote-sensing image segmentation method based on local homogeneity index

The present invention discloses texture segmentation and fusion based radar remote-sensing image artificial building recognition algorithm. The algorithm comprises the following steps of: determining image segmentation scale factors and logic mask segmentation scales according to sensor types; searching, calculating and screening space autocorrelation structure index features and gray scale co-occurrence matrix texture features; performing logic masking on space feature indexes and texture information according to the mask scales; operating and filtering mask results according to mathematical morphology; performing primary logical clustering on filtration results, and searching obvious building regions; performing secondary logical clustering according to primary search results, and rebuilding the algorithm in combination with the mathematical morphology; updating and perfecting the obvious building regions; rebuilding the sections through the mathematical morphology; and finally accurately obtaining a building information recognition result. The recognition ability of the mathematical morphology and the logical clustering on an SAR (synthetic aperture radar) image building is maximally excavated; and the final recognition precision of building information can be improved.

Texture segmentation and fusion based radar remote-sensing image artificial building recognition algorithm

The invention discloses a high-resolution remote sensing image unsupervised segmentation method based on scale parameter automatic optimization. The high-resolution remote sensing image unsupervised segmentation method mainly comprises three steps of 1) J-value self-adaptive SP selection based on a local homogeneity index; 2) image segmentation based on an inter-scale object boundary constraint strategy; and 3) region merging based on multiple features. The method is more accurate in object edge localization and more completed in object contour extraction through the experiment of multiple sets of high-resolution remote sensing images of different sensor types and comparison with the well-known commercial software eCognition and the conventional supervised segmentation method, and the segmentation process has no artificial intervention so that the high-resolution remote sensing image unsupervised segmentation method is a high-generality and effective unsupervised solution.

High-resolution remote sensing image unsupervised segmentation method based on scale parameter automatic optimization

The invention discloses a geometric constraint-based high-resolution optical satellite staring image registration method. The method comprises the steps of S100, building a free network adjustment directional model of images by utilizing a rational function model of additional image space error compensation; S200, based on the free network adjustment directional model, performing dense matching ona stereoscopic image pair with an optimal rendezvous condition in the images to obtain coordinates of dense homonymy points; S300, generating dense point cloud data, performing gross error point deletion on the dense point cloud data, and generating digital surface model data; and S400, based on the free network adjustment directional model and DSM data, performing geometric correction on the images in sequence. By utilizing geometric locating consistency, high-precision registration of high-resolution optical satellite multi-angle staring sequence images is realized; the multi-angle staringsequence image registration problem caused by satellite platform control inaccuracy and attitude measurement inaccuracy is solved; and a high-precision data basis can be provided for subsequent application.

Geometric constraint-based high-resolution optical satellite staring image registration method

In order to solve problems of over segmentation, under segmentation, and difficult accurate positioning of an object boundary during high-resolution remote sensing image segmentation, the invention discloses an improved-JSEG-algorithm-based urban high-resolution remote sensing image segmentation method. According to the method, an information-entropy-based multi-band fusion strategy is used for obtaining a unified multi-scale J-image sequence, so that the detailed information loss caused by over-rough image quantization; multi-scale minimum marking is carried out by using a local homogeneity index J-value for to determine a seed area reasonably; and then regional segmentation and merging are carried out by using JSEG. In the experiment, high-resolution IKONOS and WorldView images are selected and comparison with JSEG and WJSEG is carried out. The result demonstrates that the real boundary of an object can be localized accurately, under segmentation and over segmentation problems can be solved effectively, and the segmentation precision and reliability can be improved when the method is used.

Improved-JSEG-algorithm-based urban high-resolution remote sensing image segmentation method

The invention discloses a high-resolution remote sensing image segmentation method based on inter-scale mapping. The high-resolution remote sensing image segmentation method has the advantages that aiming at geographical object extraction involved in objet-level change detection, the high-resolution remote sensing image multi-scale segmentation method based on wavelet transform and an improved JSEG (joint systems engineering group) algorithm is provided; aiming at a key problem that a conventional JSEG algorithm affects segmentation accuracy in high-resolution remote sensing image segmentation, a corresponding improvement strategy is adopted to achieve a good effect; wavelet transform is introduced to serve as a multi-scale analysis tool, excessive rough color quantization of the conventional JSEG algorithm is abandoned, and thus, detail information in original images is kept to the utmost; a novel inter-scale segmentation result mapping mechanism is set up, image segmentation of a current scale is realized on the basis of a segmentation result of a previous scale, and correction of the segmentation result of the previous scale is also realized, and thus, accumulation of inter-scale segmentation errors is decreased effectively; finally, an improved multi-scale segmentation strategy and an improved region merging strategy are provided, and over-segmentation and mistaken merging are effectively reduced.

High-resolution remote sensing image segmentation method based on inter-scale mapping

The invention discloses a high-resolution remote sensing image registration method with control points distributed in an adaptive manner. By the use of the high-resolution remote sensing image registration method, a multi-scale J-image is introduced into image registration. A self-adaptive control points extracting method based on a partitioning strategy is provided to extract the control points of the multi-scale J-image image, thus the defect that the control points only sense specific directional high-frequency information is overcome. A self-adaptive control points extracting strategy is defined so as to limit control point distribution. The control points are subjected to multi-scale matching by NMI (normalized mutual information measure), allowing registration function smooth. Geometric correction is realized by adopting Delaunay triangle local transformation. In the following text, basic principle and key steps of the algorithm are introduced, and three remote-sensing image groups of different types are subjected to experiment and analysis and comparison experiments with various registration methods based on wavelet transform and NSCT (non-subsampled contourlet transform).

Huang Fengchen

Papers

High-resolution remote sensing image segmentation method based on inter-scale mapping

High-resolution remote sensing image registration method with control points distributed in adaptive manner