63results about How to "Improve Change Detection Accuracy" patented technology

The invention discloses a method for carrying out change detection on a multi-level segmented remote sensing image, and is characterized in that the method comprises nine steps, and in the method, after carrying out denoising and smoothing and the like on the image, through merging patch objects, a change detection result of the remote sensing image is obtained finally. In the method, the patch objects subjected to segmentation are compared by using a direct comparison method firstly, if the patch objects are changed, the patch objects are subjected to secondary segmentation by using an algorithm, then characteristic values are extracted and compared. By using the method disclosed by the invention, the pseudo change information can be removed effectively, and the change detection accuracy can be improved; and compared with the existing methods, by using the method disclosed by the invention, a more detailed and complex change result can be obtained, thereby realizing the change detection on sub-patch objects.

The invention discloses an object-level high-resolution remote sensing image change detection method based on multi-scale fusion. A color texture segmentation algorithm JSEG is introduced to change detection and multi-scale feature extraction and analysis on an object is realized based on a J-image image sequence generated in the segmentation process of the JSEG algorithm. The algorithm makes comprehensive use of the shape, the size and the spectral and texture features of the object, introduces two different multi-scale fusion strategies based on a D-S evidence theory and weighted data fusion and further improves divisibility of changing categories and non-changing categories so as to construct a complete set of object-level change detection frames based on multi-scale fusion. The feasibility and effectiveness of the method are verified by respectively carrying out experiment on a high-resolution aerial remote sensing DOM image and an SPOT5 satellite remote sensing image and comparing detection effects of change detection methods of different object levels and pixel levels.

The invention provides a remote sensing image change detection method and system based on mask classification, and the method comprises the steps: 1, the preprocessing of a multiband remote sensing image; 2, the single-band separation of the multiband remote sensing image after preprocessing; 3, the constructing of a single-band difference image of two-phase single-band remote sensing images; 4, the construction of a change mask difference image, and cutting the change mask difference image based on an Otsu method; 5, the multiplication fusion of a change mask with one of the two-phase single-band remote sensing images, thereby obtaining a change region; 6, the clustering of the change region through a fuzzy C mean value method; and 7, determining the change type of the change region through combination of the prior knowledge of the ground object type of the other one of the two-phase single-band remote sensing images according to the clustering results of the change region. The method and system are resistant to nose interference, can alleviate local optimum effectively, can consider the qualitative and quantitative description of an image change process, and is high in change detection precision and reliability.

The invention discloses a GPS spatial-temporal trajectory-based road network topological change automatic detection method and system, and the method comprises the steps: step 1, establishing a local spatial-temporal trajectory vector based on a GPS spatial-temporal trajectory; step 2, establishing a local road topological vector based on road network data and the GPS spatial-temporal trajectory; step 3, measuring the similarity of the local spatial-temporal trajectory vector and the local road topological vector; step 4, extracting changed trajectory points and establishing a road topological relation of a local range of the changed trajectory points; step 5, comparing the road topological relation of the local range of the changed trajectory points with an original road network, thus obtaining the road network topological change. The method can automatically detect the topological and graph change of an urban road network, and realizes the change detection of a road network topology and a road network graph while improving the change detection precision of the road network; in addition, the invention has the advantages of low cost and efficiency.

The invention provides a wave band correction change detection method of remote sensing image fuzzy clustering and a system thereof. The method comprises the following steps: step1, carrying out pretreatment of a multi-band remote sensing image, which means filtering and enhancement processing of the multi-band remote sensing image; step 2, carrying out single band separation on the multi-band remote sensing image after the pretreatment and acquiring a single-band remote sensing image; step 3, constructing a single band difference image of two time-phase single-band remote sensing images; step 4, constructing a multi-band combined image based on two time-phase multi-band remote sensing images after the pretreatment and acquiring a mutual neighborhood information content of each pixel of the multi-band combined image; step 5, correcting the mutual neighborhood information content of the pixels of the multi-band combined image and acquiring a correction image; step 6, using a fuzzy C-means method to carry out change detection on the correction image. By using the method and the system of the invention, detection precision is high, an anti-noise interference ability is good and an automation degree is high.

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method based on a mixing model, which mainly solves the problems of single mode structure, poor matching degree and poorer speckle noise suppression of the traditional SAR image change detection technology. The SAR image change detection method comprises the following steps of: (1) implementing geometric registration and radiation correction for two images with different time phrases in a same area; (2) inputting images to be matched and corrected; (3) preprocessing the input image; (4) extracting difference images for the pre-processed images ; (5) constructing a mixing model of the difference images; (6) constructing an evaluation function and a penalty function in the mixing model; (7) solving the evaluation function to obtain an optimal threshold value; and (8) outputting a result image according to the optimal threshold value. According to the SAR image change detection method based on the mixing model, the mixing model is modeled according to the extracted different images, the problem that the pixel distribution of the SAR image cannot be perfectly matched in a single model is solved, the false change information is reduced, and a better detection result is obtained.

The invention discloses a method for detecting two-time-phase remote sensing image change. In the method for detecting two-time-phase remote sensing image change, a gaussian process classifier is combined with a Markov random field model, effective utilization of image space information is reinforced, and change detecting precision is improved.

The invention provides an unsupervised SAR image change detection method which is used for solving the technical problem existing in the prior art that detection accuracy and calculation efficiency are relatively low. The implementation steps include: filtering registered SAR images of two time phases; generating an initial difference diagram; performing saliency detection on the initial difference diagram to obtain a saliency difference diagram; utilizing a fuzzy C-means clustering algorithm to pre-classify the saliency difference diagram to obtain candidate training samples and uncertain samples; performing equalization processing on the candidate training sample set to obtain training samples; utilizing a PCA filter to extract characteristics of the training samples and characteristicsof the uncertain samples; and utilizing the characteristics of the training samples to train a support vector machine, and utilizing the trained support vector machine to classify the uncertain samples to obtain a change detection result. The unsupervised SAR image change detection method provided by the invention can reduce operation time while improving detection accuracy, and can be used for disaster assessment, disaster development tendency prediction, target detection, land covering and utilization monitoring and the like.

The invention discloses an SAR image change detection method based on a stack semi-supervised adaptive denoising auto-encoder, and aims at solving the problem that in an existing method, the precision of detection on coherent speckle noise points and change areas of many edges is low. The method comprises the steps that a multi-scale difference guide diagram is generated; a first time phase image is taken as the input to train an SDAE; the multi-scale difference guide diagram, the first time phase image and a second time phase image are taken as the input to train the SSADAE, and weights obtained in SDAE training are used in an SSADAE self-adaption error function; the feature vector of the first time phase image and the feature vector of the second time phase image are calculated by the SSADAE; and the feature vector of the first time phase image and the feature vector of the second time phase image are subtracted to obtain a difference vector, an FCM classification is conducted on the difference vector to obtain a change detection result diagram. According to the method, the multi-scale difference guide diagram is proposed firstly and can highlight the change areas in the difference diagram; and the SSADAE proposed later can improve the change detection accuracy by utilizing a small quantity of mark samples in the image.

An image change detecting system may include an image processor cooperating with the database for generating a reference geospatial image corresponding to the collected geospatial image, an image enhancer for enhancing at least one of the reference geospatial image and the collected geospatial image based upon at least one environmental condition, and a change detector cooperating with the image processor and the image enhancer. The change detector may detect a change between the collected geospatial image and the reference geospatial image with at least one thereof enhanced by the image enhancer based upon the at least one environmental condition. The environmental condition may include a weather condition, a time of day, or a time of year. The environmental condition may be typically associated with the collected geospatial image.

The invention discloses a non-local wavelet information based remote sensing image change detection method. The non-local wavelet information based remote sensing image change detection method mainly solves the problem that the remote sensing image change detection method is low in detection accuracy. Achieving steps of the non-local wavelet information based remote sensing image change detection method comprise (1) reading data; (2) constructing a difference chart; (3) performing wavelet decomposition; (4) denoising a high-frequency portion through a non-local information based method; (5) performing inverse transformation; (6) cutting through a local fuzzy C-mean clustering method. According to the non-local wavelet information based remote sensing image change detection method, the low-frequency information integrity is protected and noise is effectively removed due to the facts that the noise is mainly distributed on a high-frequency detail information portion and accordingly image denoising is performed on a high-frequency detail portion and a self-structure of the image is protected and the image robustness to the noise is improved due to the fact that a single pixel is processed in combination with vectors of neighborhood information.

The present invention discloses a remote sensing image change detection method based on Treelet and direction adaptive filtering, which comprises the following realizing steps of: (1) reading in data; (2) constructing a difference image; (3) direction adaptive filtering; (4) fusion of Treelets; (5) adaptive threshold value classification; and (6) post-processing. With the remote sensing image change detection method based on Treelet and direction adaptive filtering, marginal information of changing areas can be better kept and residual information of change detection results can be reduced as well. The method, having sound change detection accuracy, can be applied to fields such as dynamic monitoring of water levels of lakes, crop growth situation, urban planning, and military reconnaissance.

The invention discloses a remote sensing image binary change detection method based on feature deviation alignment. The method comprises the following steps: 1, constructing a dual-time-phase remote sensing image binary change detection data set and carrying out preprocessing; 2, constructing a binary change detection model based on feature deviation alignment, and giving a dual-temporal remote sensing image to obtain a change region prediction result and a change region auxiliary prediction map; 3, respectively calculating a main loss function and an auxiliary loss function by using a real change area label result, a prediction change area result and a change area auxiliary prediction map, carrying out back propagation on a gradient according to loss to update a model, stopping training until a loss value converges, and storing a model structure and a model weight; and 4, predicting test set data by using the model weight trained in the step 3. According to the invention, the false detection phenomenon of the change area of the dual-time-phase remote sensing image caused by factors such as multi-view shooting, too many high-rise buildings or large topographic relief can be effectively solved.

The invention discloses a bilateral depth feature domain heterogeneous remote sensing image change detection method. The method comprises the following steps: constructing a bilateral input symmetricconvolutional neural network; establishing a feature domain constrained probability model; iteratively optimizing the probability model, and solving a difference probability graph; and thresholding the difference probability graph, and outputting a binary change graph. According to the bilateral symmetric convolutional neural network, unsupervised feature automatic extraction of two heterogeneousimages can be realized, the probability of change is measured in a feature domain, the change detection false alarm rate caused by non-uniform illumination, small particle noise and the like can be overcome, the effect of a heterogeneous image difference graph is enhanced, and the change detection accuracy is improved.