322 results about "Land cover" patented technology

A computerised method for developing real property a land owner, builder, end buyers and a development manager are given participatory roles in the development process wherein returns produced by the development of land and realisation of development rights attaching to land are accesible to the land owner and other profit participants; realisation is not limited to receipt of a return on the land value only through the disposition of the land to a developer, and wherein the development can be carried out on a computer generated model of the land together with any improvements thereon and official titles to the real property can be issued by relevant authorities and a financial settlement able to occur on the titles prior to commencing and or completing any civil works or construction on the land.

The invention discloses a high-resolution remote-sensing multifunctional urban land spatial information generation method. According to the method, a complex system rank theory is introduced; an urban land spatial information classification system with three rank scales, i.e. a multifunctional target landscape type, a functional area type and a land cover type, which self-adapts to urban planningmanagement and environmental renovation, is proposed; and on the basis of realizing the treatment of fine correction and alignment on remote-sensing images of a Landsat TM, a Google Earth and auxiliary maps, the Landsat TM is applied to carrying out urban landscape type classification, a three-level rank classification type-merged combination and an information mining knowledge base are constructed, the classified information is merged to form a first-level classification result of urban land, the digital functional areas are classified into a second level and the land cover is classified into a third level under the constrained control of higher-level classification information. The method has the characteristics of low cost, high accuracy of classification and strong targeted application, and thus, the requirements on target applications, such as ecological urban design, urban environmental management and the like, are better met.

To achieve space correlation of pixel decomposition results and reduce noise problem caused by isolation, there is provided a decomposition apparatus and method for refining composition of mixed pixels in remote sensing images, including: a preprocessing step for temporarily determining the provability value of the composition ratio of the different land cover types of each pixel in the image, based on a received remote sensing image and spectral information, to obtain first material composition information; and a neighborhood correlation calculation step for analyzing the correlation between a main pixel and the neighboring areas by using the first material composition information of each of the pixels present in the neighboring areas within a predetermined range in which the pixels are adjacent to each other, and optimizing the first material composition information of the main pixel by the result of the correlation analysis, to obtain second material composition information.

The invention discloses an object-oriented building change detection method based on multi-feature fusion. The method includes: solving MBIs (morphological building indexes), texture features and SFA (slow feature analysis) graphs of image pixels; performing FNEA (fractal net evolution approach) splitting through the MBIs and the texture features; solving three feature values of each object, performing differencing, and solving a threshold by a K-means clustering algorithm to obtain a feature change graph; performing post-processing through AC indexes; solving weights of different feature change graphs by an entropy method, and setting thresholds according to the weights to obtain change images; performing post-processing by a voting method to obtain change detection results. The method has the advantages that building changes are detected through the MBIs, the SFA graphs and the texture features, the MBIs and the texture features are added to the FNEA splitting, the AC indexes are provided, and post-processing is performed by the voting method. A novel way for the application of high-resolution remote sensing images, land coverage and urban expansion is provided.

The invention discloses a method for classifying remote sensing images blended with high-space high-time resolution data by an object oriented technology, and relates to a method for classifying remote sensing images of an oriented object, which can be used for solving the problem that the previous method for classifying remote sensing images can not be used for distinguishing land cover types of 'foreign bodies with the same spectrum', and is not suitable for being applied to the remote sensing images with low-medium resolution ratio. The method provided by the invention comprises the following steps: carrying out filter processing by applying an SG (screen grid) filter; determining a time sequence curve of typical vegetational MODIS-NDVI (moderate resolution imaging spectroradiometer-normalized difference vegetation index) in the remote sensing image to be classified; segmenting a TM (thematic mapper) image, wherein each segmentation unit is used as an object; extracting the characteristic information of each object; extracting all non-vegetation objects; removing the non-vegetation objects, and taking the obtained vegetational objects as planar vectors to segment MODIS-NDVI time sequence data, so as to obtain corresponding biotemperature information acquired by each vegetational object; and determining the vegetational type, to which each object belongs; and completing the land cover classification. The method provided by the invention can be used for distinguishing the land cover types.

The invention discloses a land coverage change algorithm based on time-space analysis. The method comprises the following steps: S1, performing remote sensing data preprocessing; S2, performing NDVI distribution map calculation; S3, performing regression simulation on NDVI values in different land coverage type scopes; S4, performing threshold determining and land coverage type distribution map extraction; and S5, performing land coverage type change statistics. According to the invention, by taking remote sensing images of different time phases and land data (such as second national land data employed by the embodiments of the invention) of field investigation as a basis, image preprocessing is performed on the remote sensing images, and plantation normalization exponents of the different time phases are obtained. Model analyzing is performed on NDVI data of the different time phases and corresponding land field investigation data, an NDVI threshold scope corresponding to each type of land is determined, a quite clear land use type graph is obtained, and changing land coverage change data statistics is carried out.

The invention relates to a full convolution network based remote-sensing image land cover classifying method. The method comprises the following steps: S1, performing data enhancement on a data set with limited data quantity; and generating a training set of which the data quantity and quality meet the training requirement; S2, combining the improved full convolution network FCN4s and the improvedU type full convolution network U-NetBN; and building a remote-sensing image land cover classifying model; S4, minimizing the cross entropy loss as the decrease of random gradient; learning the optimal parameters of the model to obtain the trained remote-sensing image land cover classifying model; and S4, performing pixel class classifying prediction on the predicted remote-sensing image throughthe trained remote image land cover classifying model. According to the method, the properties of the FCN full convolution network and the U-Net full convolution network are combined, so that the remote-sensing image land cover classifying performance can be improved.

The invention relates to peat bog information extracting methods, in particular to a peat bog information extracting method based on ENVISAT ASAR, Landsat TM and DEM data, and solves the problem that peat bog and other bog types cannot be distinguished by a conventional method. The peat bog information extracting method includes step 1, preprocessing Landsat TM data; step 2, preprocessing ENVISAT ASAR data; step 3, re-sampling the ENVISAT ASAR data; step 4, acquiring an ENVISAT ASAR image; step 5, acquiring gradient data; step 6, extracting back scattering coefficient; step 7, determining optimal polarization mode waveband of the ENVISAT ASAR image; step 8, acquiring a division unit; step 9, extracting feature parameters; step 10, determining optimal classification waveband; step 11, establishing a classification decision-making tree; step 12, generating a soil covering type vector file; step 13, making a peat bog map. The peat bog information extracting method is applied to the field of peat bog information extracting.

The invention discloses a land cover classification method based on MODIS time series data, and relates to the field of land cover classifying. The land cover classification method aims at solving the problems that as for a traditional method, the using time is long, the minus deviation of the vegetation index is generated, and the accuracy of an SG reestablishment result is reduced. The land cover classification method based on the MODIS time series data specifically includes the following steps: (1) building an original curve; (2) carrying out filtering on the original curve to form an initial curve in a fitted mode; (3) building a cloudless image two-dimensional array of pixels of the initial curve; (4) setting a threshold value T, wherein Y is not equal to y; (5) processing the initial curve; (6) obtaining a rebuilt NDVI annual variation curve; (7) extracting vegetation growth season parameters for forming a feature image; (8) determining a final voting classification result. The land cover classification method is used for the land cover classification field based on the MODIS time series data.