ALOS is a high performance satellite sensor; the visible spectrum component can replace SPOT imagery. The paper fuses ALOS image with wavelet transform and local deviation. Wavelet transform can decompose image into low-frequency part and high-frequency part, condition manner will discard the low-frequency part or high-frequency part, and so the fusion result is general. Local deviation can hold low-frequency information, and associate the high-frequency information of multispectral and panchromatic image. The result shows that wavelet transform with local deviation can improve the performance of fusion image.
Wavelet transform is an important pretreatment tool to extract texture information, which can decompose
image as different level and provide rich detail information. But the tool is fit to any size spatial resolution image? The
paper gives several tests and extracts texture information from high and medium spatial resolution images, compares the
texture information; what's more, image classification is tested too. The result shows that wavelet transform cannot provide effective texture information for medium spatial resolution image.
System identification and damage detection for structural dynamic systems have received more and more attention in recent years. One of the time domain methodologies, the extended Kalman filter (EKF), has been widely applied in identifying the states and parameters of dynamic systems simultaneously. In EKF algorithm, the original dynamic systems have been transformed into nonlinear state-space system models. Therefore, the observability problem of the nonlinear state-space systems is required to be investigated before the application of EKF algorithm. In this paper, the definitions and the rank criterion of nonlinear observability for continuous-time systems are presented, which are only discussed in a few areas involved nonlinear systems previously. The analysis on the nonlinear observability of SISO and SIMO state-space structure-systems show that the rank criterion presented can provide sufficient conditions for the observability of the nonlinear systems to be identified. In practice, this criterion gives out a theoretical guideline for the selection of appropriate sensor types and locations before putting up the system identification, which is quite important but neglected in most of the EKF-identification literature thus far.
The modeling of moving objects is the basic of the information management of them. The common practice of moving objects’ modeling is to regard the objects as points which have different positions in the space at different time regardless of their structures, sizes, colors, etc. The General Motion Model (GMM) proposed here is good for representing both 2D and 3D moving objects. It combines sampling method and function method, and encapsulates all the data, including parameters and sampling data, and operations with object oriented method. GMM mixes discrete processing and continuous processing of motion data, and offers multiple functions to fulfill the needs of motion data LOD, thus gives users the option to balance processing speed and precision. Nonlinear interpolations and extrapolations could be applied to GMM as well as linear interpolations and extrapolations. GMM also gives users the flexibility of choosing interested dynamic attributes of moving objects in dynamic attributes set, which are not required to be presented explicitly, incrementally and separately as in other models. Experiments show that GMM is easy to implement and easy to operate. With proper indexing of motion data, GMM is also efficient in spatiotemporal data query.
Most spatial data organizations need automated conflation technology. For the same geographic area, an organization usually has several sources of spatial data, with each source differing in terms of available spatial features, attributes, resolution, accuracy, and other qualities. Processing multiple sources of spatial data, along with their respective differences and advantages, is a huge and ever increasing problem. The maintenance of spatial data is very costly and time consuming. This situation will become intensified when more and more digital spatial data are offered by using Internet technologies.In this paper the conflation technology for integration of spatial data from different source on the Internet is introduced. Conflation attempts to match the spatial data of the source and destination coverage. If the coverage have different origins, it is likely that the shapes, and even the locations of the features, do not match exactly. Most conflation algorithms only match similar features that are very close to each other. But indeed, spatial object is defined by its location, shape, attributes and relationship to others. Therefore, before the matching itself, the semantic relations, topological relations and geometrical matching technologies have to be probed. The research work is performed on road network, which are captured in different data models on the Internet. The approach is based on matching criterions between the spatial data of different data models. At first, the semantic relations have been considered, and then different data models compare with topological relations and select the similar data sets. The geometrical matching has been done in the selected data sets and chooses the best reasonable one for the conflation result. It can get more quickly speed than other conflation approach based on statistical investigations before.
In the region covered by variable amounts of vegetation, pixel spectra received by remotely-sensed sensor with given spatial resolution are a mixture of soil and vegetation spectra, so vegetation covering on soil influences the accuracy of soils surveying by remote sensing. Mixed pixel spectra are described as a linear combination of endmember signature matrix with appropriate abundance fractions correspond to it in a linear mixture model. According to the principle of this model, abundance fractions of endmembers in every pixel were calculated using unsupervised fully constrained least squares(UFCLS) algorithm. Then the signature of vegetation correspond to its abundance fraction was eliminated, and other endmember signatures covered by vegetation were restituted by scaling their abundance fractions to sum the original pixel total and recalculating the model. After above processing, de-vegetated reflectance images were produced for soils surveying. The accuracies of paddy soils classified using these characteristic images were better than that of using the raw images, but the accuracies of zonal soils were inferior to the latter. Compared to many other image processing methods, such as K-T transformation and ratio bands, the linear spectral unmixing to removing vegetation produced slightly better overall accuracy of soil classification, so it was useful for soils surveying by remote sensing.
Image fusion is an important content for digital image processing. For the past research, the method would be good at either the low frequency information or the high frequency information. For example, the fusion method based on high-pass filter of wavelet transform (HPFWT) is good at retaining detail information, and the method based on local deviation of wavelet transform (LDWT) is specialize in preserving multi-spectral information. It would be great if the two methods are combined. Therefore, the paper combines local deviation and high-pass filter to fuse image. The result indicates that this method can improve the detail information comparing with LDWT, enhance the spectral information comparing with HPFWT.
The paper researches texture extraction using wavelet transform. After introducing the wavelet transform and the texture analysis methods, the image is decomposed by wavelet transform, and the sub-images are gained. Secondly, the paper takes entropy and mean as texture parameter, so the texture image is an entropy or mean image. Finally, the image is classified by the spectral and texture information. The size of the texture calculating window and the treatment to the sub-image are researched in this paper. On condition that the spectral classification adding with texture feature, the precision will improve 4% averagely. Wavelet transform can decomposed image at several levels, so it can provide many information to classify and extract, which is helpful to those applications. Because of the texture window, texture image has fuzzy edge, it will lead to error for the image that have fine object or the area with different objects interleaved.
Moving objects are complicated to manage because they involve temporal attributes as well as spatial attributes. There are two methods to represent the motion of moving objects, function method and sampling method. Motion state modeling, based on sampling method, can give object's position, orientation and their changes at a specific epoch, and encapsulates all the calculation by object orientation method. A big job is to search the motion state vectors efficiently, which can be performed with the help of 2<sup>n</sup> index trees. 2<sup>n</sup> index tree is an efficient index method to multi-dimensional data. Different kinds of motion data retrieval can be transformed to basic searching in 2<sup>n</sup> index trees. With proper operation algorithm, 2<sup>n</sup> index trees work well with the indexing and retrieval of moving objects.