Image matching is one of the key technologies in the image processing. In order to increase its efficiency and precision, a new method for image matching which based on the improved SURF and Delaunay-TIN is proposed in this paper. Based on the original SURF algorithm, three constraint conditions, color invariant model, Delaunay-TIN, triangle similarity function and photography invariant are added into the original SURF model. With the proposed algorithm, the image color information is effectively retained and the erroneous matching rate of features is largely reduced. The experimental results shows that this proposed method has the characteristics of higher matching speed, uniform distribution of feature points to be matched, and higher correct matching rate than the original algorithm does.
In recent years, the effect of urban heat island (UHI) is increasingly obvious with moving forward in further
urbanization process, which has become one of the prominent issues of environment. The image data of
Nanchang city supplied by Landsat 5 Thematic Mapper (TM) in September 2006 is used in this paper, and the
land surface temperature (LST) over the same period has been retrieved by using a mono-window algorithm
based on remote sensing technology. The classification of LST is subsequently fulfilled by the method of proper
density cutting. Characteristics of intensity and spatial distribution of UHI effect in Nanchang, as well as its
relationships with land use type and vegetation coverage degree (VCD) are discussed in detail. The result shows
that the phenomena of UHI are significantly presented in urban area with an inhomogeneous distribution, and
the degree of influence of UHI depends on types of land uses. The intensity of UHI effect has a significant
negative linear correlation with normalized difference vegetation index (NDVI). It is deduced that suitably
optimizing land use types and raising VCR are obvious and effective ways to reduce UHI.
At present, the dynamic change monitoring of urban ecological environment has became an important guarantee measure for urban management, planning and construction. In this paper, taking Nanchang city as a case study, the remote sensing ecological index (RSEI) which is based on the natural factors is used to study the changes of the urban ecological environment. The Landsat images in the three different time periods of 1996, 2005, and 2013 in Nanchang were selected. To extract the four factors of green level, moisture, dryness and heat respectively as sub-indexs of the ecological assessment, in which the single window algorithm was used to calculate the heat. Based on the four factors, the RSEI in each year was finally calculated. The results show that the ecological environment in Nanchang deteriorated in the past 17 years, the value of the RSEI has decreased from 0.385 in 1996 to 0.267 in 2005, falling by 30.65%, but the ecological environment has improved in the later period, with the value of RSEI value rising to 0.413, increased by 54.68% compared with the results in 2005. It is indicates that the urban ecological environment of Nanchang has been significantly improved after some effective measures such as urban greening, pollution control, environmental protection were taken.
Desertification is an alarming sign of land degradation in Henshan county of northwest china. Due to the considerable
costs of detailed ground surveys of this phenomenon, remote sensing is an appropriate alternative for analyzing and
evaluating the risks of the expansion of land degradation. Degradation features can be detected directly or indirectly by
using image data. In this paper, based on the Hyperion images of Hengshan desertification region of northwest china, a
new algorithm aimed at land degradation mapping, called Land Degradation Index (LDI), was put forward. This new
algorithm is based on the classified process. We applied the linear spectral unmixing algorithm with the training samples
derived from the formerly classified process so as to find out new endmembers in the RMS error imagine. After that,
using neutral net mapping with new training samples, the classified result was gained. In addition, after applying mask
processing, the soils were grouped to 3 types (Kappa =0.90): highly degraded soils, moderately degraded soils and
slightly degraded soils. By analyzing 3 mapping methods: mixture-classification, the spectral angle mapper and mixturetuned
matched filtering, the results suggest that the mixture-classification has the higher accuracy (Kappa=0.7075) than
the spectral angle mapper (Kappa=0.5418) and the mixture-tuned matched filter (Kappa=0.6039). As a result, the
mixture-classification is selected to carry out Land Degradation Index analysis.
Raw LIDAR data is a irregular spacing 3D point cloud including reflections from bare ground, buildings, vegetation and vehicles etc., and the first task of the data analyses of point cloud is feature extraction. However, the interpretability of LIDAR point cloud is often limited due to the fact that no object information is provided, and the complex earth topography and object morphology make it impossible for a single operator to classify all the point cloud precisely 100%. In this paper, a hierarchy method for feature extraction with LIDAR data and aerial images is discussed. The aerial
images provide us information of objects figuration and spatial distribution, and hierarchic classification of features makes it easy to apply automatic filters progressively. And the experiment results show that, using this method, it was possible to detect more object information and get a better result of feature extraction than using automatic filters alone.
3D spatial data model and modeling method is the core of 3D GIS application in different domains. There have been
many 3D data models or data structures investigated in the past years. In geology exploration domain, most stratums are
stratified and can be modeled by using multi-DEMs. In terms of a ore deposit, it is necessary to model its inner
structures. For achieving this purpose, a modeling method based on Quasi Tri-Prism Volume (QTPV) can be adopted,
which has the advantage of close integrating with samples data. Therefore, it is a feasible modeling method that adopting
hybrid 3D data model based on multi-DEMs and QTPVs in the 3D modeling of geology body. In this paper, a hybrid 3D
spatial data model based on multi-DEMs and Quasi Tri-Prism Volume (QTPV) is proposed. The proposed model is
composed of six primitives and six objects. The primitives are vertex, segment (edge, triangle side), triangle, side
quadrilateral, QTPV and DEMs, and the objects are point, line, face, solid, complex and spatial object. Data structures
and topological relations of the six primitives and two geological objects are designed in detail. Two modeling methods,
which are based on samples points and interpolation points, are designed separately. A set of simulation data and a set of
real borehole sample data are used to verify the prototype system developed in VC++ program language by us. The
research results show that the proposed model has better abilities of describing the surface and the inner structure of
spatial objects, and it is suitable for 3D modeling in geology exploration field.