Coastal islands are located in a transitional environment where land and ocean interact. During the past 20 years, large
areas of the natural landscape of Zhoushan islands have been replaced by human features. There is limited research
detailing the amount of impervious surface growth in the Zhoushan islands and trends in the land environment using
remotely sensed technologies. The purpose of this paper is to assess the influences of anthropogenic activities on land
ecological environment in Zhoushan islands based on remotely sensed impervious surfaces. First the impervious surfaces
information of 1986, 1995 and 2006 were estimated using Landsat Thematic Mapper (TM) images, and then an
integrated state indicator was built. The results reveal that imperious surfaces areas (ISA) of Zhoushan islands
remarkably increased from 19.2 km<sup>2</sup> (2.73%) in 1986 to 29.5 km<sup>2</sup> (4.20%) in 1995, and to 58.2 km<sup>2</sup> (8.27%) in 2006.
The average state value for the total area was 0.70, 0.65, and 0.55 respectively for 1986, 1995, and 2006. Of the
surrounding islands, the Lujiazhi, Xiaogan, Panzhi, and Cezi suffered the most land disturbance intensity from human
activities, followed by the Jintang, Xiushan, and Damao, and the Changbai and Changzhi had the lowest disturbance
values. It indicates that the land ecological environment of Zhoushan islands was variously disturbed by growing human
activities over time. Further, we found that the topography, island size, spatial location from Zhoushan Island and
economic policy had an influence on change of impervious surfaces for each island.
Modulation transfer function (MTF) is applied to the high frequency modulation fusion in this paper. Firstly, MTFs are
calculated using the edge method, and 2-dimension MTF-filters are properly designed. Secondly, MTF-filters are used
for degrading original high resolusion images. High frequency modulation fusion parameters are then obtained under the
minimum mean square error criterion. The results show that fusion images derived from the improved high frequency
modulation based on MTF method have spatial resolution close to non-degraded pan images. Compared with fusion
methods of weighted high-pass filtering (w-HPF), MTF general image fusion framework (MTF-GIF), the improved
method performs well in terms of preservation of spectral information and spatial resolution.
A new image fusion method of SAR, Panchromatic (Pan) and multispectral (MS) data is proposed. First of all, SAR
texture is extracted by ratioing the despeckled SAR image to its low pass approximation, and is used to modulate high
pass details extracted from the available Pan image by means of the á trous wavelet decomposition. Then, high pass
details modulated with the texture is applied to obtain the fusion product by HPFM (High pass Filter-based Modulation)
fusion method. A set of image data including co-registered Landsat TM, ENVISAT SAR and SPOT Pan is used for the
experiment. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured
areas (buildings and road network) where SAR texture information enhances the fusion product, and the proposed
approach is effective for image interpret and classification.
The research presented in this paper is aimed at the development of multisensor image fusion. The proposed approach is suitable for integration pan-sharpening of multispectral (MS) bands and SAR imagery based on intensity modulation through the a-trous wavelet transform (ATWT) and the curvelet transform(CT). The ATWT is suitable for dealing with objects where the interesting phenomena, e.g., singularities, are associated with exceptional points, and CT as a new multiscale geometric analysis algorithm is more appropriate for the analysis of the image edges and has better approximation precision and sparsity description. This proposed fusion algorithm makes full use of advantages of these multiscale analysis tools, thus it extracts SPOT-Pan high-pass details from the panchrmomatic image by means of the ATWT and SAR texture and edges by details and rationing the despeckled SAR image to its lowpass approximation derived from the CT.SPOT-Pan high-pass details and SAR texture and edges are used to modulate intensity derived from IHS transform of MS bands. SPOT-Pan, Landsat-MS and Radarsat-SAR images covering a region of sanshui in Guangdong province are used to evaluate the effect of the proposed method. The experiment result shows that the proposed algorithm has greatly improved spatial resolution while it keeps the spectral fidelity.
This study sought to develop a modified change vector analysis(CVA) using normalized multi-temporal data to detect
urban vegetation change. Because of complex change in urban areas, modified CVA application based on NDVI and
mask techniques can minify the effect of non-vegetation changes and improve upon efficiency to a great extent.
Moreover, drawing from methods in Polar plots, the extended CVA technique measures absolute angular changes and
total magnitude of perpendicular vegetation index (PVI) and two of Tasseled Cap indices (greenness and wetness). Polar
plots summarized change vectors to quantify and visualize both magnitude and direction of change, and magnitude is
applied to determine change pixels through threshold segmentation while direction is applied as pixel's feature to
classifying change pixels through supervised classification. Then this application is performed with Landsat ETM+
imageries of Wuhan in 2002 and 2005, and assessed by error matrix, which finds that it could detect change pixels
95.10% correct, and could classify change pixels 91.96% correct in seven change classes through performing supervised
classification with direction angles. The technique demonstrates the ability of change vectors in multiple biophysical
dimensions to vegetation change detection, and the application can be trended as an efficient alternative to urban
vegetation change detection and classification.
Image fusion technique has been widely applied to remote sensing image analysis and processing, and methods for quality assessment of image fusion in remote sensing have also become the research issues at home and abroad. Traditional assessment methods combine calculation of quantitative indexes and visual interpretation to compare fused images quantificationally and qualitatively. However, in the existing assessment methods, there are two defects: on one hand, most imdexes lack the theoretic support to compare different fusion methods. On the hand, there is not a uniform preference for most of the quantitative assessment indexes when they are applied to estimate the fusion effects. That is, the spatial resolution and spectral feature could not be analyzed synchronously by these indexes and there is not a general method to unify the spatial and spectral feature assessment. So in this paper, on the basis of the approximate general model of four traditional fusion methods, including Intensity Hue Saturation(IHS) triangle transform fusion, High Pass Filter(HPF) fusion, Principal Component Analysis(PCA) fusion, Wavelet Transform(WT) fusion, a correlation coefficient assessment method based on IHS cylindrical transform is proposed. By experiments, this method can not only get the evaluation results of spatial and spectral features on the basis of uniform preference, but also can acquire the comparison between fusion image sources and fused images, and acquire differences among fusion methods. Compared with the traditional assessment methods, the new methods is more intuitionistic, and in accord with subjective estimation.
The change detection of land use and land cover has always been the focus of remotely sensed study and application. Based on techniques of image fusion, a new approach of detecting vegetation change according to vector of brightness index (BI) and perpendicular vegetation index (PVI) extracted from multi-temporal remotely sensed imagery is proposed. The procedure is introduced. Firstly, the Landsat eTM+ imagery is geometrically corrected and registered. Secondly, band 2,3,4 and panchromatic images of Landsat eTM+ are fused by a trous wavelet fusion, and bands 1,2,3 of SPOT are registered to the fused images. Thirdly, brightness index and perpendicular vegetation index are respectively extracted from SPOT images and fused images. Finally, change vectors are obtained and used to detect vegetation change. The testing results show that the approach of detecting vegetation change is very efficient.
Image fusion is a technique of obtaining high spatial resolution multi-spectral images from low spatial resolution multi-spectral and high spatial resolution panchromatic images. Various techniques exist to perform such fusion. These techniques, however, do not seem to preserve the spectral information content of original multi-spectral image in the fused image. Hence, in this study a recent and efficient technique of fusion based on the Laplace pyramid was attempted and its efficiency was compared with that of the a trous wavelet transformation techniques. Accordingly, a lower resolution multi-spectral image of Ikonos and its high-resolution panchromatic image were fused using the Laplace pyramid and the a trous wavelet transformation fusion technique. The outputs were evaluated using visual comparison, statistical entropy, average gradient and correlation coefficient. Compared with a trous wavelet transformation techniques, the Laplace pyramid technique proved to be a better option since it preserved most of the spectral information content and improved spatial information.