This paper presents a novel Object-based context sensitive technique for unsupervised change detection in very high spatial resolution(VHR) remote sensing images. The proposed technique models the scene at different segment levels defining multiscale-level image objects. Multiscale-level image object change features is helpful for improving the discriminability between the changed class and unchanged class. Firstly according to the best classification principle as “homogeneity in class, heterogeneity between class”, A set of optimal scales are determined. Then a multiscale level change vector analysis to each pixel of the considered images helps improve the accuracy and the degree of automation, which is implemented on multiscale features fusion. The technique properly analyzes the multiscale-level image objects’ context information of the considered spatial position. The adaptive nature of optimal multiscale image objects and their multilevel representation allow one a proper modeling of complex scene in the investigated region. Experimental results confirm the effectiveness of the proposed approach.
The automatic generation of seamline along the overlap region skeleton is a concerning problem for the mosaicking of Remote Sensing(RS) images. Along with the improvement of RS image resolution, it is necessary to ensure rapid and accurate processing under complex conditions. So an automated seamline detection method for RS image mosaicking based on image object and overlap region contour contraction is introduced. By this means we can ensure universality and efficiency of mosaicking. The experiments also show that this method can select seamline of RS images with great speed and high accuracy over arbitrary overlap regions, and realize RS image rapid mosaicking in surveying and mapping production.
Digital change detection (CD) is the computerized process of identifying changes in the state of an object, or other earthsurface
features, between different dates. During the last years, a large number of change detection methods have been
proposed for change detection of multiple-temporal remote sensing images. Among these, change vector analysis (CVA)
is a very important and widely used method. The key of CVA is to determine change detection threshold. Change
detection threshold is a very valuable key for change detection precision. In the literature, many techniques to determine
change detection threshold have been proposed. However, most of them are not robust and operational since images are
diverse and complex, especially to very high resolution (VHR) data (e.g. images acquired by QuickBird, IKONOS,
SPOT5 and WorldView satellites). Such discrimination is usually performed by using empirical strategies or manual
trial-and-error procedures, which affect both the accuracy and the reliability of the change-detection process. In this
paper, we analyze the algorithm based on minimal classifying error, the algorithm based on OTSU and the algorithm
based on EM. To eliminate the complexity of VHR data, an improved algorithm based on EM is proposed. Suppose the
difference image meets the Mixed Gaussian distribution model. First, the grey histogram of the difference image is fitted
to the Mixed Gaussian Distribution Model (MGM). Then the change detection threshold is determined by the MGM
graph combing the Bayesian Criterion and the actual situation. In experiment, the semi-automatic method is effective and
operational.
The procedure of radiometric normalization is necessary as change detection (CD) is highly dependent on accurate
geometric and radiometric correction. The quality of radiometric normalization highly affects the results of change
detection. During the last years, many methods on radiometric normalization have been proposed. However, they are
generally proposed not for CD. In this paper, we will discuss these methods for CD. The ultimate objective of CD is
identifying changes in the state of an object, or other earth-surface features, between different data. If you can't properly
deal with radiometric normalization in CD, or the relationship between radiometric normalization and CD, you will fall
into trouble. With respect to the accuracy, efficiency and operation of radiometric normalization for CD in VHR(very
high resolution images such as spot5,QuickBird,Iknos), we design the processing procedure of radiometric normalization
for CD in multitemporal images. Moreover, an improved matching algorithm based on Harris corner detection is
described in this paper, which makes full use of graphic point feature, gray-level pixel and location information. In
experiment, the proposed procedure has been proved effective and can be recommended for use in CD projects.
Digital change detection is the computerized process of identifying changes in the state of an object, or other earthsurface
features, between different data. During the last years, a large number of change detection methods have evolved
that differ widely in refinement, robustness and complexity. Some traditional change detection methods could not any
more adapt to high resolution remote sensing images. The prime tendency of remote sensing change detection is from
pixels level to object level. In the paper, with respect to the views of object-oriented change detection in remote sensing
images, an unsupervised technique for change detection (CD) in very high geometrical resolution images is proposed,
which is based on the use of morphological filters. This technique integrates the nonlinear and adaptive properties of the
morphological filters with a change vector analysis (CVA) procedure. Different morphological operators are analyzed
and compared with respect to the CD problem. Alternating sequential filters by reconstruction proved to be the most
effective, permitting the preservation of the geometrical information of the structures in the scene while filtering the
homogeneous areas. We collect two multi-temporal SPOT5 remote sensing images to analyze YangSan island change
detection in this procedure as above mentioned. Experimental results confirm the effectiveness of the proposed technique.
It increases the accuracy of the CD in high remote sensing change detection as compared with the standard CVA
approach.
One of the important tasks about oceanic environment remote sensing is to real-time forecast the oceanic
environment unexpected abnormalities or disasters. We are requested to instantly detect, quickly process, exactly analyze
and forecast. As we known, it is not enough to estimate by gray. We can make full use of color difference of images to
obtain more environment information, synthesize spectrum information to quickly detect, exactly extract and forecast the
oceanic environment unexpected abnormalities. In this paper, we aim at algal overrun incident in Qingdao sea area of
China On August, 2008. We will discuss the method of using NDVI to detect and extract oceanic environment
abnormalities on color difference. Our research will establish the foundation of monitoring oceanic algal overrun by
remote sensing.
KEYWORDS: Raster graphics, Data integration, Remote sensing, Image fusion, Georeferencing, Data fusion, Geographic information systems, Data processing, Feature extraction, Software development
Data fusion is always a hot field in remote sensing research. It mainly includes checking, correlating, integrating and
estimating multiple kinds of Data. Integration of global remote sensing image and local vector data is often necessary. In
this paper, for global east Chinese high resolution remote sensing coast zone image and local vector data about ocean
planning, we will introduce one way based on ArcGIS, in which the two kinds of data would be rapidly integrated. First,
we change the vector data into raster data, then adjust these raster data precisely by Georeferencing module of ArcGIS,
then vector and integrate data which we needed by Arcscan module of ArcGIS, at last, we program custom codes to
solve the conflict between integrated new data and old data in VBA of ArcGIS. In the process, we wish to improve
efficiency through a good management of map raster data.
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