Forest fire is a kind of worldwide natural calamity. It is extensively distributed with high occurrence frequency and destroys forest resources thus disturbing normal living order of people and leading to environmental deterioration. The appearance and development of modernistic new high
technologies, such as Remote Sensing (RS), Geographic Information System (GIS) and Internet, have given more convenience for preventing and decreasing disaster than before. In this study, basing on analyzing the information of related bands of Advanced Along Track Scanning Radiometer (AATSR) and integration of background GIS data, a thread condition forest fire identification methodology has been developed. At the same times, the fire identification results by using AATSR and Moderate Resolution Image Spectroradiometer (MODIS) data have been tested in northeast of China.
Wetland is a land with seasonal or yearly water, on which lives with specific vegetations and animals. Wetland specifies with both ecologic and scientific values. With the study area of international importance, supervised classification supported by exports was approached successfully to Landsat TM based on the scale of community. Our research indicated that efficient preprocess of image enhancement and precise selection of signatures is the key to classify succesfully. Spatial structure of virgin riparian wetlands of North China was presented according to the classifiation result of community types. Our research also demonstrated that very high proportion of Ass.Calamagrostis angustifolia-sundry herbs within the study area made it clear the occurent loss of the wettability of study area as typical mires in North China, so that urgent measurements were requested in case of further degeneration.
Based on winter wheat' s canopy spectra data,the spectral parameters which are selected by linear regression method and Generalization Regression Neural Network(GRNN) method is as network input, canpoy chlorophyll Concentration as network output,we use three models,linear regression model, Back Propagation neural network(BPNN)and GRNN to inverse chlorophyll concentration. The result shows BPNN and GRNN have higher estimation precision than linear regression model, the RMSEP are 0.52、0.36 and 0.98 respectively. Due to adapation to small sample study and needless iterate repeatly, GRNN' s froecast abality,generalization and study speed is better than BPNN.
In order to determine the chlorophyll-a concentration of inland water using remote sensing, field hyperspectral data and water samples were collected in the Lake Xinmiao of China from May to September in 2004. Through analyzing the correlation between data measured in laboratory and hyperspectral reflectance, it was found that single band reflectance could not denote the chlorophyll-a concentration well. The reflectance peak near 700 nm and absorption vale near 680 nm were the most important features.Three regressive linear fitting models were set up respectively between chlorophyll-a concentration and other factors as follows: the ratio of reflectance peak near 700 nm to absorbance vale near 680 nm, the first derivative of reflectance near 700 nm, the distance from reflectance peak to absorbance vale. All of the models gave good results with high determination coefficients 0.7834, 0.7927 and 0.7969 respectively.The determination coefficients of testing models were 0.6513, 0.4317, 0.7564 respectively and root mean square error (RMSE) were 8.69μg·L-1, 14.50μg·L-1, 10.04μg·L-1-1with significance levelP<0.01. By comparison of RMSE and two kinds of determination coefficients, the third model was the best to predict chlorophyll-a contents in the Lake Xinmiao, China.
In recent year, ship detection and monitoring techniques have been widely used in many areas, one of which is the fishermen monitoring and protection of marine resources. Researches of monitoring and managing the fishermen are being carried through in china in order to protect the marine resources.Ship detection techniques are introduced; the ship detection algorithms and wake detection algorithms have been both reviewed and analyzed. The stages of ship detection applications have been described as well. At the end of this paper, some successful applications, in which ship detection techniques based on spaceborne SAR are used in fishery monitoring and marine resource protection in foreign countries, are introduced. It is expected that some experiences and illuminations will be obtained from this paper, which will benefit our future works in fishermen monitoring and protection of fishery resources based on spaceborne SAR.
In comparison with multispectral remote sensing,the characteristics of hyperspectral remote sensing data with more channels, narrower bandwidth and larger amount of data have caused many problems to further management. In order to solve these problems, this paper introduced an improved
adaptive band selection method of dimensional reduction, which can' t only obtain the bands with the high information content and the low correlativity, but also the bands with the longer divisibility distance between the continous spectrals of all ground objects. It shows that the divisibility distance between the spectrals of the arbitrary two ground objects is more long, these two kinds of ground objects appear more clearly. At first, the ideal band subsets are choiced, from which the optimal three bands combination(not unique) with high information content, smaller correlation coefficient and longer divisibility distance are further choosed based on the lowest correlation coefficient sum and the lowest mean square deviation. In order to testify the effect of the improved adaptive band selection method, the spectral angle mapping classification method is implemented on the false color synthesis image of the channels 45、75 and 85.The results of the classification show that the total classification accuracy percentage of the choiced bands image is 91.7%, Kappa coefficient 0.82.
The Great Wall is the symbol of China, and it is one of the greatest structures during human history. The Great Wall information is analyzed and extracted using high spatial resolution data“SPOT-5”and DEM data. Firstly, light linear object is extracted using differential geometrical method based on“SPOT-5”data, then the ridge is extracted using GIS method based on DEM data, and a buffer about ten meters is built around the ridge. Finally the linear object and the buffer is analyzed using GIS method, and the Great Wall information is extracted. Through precision evaluation, it is indicated that the result is satisfying.
SIFT (Scale Invariant Feature Transform) and its application on remote sensing image registration is discussed in this paper. First, the keypoint-based framework of remote sensing image registration is described; After analyzing robust feature extractor and keypoints matching which are two
key problems in the image registration, a new approach based on SIFT and the distance between generalized tight pair-wise prototypes for remote sensing image registration is proposed; The relation between different matching methods based on SIFT is established by the concept of generalized tight pairwise prototypes. Compared with the related work, our method can get more number of correct point matches and total number of point matches. The experimental results demonstrate the robustness and efficiency of the algorithm.
Speckle noise is an intrinsic property of InSAR interferograms, and InSAR interferograms are sensitive to it. In order to obtain a more accurate interferometric product, a noise filtering step must be performed. According to the statistical property of speckle noise, SAR interferograms filtering using wavelet-domain hidden markov tree (HMT) model is studied. It is employed to filter the real component and the imaginary component of interferogram respectively. The result shows that the new method is powerful to interferogram speckle noise suppression and residues reduction, as well as it can be preserved fine phase details and fringes' structures. The method proposed can improve the accuracy of interferometric products.
With the principles of image fusion methods, this paper focused on finding a proper method for fusing the panchromatic and multi-spectral images of QuickBird, SPOT5 and Landsat ETM+. Brovey,SVR, PCA, Pansharp and Gram-schmidt were used in this paper. The results showed that within these
methods,①the optimum method for QuickBird image was Gram-schmidt transform.②Pansharp transform was the optimum for SPOT5 image.③For ETM+ image, the Gram-schmidt transform was the best method from the view of preserving spectral information of multi-spectral image; Brovey transform presented the best spatial information for band 2 and band 3, SVR transform obtained the better spatial effects than other transforms for band 1, 5, 7, and Pansharp transform was the best method for band 2.
A new image retrieval algorithm based on color histogram information entropy is presented.Because the traditional method has not considered the symmetrical character of entropy, so it is easy to create mistakes. By using a limited hue value on information entropy the new method can solve this
problem well. As to evaluate the accuracy, we used histogram analysis and correlation coefficients, from qualitative and quantitative of two views to make sure accuracy is dependable. The results show that use this method to search for a similar image is possible and have a high accuracy, the correlation coefficients is above 0.95, and in practical application this method also has a good possibility.
The noise existed in Synthetic Aperture Radar (SAR) image weakens the detailed features of region of interest(ROI) such as target and shadow. It also leads to the serious performance reduction of subsequent target detection, classification and recognition. The traditional regularization method could enhance target features in SAR image; however, the high computation complexity limits the real-time application of it. An improved regularization method is introduced, which increases both speed and precision of region feature extraction for SAR image significantly. It is theoretically proved that, by optimizing SAR projection operator, computation complexity could be reduced fromO(M3N3) toO(MN) without ability losing of the region-based feature enhancement. MSTAR SAR image data is employed for algorithm experiment. The result shows that our method can increase target-to-clutter ratio significantly while restraining the noise in ROI, and then extract target and shadow from background clutters in SAR
image more accurately.
Low spatial-resolution is a disadvantage of TM thematic remote sensing (TM6) image, which hampers its widespread application. So this paper put forward a new method based on the genetic algorithms and back-propagation algorithms (GA—BP) to improve the spatial resolution of TM6 image.
This method combines the genetic algorithms and neural network, replacing the BP algorithm with the genetic algorithm. Tests showed that:①GA—BP algorithm can avoid the problems of BP algorithm which indicate calculation procedure is easily caught in local minimal point and the convergence is slow. The advantages of GA—BP are especially of more practical use for mass calculation of remote sensing images.②According to the simulation result, GA—BP algorithm had a distinct advantage over BP algorithm, no matter calculation efficiency or fusion effectiveness.③GA—BP algorithm improves spatial resolution of TM6 image and the gray value can also be reserved, thus can be applied into the practice of improving the
By researching the principles of the mixed pixel unmixing algorithm, this paper proposes a new GA-based method for mixed pixel unmixing and classification problems, which uses the GA evolution models to fit (approximate) the hyperplane of the unmixing results. The validity and feasibility of the algorithm are demonstrated by remote multispectral images. Experimental results show that the approach is able to get almost the same category proportion with the method of Full Constrained Least-squares(correlation coefficients are above 0.99), and has more flexibility than the method of least squares.
Spaceborne radar altimeter is an important active microwave remote sensor which is able to measure the oceans with high accuracy. As altimeter is able to measure the height, SWH and backscattering coefficient, it has been widely used in various aspects of ocean research. Sea surface return signal simulator (RSS) receives and processes the chirp signal transmitted by radar altimeter. It then generates sea surface wave spectrum to modulate the altimeter chirp and retransmits the modulated signal back to the altimeter, after precisely controlled delay of time. RSS is an effective way for altimeter prelaunch test and calibration. RSS is a highly complex system with comprehensive functions, and thus the simulation and analysis have significant meaning for its design, precision analysis and calibration application. This thesis introduces the structure of RSS, analyses the sea echo spectrum generation principle, designs a SIMULINK model for RSS and gives the simulation result.
An algorithm for daytime multilayered cloud detection using MOIDS data is presented. The algorithm is applied to pixel data from MODIS and is primarily based on the scatter plot of the nearinfrared 2.1μm channel reflectance and the 11μm brightness temperatures. Additional information used
by the algorithm includes the MODIS cloud mask from IMAPP and cloud thermodynamic phase inferred from the 8.5μm, 11μm and 12μmbrightness temperatures. The presented algorithm is evaluated by an multilayered cloud detection of tropical storm. The results show that the algorithm is reliably to identify areas containing typical multilayer cloud.
The coastline is the base directrix divides the region of sea and land management. The different seacoast landform coastline division rests on various, therefore, they translate the symbol on the satellite imagery solution with to withdraw the method also to have the difference. This article elaborated has carried on the coastline dynamic monitor using the remote sensing the superiority and the coastline essential feature and the classification, has been clear about in each domain to the coastline different definition, had pointed out the different type seacoast remote sensing solution translated the symbol. In detail elaborated in the different type satellite imagery coastline image formation mechanism, introduced
the use satellite remote sensing image withdraws each kind of coastline the algorithm and the developing process, and withdrew the algorithm to in the remote sensing image coastline the development direction to make the analysis.
