The paper presents a novel approach to automate the Change Detection (CD) problem for the specific task of road extraction. Manual approaches to CD fail in terms of the time for releasing updated maps; in the contrary...
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The paper presents a novel approach to automate the Change Detection (CD) problem for the specific task of road extraction. Manual approaches to CD fail in terms of the time for releasing updated maps; in the contrary, automatic approaches, based on machine learning and imageprocessing techniques, allow to update large areas in a short time with an accuracy and precision comparable to those obtained by human operators. This work is focused on the road-graph update starting from aerial, multi-spectral data. Georeferenced, ground data, acquired by a GPS and an inertial sensor, are integrated with aerial data to speed up the change detector. After roads extraction by means of a binary AdaBoost classifier, the old road-graph is updated exploiting a particle filter. In particular this filter results very useful to link (track) parts of roads not extracted by the classifier due to the presence of occlusions (e.g., shadows, trees).
Modern high resolution satellite SAR sensors even allow analysis of building sub-structures like windows and balconies. In the amplitude data man-made objects usually appear either as salient bright lines or points em...
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Modern high resolution satellite SAR sensors even allow analysis of building sub-structures like windows and balconies. In the amplitude data man-made objects usually appear either as salient bright lines or points embedded within dark background. The latter features may coincide also with so-called persistent scatterers (PS), whose phase history is exploited by time series analysis for 3D reconstruction and deformation monitoring. We apply principles of human gestalt perception for grouping urban objects such as entire facades. The analysis takes place both in the amplitude and the phase data. Fusion is possible at different levels, e.g: i) grouping results in one domain may focus search in the other domain, ii) both approaches allow to infer independently the 3D structure by exploiting complementary features (amplitude vs. phase), and iii) grouping as such is useful to introduce model knowledge.
Monitoring water quality using remotesensing technology is current research focus, the main challenge of which is to design an appropriate inversion model of water quality and an effective simulation platform. For th...
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Monitoring water quality using remotesensing technology is current research focus, the main challenge of which is to design an appropriate inversion model of water quality and an effective simulation platform. For the Small Sample Size problem, this paper proposes a water quality inversion method based on SVM. Such method uses ε-SVR whose kernel is RBF to build the inversion model. Besides, we design a water quality monitoring simulation platform (WRS) based on MVC pattern. WRS is developed by MFC, GDAL and LIBSVM to realize the function of graphical interface, image read/write, modeling and inversion. Furthermore, the divide and conquer algorithm is utilized to speed up the huge-volume remotesensingimageprocessing. Finally, we simulate this SVM method on WRS, and the results show the feasibility of our method and the effectiveness of the simulation platform.
In classical image classification approaches, it assumes that there are a number of labeled training data per class. In real applications, labeled data generally are difficult to obtain while unlabeled data are suffic...
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In classical image classification approaches, it assumes that there are a number of labeled training data per class. In real applications, labeled data generally are difficult to obtain while unlabeled data are sufficient and helpful to improve the accuracy of classifier. Bipartition based clustering method is to generate better initial cluster centers and to preselect representative data samples from each cluster region with given area under clustering model. To attack the quantity and quality problems of training samples, we propose a Cluster-based Classification Algorithm (CCA) for remotesensingimages, and different data samples selection methods are evaluated. Using this approach, the confident unlabeled data both cluster centroid and the ones nearest to the centroid are labeled as training data and extracted. SVM can subsequently be trained with the labeled dataset. The conducted experiments by clustering and classification on real remotesensingimages have validated the proposed approach.
remotesensing change detection is a hot issue in recent years. However, most methods originate from statistical patternrecognition. Parameter resolution and time-consuming are main disadvantages of these methods. He...
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remotesensing change detection is a hot issue in recent years. However, most methods originate from statistical patternrecognition. Parameter resolution and time-consuming are main disadvantages of these methods. Hence, in this paper we propose a novel remotesensing change detection method which originates from neural network patternrecognition. The method is based on Growing Hierarchical Self Organization Map (GHSOM). GHSOM has flexible network architecture to adjust remotesensing scene complexity. Theoretically speaking, GHSOM is able to extract change areas well. In experiment, we select three pairs of remotesensingimage. We compare the results with Gaussian Mixture Model result and traditional SOFM result. The experiment shows the proposed method is advantageous in efficiency and detection accuracy. It can be expected that the method will be applied in GIS data updating, land use cover surveying, and natural disaster evaluation.
作者：
Sidorova, V. S.Russian Acad Sci
Inst Computat Math & Math Geophys Siberian Branch Pr Akademika Lavrenteva 6 Novosibirsk 630090 Russia
A histogram-based clustering algorithm is proposed that takes into account features of the collection of image texture statistics. The algorithm allows the addition of the false clusters occurring on the boundaries of...
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A histogram-based clustering algorithm is proposed that takes into account features of the collection of image texture statistics. The algorithm allows the addition of the false clusters occurring on the boundaries of objects with different textures, thus significantly reducing their number. The clusters are analyzed by estimating their separability in the multidimensional vector space of features and the image context. The application of the algorithm to the automated recognition of types of land cover from aerial photographs of forest landscapes is considered. A comparison of cluster maps and schematic map of ground survey shows their good agreement
Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remotesensingimagery. In this paper we present a supervised seman...
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Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remotesensingimagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferation monitoring using high resolution remotesensingimages.
作者：
D. SasikalaR. NeelaveniAssistant Professor
Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Tamilnadu India 638401 Assistant Professor
Department of Electrical and Electronics Engineering PSG College of Technology Peelamedu Coimbatore Tamilnadu India 641014
image registration is of great significance to medicine and remotesensing, so a lot of techniques have been developed within the context of one or the other discipline. This paper proposes an approach for medical ima...
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image registration is of great significance to medicine and remotesensing, so a lot of techniques have been developed within the context of one or the other discipline. This paper proposes an approach for medical image registration using Modified Gabor Wavelet Transform (MGWT) for Modified Adaptive Polar Transform (MAPT). This algorithm can be used to register images of the same or different modalities. This transform analyzes periodic signal components and presents the advantage of being independent of the window length. The performance of the Modified Gabor Wavelet Transform is compared with previous methods like Log Polar Transform and Adaptive Polar Transform. The results show that MGWT outperforms all evaluated model-independent methods with respect to identification accuracy. These results show that the basis of errors produced by the previous methods is the fixed working scale. The new method not only avoids this basis of errors but also makes a tool available for detailed study.
Ocean primary production (OPP) is an important indicator of ocean ecological system. The spatial and temporal pattern of OPP is helpful for global climate change study. remotesensing has the advantage of dynamic and ...
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ISBN:
(纸本)9781424458653
Ocean primary production (OPP) is an important indicator of ocean ecological system. The spatial and temporal pattern of OPP is helpful for global climate change study. remotesensing has the advantage of dynamic and large-scale information collection. Integration of remotesensing and ecological model is promising for OPP study. The aim of our study is to: 1) choose the suitable coefficient algorithms in models for OPP calculation;2) provide satellitederived OPP pattern in China Shelf Sea. We have chosen VGPM (Vertical Generalized Production Model) to estimate OPP for its extensive validation. Some parameters of VGPM were calculated using new algorithms instead. Most of the parameters were estimated using SeaWiFS and MODIS data. The OPP in the China Shelf Sea were estimated using VGPM model. And a simple test was also implemented. The correlation coefficient between in situ OPP and estimated one is over 0.5. OPP variation was also analyzed in China Shelf Sea. The result shows that the OPP model based on the specific chlorophyll algorithm in China sea area can reveal the environment better. This method can provide reference for the large-scale tendency research of OPP in the whole sea area.
imageprocessing on-board satellites is no longer a nice to have feature. Lossless image compression allows increasing the stored data on-board satellite and transmitting it to the ground station without losing qualit...
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imageprocessing on-board satellites is no longer a nice to have feature. Lossless image compression allows increasing the stored data on-board satellite and transmitting it to the ground station without losing quality of the images. An overview of development trend in this area, as well as brief state-of-the-art lossless image compression methods on-board satellites are presented in this paper.
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