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作者： Grant, Cameron S. Utah State University

学位级别：M.S.

Detection of chemical plumes in hyperpsectral data is a problem having solutions that focus on spectral information. These solutions neglect the presence of the spatial information in the scene. The spatial information is exploited in this work by assignment of prior probabilities to neighborhood configurations of signal presence or absence. These probabilities are leveraged in a total probability approach to testing for signal presence in a pixel of interest. The two new algorithms developed are named spatial information detection enhancement (SIDE) and bolt–on SIDE (B–SIDE). The results are explored in comparison to the clutter matched filter (CMF), a standard spectral technique, and to several supervised machine learning techniques. The results show a great improvement of SIDE over these other techniques, in some cases showing the poorest performance of the SIDE filter being much better than the CMF at its best.

关键词： effluent plume detection hyperspectral image processing pattern recognition signal processing remote sensing Electrical and Computer Engineering

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An image segmentation method based on Type-2 fuzzy Gaussian Mixture Models

An image segmentation method based on Type-2 fuzzy Gaussian ...

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International Conference on image Analysis and Signal processing, IASP

作者： Xu Kai Wu Fangfang Qin Kun School of Remote Sensing Information Engineering Wuhan University of China Wuhan China

This paper proposes a new image segmentation method based on Type-2 fuzzy Gaussian Mixture Models (T2 FGMMs). First, the core-region and the open-region of image are extracted according to spatial information of pixels. Then, the GMMs parameters are estimated by EM algorithm. The interval in which T2 FGMMs parameters vary is constrained by the GMMs parameters of the core-region and the open-region of image. Finally, Bayesian decision is used to realize image segmentation. In the end, the method is compared with image segmentation using Otsu's method, FCM and GMM. Experiments demonstrate the effectiveness of this method.

关键词： image segmentation Pixel Uncertainty Parameter estimation Bayesian methods Gaussian distribution Data mining Covariance matrix remote sensing pattern recognition

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Modeling multi-source remote sensing image classifier based on the MDL principle: Experimental studies

Modeling multi-source remote sensing image classifier based ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huaiying Xia Rukun Hu Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.

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Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2010年第4期12卷 261-269页

作者： Kumar, A. Senthil Keerthi, V. Manjunath, A. S. van der Werff, Harald van der Meer, Freek Natl Remote Sensing Ctr Data Proc Area Hyderabad 500625 Andhra Pradesh India Int Inst Geoinformat Sci & Earth Observ ITC NL-7500 AA Enschede Netherlands

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes-variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Hyperspectral image Spectral curve matching Multiresolution analysis Mixed pixel classification

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Transformation-invariant extraction of multi-location image features from remote sensing imagery

Transformation-invariant extraction of multi-location image ...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Roman M. Palenichka Ahmed Lakhssassi Marek B. Zaremba Department of Computer Science and Engineering Université du Quàbec en Outaouais Gatineau QUE Canada

A novel method of transformation-invariant feature extraction called multi-location saliency pattern is proposed in this paper for object recognition and image matching. Multi-location image features are extracted in salient image points, which indicate image locations with high intensity contrast, region homogeneity and shape saliency. Three distinctive types of fragment descriptors are extracted to form the descriptor vector: pose, regional shape, and intensity (texture) descriptors. Pose characteristics and regional shape descriptors are made invariant to image similarity transformations.

关键词： Feature extraction Shape remote sensing image matching Robustness Object recognition Detectors

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A Novel recognition Approach Based on Multi-agent

A Novel Recognition Approach Based on Multi-agent

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International Conference on Digital Manufacturing and Automation (ICDMA)

作者： Yong-mei Zhang Li Ma Qi Wei Yin-gang Yang

Since the agent is autonomous, cooperative and distributed, and it is based on the BDI model of the agent, the paper describes the process of object recognition on the basis of multi-sensor remote sensing images using multi-agent system, proposes a multi-agent object recognition model(MAORM) which combines concurrency research results and the specific characteristics of multi-sensor remote sensing image recognition. In order to improve recognition probability, the task of multi-source remote sensing image recognition for near-infrared, panchromatic and SAR images can be accomplished by the model, and the features that are sensitive to remote sensing classification data are selected through property correlative analysis. Compared with the current object recognition methods, the proposed framework is more close to the human vision. A majority-decision algorithm based on multi-agent is presented. The paper proposes a new approach in decision fusion, the method uses less data than other fusion, and improves the reliability. Experiment results show that the system can effectively identify the bridges, wharfs, ships and so on. Compared with a single remote sensing image, the system can effectively recognize targets with higher recognition accuracy and lower error recognition rate, and achieve the distributed object processing.

关键词： image recognition remote sensing Bridges Feature extraction Target recognition Marine vehicles

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Gerbil-A novel software framework for visualization and analysis in the multispectral domain

Gerbil-A novel software framework for visualization and anal...

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15th International Workshop on Vision, Modeling and Visualization, VMV 2010

作者： Jordan, J. Angelopoulou, E. Pattern Recognition Lab. University of Erlangen-Nuremberg Germany

ISBN: (纸本)9783905673791

Multispectral imaging has been gaining popularity and has been gradually applied to many fields besides remote sensing. Multispectral data provides unique information about material classification and reflectance analysis in general. However, due to the high dimensionality of the data, both human observers as well as computers, have difficulty interpreting this wealth of information. We present a new software package that facilitates the visualization of the relationship between spectral and topological information in a novel fashion. It puts emphasis on the spectral gradient, which is shown to provide enhanced information for many reflectance analysis tasks. It also includes a rich toolbox for evaluation of image segmentation and other algorithms in the multispectral domain. We combine the parallel coordinates visualization technique with hashing for a highly interactive visual connection between spectral distribution, spectral gradient and topology. The framework is released as open-source, has a modern cross-platform design and is well integrated into existing established computer vision software (OpenCV). © The Eurographics Association 2010.

关键词： image segmentation

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Tree crown recognition algorithm on high spatial resolution remote sensing imagery

Tree crown recognition algorithm on high spatial resolution ...

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2010 3rd International Congress on image and Signal processing, CISP 2010

作者： Deng, Guang Li, Zengyuan Wu, Honggan Institute of Forest Resource Information Techniques Chinese Academy of Forestry Beijing China

ISBN: (纸本)9781424465149

To extract information at the individual tree level ,which is very useful in biologyinverted commas ecology and forestry, would be prohibitively time-consuming and be necessary for artificial intelligence by considering many factors. The presented approach develops a tree top seeded based region growth tree detection and crown delineation algorithm for analyzing QuickBird satellite images in Populusxxiaohei plantation even stand at Xue Jia Zhuang wood farm in Shanxi Province of China. After multi resolution segmentation, we get image object segments for tree top seeds detection with NDVI and ratio NIR feature. Around theses seeds, we let them region growing in a cycle way. Some false seeds must be wiped off with given feature threshold. After quad tree segmentation for crown shape optimization, the same category region must be merged. We use 9 plots with different plantation density to validate the above method. Average tree numbers identification error is 18.9% , R2 = 0.4693. From comparing tree numbers of field work and software identification by tree matching, the confusion matrix, overall accuracy, commission error, omission error is computed. ©2010 IEEE.

关键词： Forestry

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Salient region detection in high resolution remote sensing images

Salient region detection in high resolution remote sensing i...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Junge Sun Yunhong Wang Zhaoxiang Zhang Yiding Wang School of Computer Science and Engineering Beihang University China School of Information Engineering North-China University of Technology China

ISBN: (纸本)9781424475971

In this paper, we address the problem of automatic pre-segmentation for object detection and recognition in remote sensing image processing. It plays an important role in reducing computational burden and increasing efficiency for further image processing and analysis. A visual-attention based saliency computation approach is introduced to select the perceptually salient and highly informative regions that represent the main contents of the high resolution remote sensing images. In our method, two bottom-up visual saliency computation methods, edge-based and Graph-based visual saliency (GBVS), are adopted to exploit different kind of features, and the two saliency maps are fused using a 2D Gaussian shaped function for the purpose of improving salient region detection performance. The experimental results demonstrate that our proposed method performs well in ground-truth evaluation and outperforms on the salient target area segmentation task, thus could be introduced for preprocessing of targets object detection and recognition.

关键词： image resolution remote sensing Object detection image edge detection image processing image segmentation Data mining image recognition Frequency Computer science

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Multiparameter Receiver Operating Characteristic Analysis for Signal Detection and Classification

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IEEE SENSORS JOURNAL 2010年第3期10卷 423-442页

作者： Chang, Chein-, I Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Remote Sensing Signal & Image Proc Lab Baltimore MD 21250 USA Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan

Receiver operating characteristic (ROC) analysis is a widely used evaluation tool in signal processing and communications, and medical diagnosis for performance analysis. It utilizes 2-D curves plotted by detection rate (P-D) against false alarm rate (P-F) to assess effectiveness of a detector, sensor/device for detection. However, P-D and P-F are actually dependent parameters resulting from a more crucial but implicit parameter hidden in the ROC curves, threshold tau, which is determined by the cost of implementing a detector or sensor/device, except only the case that when the Bayes theory is used for detection, tau is completely determined by the Bayes cost. This paper extends the traditional ROC analysis for single-signal detection to detection and classification of multiple signals. It also explores relationships among the three parameters, P-D, P-F, and tau, and further develops a new concept of multiparameter ROC analysis, which uses 3-D ROC curves plotted by three parameters, P-D, P-F, and tau, to evaluate effectiveness of detection performance based on interrelationship among P-D, P-F, and tau, rather then only P-D and P-F used by 2-D ROC analysis. As a result of a 3-D ROC curve, three 2-D ROC curves can be also derived: the conventional 2-D ROC curve plotted by P-D versus P-F and two new 2-D ROC curves plotted based on P-D versus and P-F versus tau. In order to demonstrate the utility of 3-D ROC analysis, four applications are considered: hyperspectral target detection, medical diagnosis, chemical/biological agent detection, and biometric recognition.

关键词： Detection rate (DR) false alarm (FA) rate Neyman-Pearson (NP) detection receiver operating characteristic (ROC) signal detection signal classification 3-D ROC

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