Pan-sharpening of multi-spectral images using a new variational model

In remote-sensing image processing, pan-sharpening is used to obtain a high-resolution multi-spectral image by combining a low-resolution multi-spectral image with a corresponding high-resolution panchromatic image. In this article, to preserve the geometry, spectrum, and correlation information of the original images, three hypotheses are presented, i.e. (1) the geometry information contained in the pan-sharpened image should also be contained in the panchromatic bands; (2) the upsampled multi-spectral image can be seen as a blurred form of the fused image with an unknown kernel; and (3) the fused bands should keep the correlation between each band of the upsampled multi-spectral image. A variational energy functional is then built based on the assumptions, of which the minimizer is the target fused image. The existence of a minimizer of the proposed energy is further analysed, and the numerical scheme based on the split Bregman framework is presented. To verify the validity, the new proposed method is compared with several state-of-the-art techniques using QuickBird data in subjective, objective, and efficiency aspects. The results show that the proposed approach performs better than some compared methods according to the performance metrics.     

Mapping multi-spectral remote sensing images using rule extraction approach

To improve the accurate rate of mapping multi-spectral remote sensing images, in this paper we construct a class of HyperRectangular Composite Neural Networks (HRCNNs), integrating the paradigms of neural networks with the rule-based approach. The supervised decision-directed learning (SDDL) algorithm is also adopted to construct a two-layer network in a sequential manner by adding hidden nodes as needed. Thus, the classification knowledge embedded in the numerical weights of trained HRCNNs can be extracted and represented in the form of If-Then rules. The rules facilitate justification on the responses to increase accuracy of the classification. A sample of remote sensing image containing forest land, river, dam area, and built-up land is used to examine the proposed approach. The accurate recognition rate reaching over 99% demonstrates that the proposed approach is capable of dealing with image mapping.     

Repair approach for DMC images based on hierarchical location using edge curve

The color composite digital mapping camera (DMC) images are produced by the post-processing software of Z/I imaging. But the failure of radiometric correction in post-processing leads to residual radiometric differences between CCD images, which then affect the quality of the images in further applications. This paper, via analyzing the characters and causes of such a phenomenon, proposes a repair approach based on hierarchical location using edge curve. The approach employs a hierarchical strategy to locate the transition area and seam-line automatically and then repair the image through the global reconstruction between CCD images and the local reconstruction in the transition area. Experiments indicate that the approach proposed by this paper is feasible and can improve the quality of images effectively. Supported by the National Basic Research Program of China (Grant No. 2006CB701302) and the Youth Fundation Plan of Wuhan (Grant No. 200750731253)     

A method for multi-spectral image segmentation evaluation based on synthetic images

A general framework for testing the quality of the segmentation of a multi-spectral satellite image is proposed. The method is based on the production of synthetic images with the spectral characteristics of the image pixels extracted from a signature multi-spectral image. The knowledge of the location of objects in the synthetic image provides a reference segmentation, which allows for a quantitative evaluation of the quality provided by a segmentation algorithm. The Hammoude metric and three external similarity indices (Rand, Corrected Rand, and Jaccard) were chosen to perform this evaluation, but other metrics can also be used. The proposed methodology can be used for any type of satellite image (or multi-spectral image), set of land cover types, and segmentation algorithms.A practical application was carried out to illustrate the value of the proposed method. A SPOT satellite image was used to extract the spectral signature of 8 land cover types. Three test images were produced using the 8 land cover classes and two different 5 class sub-sets. The segmentation results provided by a standard algorithm were compared with the reference or expected segmentation. The results clearly indicate that the quality of a segmentation obtained from a multi-spectral image not only depends on the geometric properties of the objects present in the image, but also on their spectral characteristics. The results suggest that a specific evaluation should be carried out for each particular experiment, as the segmentation results are very dependent on the choice of land cover types.     

Discovering hierarchical object models from captioned images

We address the problem of automatically learning the recurring associations between the visual structures in images and the words in their associated captions, yielding a set of named object models that can be used for subsequent image annotation. In previous work, we used language to drive the perceptual grouping of local features into configurations that capture small parts (patches) of an object. However, model scope was poor, leading to poor object localization during detection (annotation), and ambiguity was high when part detections were weak. We extend and significantly revise our previous framework by using language to drive the perceptual grouping of parts, each a configuration in the previous framework, into hierarchical configurations that offer greater spatial extent and flexibility. The resulting hierarchical multipart models remain scale, translation and rotation invariant, but are more reliable detectors and provide better localization. Moreover, unlike typical frameworks for learning object models, our approach requires no bounding boxes around the objects to be learned, can handle heavily cluttered training scenes, and is robust in the face of noisy captions, i.e., where objects in an image may not be named in the caption, and objects named in the caption may not appear in the image. We demonstrate improved precision and recall in annotation over the non-hierarchical technique and also show extended spatial coverage of detected objects.     

Comparing images using joint histograms

Color histograms are widely used for content-based image retrieval due to their efficiency and robustness. However, a color histogram only records an image's overall color composition, so images with very different appearances can have similar color histograms. This problem is especially critical in large image databases, where many images have similar color histograms. In this paper, we propose an alternative to color histograms called a joint histogram, which incorporates additional information without sacrificing the robustness of color histograms. We create a joint histogram by selecting a set of local pixel features and constructing a multidimensional histogram. Each entry in a joint histogram contains the number of pixels in the image that are described by a particular combination of feature values. We describe a number of different joint histograms, and evaluate their performance for image retrieval on a database with over 210,000 images. On our benchmarks, joint histograms outperform color histograms by an order of magnitude.     

Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images

The fuzzy ARTMAP has been applied to the supervised classification of multi-spectral remotely-sensed images. This method is found to be more efficient, in terms of classification accuracy, compared to the conventional maximum likelihood classifier and also multi-layer perceptron with back propagation learning. The results have been discussed.     

基于A* OMP算法的压缩感知声纳成像

传统声纳成像系统所要采集的数据量巨大,给硬件设备以及数据的存储和传输带来很大的压力。压缩感知作为一种全新的采样理论,可以从很少的采样数据中以很大的概率重建原始信号。将压缩感知用于声纳成像,减少数据采集传输量。考虑到水下环境的复杂性,提出了A* OMP作为声纳成像算法,该算法使用A*搜索方法寻找最优原子,得到全局最优路径。实验结果表明,相比于传统OMP算法,所提算法有效地提高了声纳成像的质量。     

DYANA: Best-first hierarchical diagnosis based on differential equation models

In this paper we evaluate and extend existing methods for an intended application for monitoring and diagnosis of a combined heat-power system. Only a few methods in model based diagnosis take advantage of the available design model by using (semi-)qualitative abstractions of ordinary differential equations, e.g. by Dvorak and Kuipers (1989), Ng (1990) and Lackinger and Nejdl (1991). Diagnosis of complex devices benefits from hierarchical models, both from a modelling perspective and from a computational perspective. This leads us to prefer the hierarchical diagnosis methoddiamon (Lackinger and Nejdl, 1991). We propose an extension of diamon, calleddyana, that is suitable for on-line diagnosis and uses an alternative method for dynamic model zooming which is based on heuristics regarding parsimony of diagnoses and consistency of fault models.dyana also uses numerical simulation, based on differential equations, instead of qualitative simulation.This article is dedicated to the memory of Martin Reinders. During the preparation of this paper, Martin died in an air accident. Please send any correspondence concerning this article to Hans Akkermans, at the above address (E-mail: akkermans@ecn.nl or akkerman@cs.utwente.nl).     

Unsupervised multi-class segmentation of SAR images using triplet Markov fields models based on edge penalty

Non-Gaussian triplet Markov random fields (TMF) model is suitable for dealing with multi-class segmentation of nonstationary and non-Gaussian synthetic aperture radar (SAR) images. However, the segmentation of SAR images utilizing this model still fails to resolve the misclassifications due to the inaccuracy of edge location. In this paper, we propose a new unsupervised multi-class segmentation algorithm by fusing the traditional energy function of TMF model with the principle of edge penalty. Through the introduction of the penalty function based on local edge strength information, the new energy function could prevent segment from smoothing across boundaries. Then we optimize the objective function that stems from the new energy function to obtain an iterative multi-region merging Bayesian maximum posterior mode (MPM) segmentation equation for the new segmentation algorithm. The effectiveness of the proposed algorithm is demonstrated by application to simulated data and real SAR images.     

Multipass hierarchical stereo matching for generation of digital terrain models from aerial images

This paper presents a new multi-pass hierarchical stereo-matching approach for generation of digital terrain models (DTMs) from two overlapping aerial images. Our method consists of multiple passes which compute stereo matches with a coarse-to-fine and sparse-to-dense paradigm. An image pyramid is generated and used in the hierarchical stereo matching. Within each pass, the DTM is refined by using the image pyramid from the coarse to the fine level. At the coarsest level of the first pass, a global stereo-matching technique, the intra-/inter-scanline matching method, is used to generate a good initial DTM for the subsequent stereo matching. Thereafter, hierarchical block matching is applied to image locations where features are detected to refine the DTM incrementally. In the first pass, only the feature points near salient edge segments are considered in block matching. In the second pass, all the feature points are considered, and the DTM obtained from the first pass is used as the initial condition for local searching. For the passes after the second pass, 3D interactive manual editing can be incorporated into the automatic DTM refinement process whenever necessary. Experimental results have shown that our method can successfully provide accurate DTM from aerial images. The success of our approach and system has also been demonstrated with a flight simulation software. Received: 4 November 1996 / Accepted: 20 October 1997     

Vector quantization of images using modified adaptive resonancealgorithm for hierarchical clustering

A modified adaptive resonance theory (ART2) learning algorithm, which we employ in this paper, belongs to the family of NN algorithms whose main goal is the discovery of input data clusters, without considering their actual size. This feature makes the modified ART2 algorithm very convenient for image compression tasks, particularly when dealing with images with large background areas containing few details. Moreover, due to the ability to produce hierarchical quantization (clustering), the modified ART2 algorithm is proved to significantly reduce the computation time required for coding, and therefore enhance the overall compression process. Examples of the results obtained are presented, suggesting the benefits of using this algorithm for the purpose of VQ, i.e., image compression, over the other NN learning algorithms.     

Hierarchical fusion of multi-spectral face images for improved recognition performance

This paper presents a two level hierarchical fusion of face images captured under visible and infrared light spectrum to improve the performance of face recognition. At image level fusion, two face images from different spectrums are fused using DWT based fusion algorithm. At feature level fusion, the amplitude and phase features are extracted from the fused image using 2D log polar Gabor wavelet. An adaptive SVM learning algorithm intelligently selects either the amplitude or phase features to generate a fused feature set for improved face recognition. The recognition performance is observed under the worst case scenario of using single training images. Experimental results on Equinox face database show that the combination of visible light and short-wave IR spectrum face images yielded the best recognition performance with an equal error rate of 2.86%. The proposed image-feature fusion algorithm also performed better than existing fusion algorithms.     

Hardware implementations of software programs based on hierarchical finite state machine models

Advances in microelectronic devices have dissolved the boundary between software and hardware. Faster hardware circuits that enable significantly greater parallelism to be achieved have encouraged recent research efforts into high-performance computation in electronic systems without the direct use of processing cores. Standard multi-core processors undoubtedly introduce a number of constraints, such as pre-defined operand sizes and instruction sets, and limits on concurrency and parallelism. This paper suggests a way to convert methods and functions that are defined in a general-purpose programming language into hardware implementations. Thus, conventional programming techniques such as function hierarchy, recursion, passing arguments and returning values can be entirely implemented in hardware modules that execute within a hierarchical finite state machine with extended capabilities. The resulting circuits have been found to be faster than their software alternatives and this conclusion is confirmed by numerous experiments in a variety of application areas.     

Toward semantic indexing and retrieval using hierarchical audio models

Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models the statistical characteristics of audio events over a time series to accomplish semantic context detection. Two stages, audio event and semantic context modeling, are devised to bridge the semantic gap between physical audio features and semantic concepts. In this work, hidden Markov models (HMMs) are used to model four representative audio events, i.e., gunshot, explosion, engine, and car-braking, in action movies. At the semantic-context level, Gaussian mixture models (GMMs) and ergodic HMMs are investigated to fuse the characteristics and correlations between various audio events. They provide cues for detecting gunplay and car-chasing scenes, two semantic contexts we focus on in this work. The promising experimental results demonstrate the effectiveness of the proposed approach and exhibit that the proposed framework provides a foundation in semantic indexing and retrieval. Moreover, the two fusion schemes are compared, and the relations between audio event and semantic context are studied.     

A genetic algorithm for MRF-based segmentation of multi-spectral textured images

A segmentation approach based on a Markov random field (MRF) model is an iterative algorithm; it needs many iteration steps to approximate a near optimal solution or gets a non-suitable solution with a few iteration steps. In this paper, we use a genetic algorithm (GA) to improve an unsupervised MRF-based segmentation approach for multi-spectral textured images. The proposed hybrid approach has the advantage that combines the fast convergence of the MRF-based iterative algorithm and the powerful global exploration of the GA. In experiments, synthesized color textured images and multi-spectral remote-sensing images were processed by the proposed approach to evaluate the segmentation performance. The experimental results reveal that the proposed approach really improves the MRF-based segmentation for the multi-spectral textured images.     

Modeling textured images using generalized long correlation models

The long correlation (LC) models are a general class of random field (RF) models which are able to model correlations, extending over large image regions with few model parameters. The LC models have seen limited use, due to lack of an effective method for estimating the model parameters. In this work, we develop an estimation scheme for a very general form of this model and demonstrate its applicability to texture modeling applications. The relationship of the generalized LC models to other classes of RF models, namely the simultaneous autoregressive (SAR) and Markov random field (MRF) models, is shown. While it is known that the SAR model is a special case of the LC model, we show that the MRF model is also encompassed by this model. Consequently, the LC model may be considered as a generalization of the SAR and MRF models