This paper presents a new method for urban detection from high-spatial-resolution satellite images. Unlike traditional approaches using only texture information for urban detection, we integrate several complementary image features through multiple Kernel Learning framework, and demonstrate that fusing multiple features can help improving urban detection accuracy rate. Furthermore, since that most of supervised urban classification approaches are mainly based on block-based image interpretation, the resulting urban boundary is very coarse. To handle this, we formulate the urban boundary refinement as a binary labeling problem, and propose a graph cut based approach to solve it. Experimental results show that the proposed approach outperforms the existing algorithm in terms of detection accuracy.

This letter presents a new method for airport detection from large high-spatial-resolution IKONOS images. To this end, we describe airport by a set of scale-invariant feature transform (SIFT) keypoints and detect it using an improved SIFT matching strategy. After obtaining SIFT matched keypoints, to both discard the redundant matched points and locate the possible regions of candidates that contain the target, a novel region-location algorithm is proposed, which exploits the clustering information from matched SIFT keypoints, as well as the region information extracted through the image segmentation. Finally, airport recognition is achieved by applying the prior knowledge to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy.

This brief paper proposes an efficient multi-input/multi-output VLSI architecture (MIMOA) for two-dimensional lifting-based discrete wavelet transform (DWT). The novelty is the simplicity and generality to construct the MIMOA, which is a high-speed architecture with computing time as low as N2/M for an N × N image with controlled increase of hardware cost. M is the throughput rate.

In this letter, we address the problem of modeling scene background for moving object detection. Although the per-pixel model has been extensively exploited in the literature, the pixel-pair relationship is still a nontrivial problem to be represented efficiently. To deal with this issue, we propose a spatiotemporal smooth model based on conditional random field. Besides the mutual influence on labels, data dependencies are encoded into the spatiotemporal smooth model effectively by employing the contextual constraints in terms of both spatial coherence and temporal persistency. Accurate extraction of foreground from nonstationary scenes can be achieved by the proposed model. Experiments conducted on various sequences demonstrate the property of the proposed method.

The panchromatic (Pan) sharpening of multispectral (MS) bands is an important technique in the various applications of satellite remote sensing. This paper presents an MS Pan- sharpening method using the proposed multiscale mapped least-squares support vector machine (LS-SVM). Under the LS-SVM framework, the salient features underlying the image are represented by support values, and the support value transform (SVT) is developed for image information extraction. The low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan image. The support value analysis is implemented by using a series of multiscale support value filters that are deduced from the mapped LS-SVM with multiscale Gaussian radial basis function kernels. Experiments are carried out on very high resolution QuickBird MS + Pan data. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed MS Pan-sharpening method performs comparable to the state-of-the-art in terms of the pertained quantitative quality evaluation indexes, such as the Spectral Angle Mapper, relative dimensionless global error in synthesis (ERGAS), modulation-transfer-function-based tool and quality index (Q4), etc. The SVT is an effective tool for remote sensing image fusion.

Adaptive pixel-wise Gaussian mixture model (GMM) is a popular method to model dynamic scenes viewed by a fixed camera. However, it is not a trivial problem for GMM to capture the accurate mean and variance of a complex pixel. This paper presents a two-layer Gaussian mixture model (TLGMM) of dynamic scenes for moving object detection. The first layer, namely real model, deals with gradually changing pixels specially; the second layer, called on-ready model, focuses on those pixels changing significantly and irregularly. TLGMM can represent dynamic scenes more accurately and effectively. Additionally, a long term and a short term variance are taken into account to alleviate the transparent problems faced by pixel-based methods.

Novel architectures for 1-D and 2-D discrete wavelet transform (DWT) by using lifting schemes are presented in this paper. An embedded decimation technique is exploited to optimize the architecture for 1-D DWT, which is designed to receive an input and generate an output with the low- and high-frequency components of original data being available alternately. Based on this 1-D DWT architecture, an efficient line-based architecture for 2-D DWT is further proposed by employing parallel and pipeline techniques, which is mainly composed of two horizontal filter modules and one vertical filter module, working in parallel and pipeline fashion with 100% hardware utilization. This 2-D architecture is called fast architecture (FA) that can perform J levels of decomposition for N * N image in approximately 2N2(1 - 4(-J))/3 internal clock cycles. Moreover, another efficient generic line-based 2-D architecture is proposed by exploiting the parallelism among four subband transforms in lifting-based 2-D DWT, which can perform J levels of decomposition for N * N image in approximately N2(1 - 4(-J))/3 internal clock cycles; hence, it is called high-speed architecture. The throughput rate of the latter is increased by two times when comparing with the former 2-D architecture, but only less additional hardware cost is added. Compared with the works reported in previous literature, the proposed architectures for 2-D DWT are efficient alternatives in tradeoff among hardware cost, throughput rate, output latency and control complexity, etc.

With the development of numerous imaging sensors, many images can be simultaneously pictured by various sensors. However, there are many scenarios where no one sensor can give the complete picture. Image fusion is an important approach to solve this problem and produces a single image which preserves all relevant information from a set of different sensors. In this paper, we proposed a new image fusion method using the support value transform, which uses the support value to represent the salient features of image. This is based on the fact that, in support vector machines (SVMs), the data with larger support values have a physical meaning in the sense that they reveal relative more importance of the data points for contributing to the SVM model. The mapped least squares SVM (mapped LS-SVM) is used to efficiently compute the support values of image. The support value analysis is developed by using a series of multiscale support value filters, which are obtained by filling zeros in the basic support value filter deduced from the mapped LS-SVM to match the resolution of the desired level. Compared with the widely used image fusion methods, such as the Laplacian pyramid, discrete wavelet transform methods, the proposed method is an undecimated transform-based approach. The fusion experiments are undertaken on multisource images. The results demonstrate that the proposed approach is effective and is superior to the conventional image fusion methods in terms of the pertained quantitative fusion evaluation indexes, such as quality of visual information (Q(AB/F)), the mutual information, etc.

Efficient line-based architectures for two-dimensional discrete wavelet transform (2-D DWT) are presented in this paper. We first present a four-input/four-output architecture for direct 2-D DWT that 1-level decomposition of a N/spl times/N image could be performed in approximately N/sup 2//4 intra-working clock cycles (ccs), where the parallelism among four subbands transforms in lifting-based 2-D DWT is explored. By using this four-input/four-output architecture, we propose a novel pipelined architecture for multilevel 2-D DWT that can perform a complete dyadic decomposition of N/spl times/N image in approximately N/sup 2//4 ccs. Performance analysis and comparison results demonstrate that, the proposed architectures have faster throughput rate and good performance in terms of production of throughput rate and hardware cost, as well as hardware utilization. The proposed pipelined architecture could be an efficient alternative for high-speed and/or low-power applications.

The goal of image fusion is to create new images that are more suitable for human visual perception, machine vision, object detection and target recognition. In order to adequately make use of all kinds of remote sensing image information, a new image fusion method based on local deviation of the wavelet packet transform is proposed. In fusion processing, the fused approximate coefficients are obtained by the weighted average method. For larger local deviation of each decomposed approximate coefficient, we choose a big power gene. The other approximate coefficient chooses a small one. The fused detailed coefficients are obtained by making each coefficient equal to the corresponding input image wavelet packet coefficient that has the greatest local deviation. Both the image information entropy and image clarity are employed to evaluate the performance of the remote sensing fusion algorithm. The experimental results show that the proposed method is more effective than the other methods.

This paper describes a novel version of regression SVM (Support Vector Machines) that is based on the least-squares error. We show that the solution of this optimization problem can be obtained easily once the inverse of a certain matrix is computed. This matrix, however, depends only on the input vectors, but not on the labels. Thus, if many learning problems with the same set of input vectors but different sets of labels have to be solved, it makes sense to compute the inverse of the matrix just once and then use it for computing all subsequent models. The computational complexity to train an regression SVM can be reduced to O (N2), just a matrix multiplication operation, and thus probably faster than known SVM training algorithms that have O (N2) work with loops. We describe applications from image processing, where the input points are usually of the form {(x0+ dx, y0+ dy) : |dx| < m, |dy| < n} and all such set of points can be translated to the same set {(dx, dy) : |dx| < m, |dy| < n} by subtracting (x0, y0) from all the vectors. The experimental results demonstrate that the proposed approach is faster than those processing each learning problem separately.

Wavelet image denoising has been well acknowledged as an important method of denoising in image processing. This paper describers a new method for the suppression of noise in image by fusing the wavelet denoising technique with support vector regression (SVR). Based on the least squares support vector machine (LS-SVM), a new denoising operators used in the wavelet domain are obtained. Simulated noise images are used to evaluate the denoising performance of the proposed algorithm along with the other wavelet-based denoising algorithm. Experimental results show that the proposed denoising method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the prevented edge information in most cases. It also achieves better performance than the median filter.