image fusion is an emerging area of research having a number of applications in medical imaging, remotesensing, satellite imaging, target tracking, concealed weapon detection and biometrics. In the present work, we h...
详细信息
This paper addresses the problem of remotesensingimage multi-scale classification by: (i) showing that using multiple scales does improve classification results, but not all scales have the same importance; (ii) sho...
详细信息
ISBN:
(纸本)9781467322164
This paper addresses the problem of remotesensingimage multi-scale classification by: (i) showing that using multiple scales does improve classification results, but not all scales have the same importance; (ii) showing that image descriptors do not offer the same contribution at all scales, as commonly thought, and some of them are very correlated; (iii) introducing a simple approach to automatically select segmentation scales, descriptors, and classifiers based on correlation and accuracy analysis.
In this paper, we propose a new method for remotesensingimage pan-sharpening which is based on weighted red-black (WRB) wavelet and adaptive principal component analysis (PCA), where the adaptive PCA is used to redu...
详细信息
ISBN:
(纸本)9781467322164
In this paper, we propose a new method for remotesensingimage pan-sharpening which is based on weighted red-black (WRB) wavelet and adaptive principal component analysis (PCA), where the adaptive PCA is used to reduce spectral distortions and the utilization of WRB wavelet is used to extract the spatial details in PAN images. To reduce the artifacts and spectral distortions in the pan-sharpened images, which were caused by the local instabilities and dissimilarities in the PAN and MS images, a local process strategy incorporating detail enhancement is introduced. The proposed method is tested on two datasets both acquired by QuickBird and compared with the existing methods. Experimental results show that our method can provide promising fused MS images at a high spatial resolution.
In order to restrain pseudo-Gibbs phenomenon, low contrast and blurred phenomenon in the process of image enhancement, a new method based on the Nonsubsampled Contourlet Transform (NSCT) and Grayscale Morphology is pr...
详细信息
In order to restrain pseudo-Gibbs phenomenon, low contrast and blurred phenomenon in the process of image enhancement, a new method based on the Nonsubsampled Contourlet Transform (NSCT) and Grayscale Morphology is proposed in this paper. The proposed method utilizes the shift-invariance of NSCT to restrain the pseudo-Gibbs phenomenon. The results obtained with the proposed method are superior to histogram equalization and contourlet method in detail and vision of the image.
The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, i.e. assigning image observations to the appropriate target; (...
详细信息
The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, i.e. assigning image observations to the appropriate target; (ii) the naturally continuous problem of trajectory estimation, i.e. recovering the trajectories of all targets. To go beyond simple greedy solutions for data association, recent approaches often perform multi-target tracking using discrete optimization. This has the disadvantage that trajectories need to be pre-computed or represented discretely, thus limiting accuracy. In this paper we instead formulate multi-target tracking as a discrete-continuous optimization problem that handles each aspect in its natural domain and allows leveraging powerful methods for multi-model fitting. Data association is performed using discrete optimization with label costs, yielding near optimality. Trajectory estimation is posed as a continuous fitting problem with a simple closed-form solution, which is used in turn to update the label costs. We demonstrate the accuracy and robustness of our approach with state-of-the-art performance on several standard datasets.
This paper presents a paddy growth stages classification using MODIS remotesensingimages with support vector machines (SVMs). We collected the paddy growth stages data samples from a series of MODIS mages acquired f...
详细信息
This paper presents a paddy growth stages classification using MODIS remotesensingimages with support vector machines (SVMs). We collected the paddy growth stages data samples from a series of MODIS mages acquired from March to July 2012 along paddy field area only. The data are collected based on growth stages phenology of paddy using spectral profile which consists of at least 9 classes for growth stages and 2 classes for dominated soil and cloud. We apply SVMs to build a binary classifier for each class with one against all strategy of multiclass approach. One important issue needed to address is unbalanced prior probability that should be solved by each SVM. In this study, we evaluate the effectiveness of balanced branches strategy that is applied to one against all SVMs learning. Our results shows that the balanced branches strategy does improves in average around 10% classification accuracy during training and validation, and in average around 50% during testing.
By considering the advantage of Nonsubsampled Contourlet Transform(NSCT),while Commonly used NeighShrink algorithm uses a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. ...
详细信息
By considering the advantage of Nonsubsampled Contourlet Transform(NSCT),while Commonly used NeighShrink algorithm uses a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper,A novel image denoising algorithm based on an improved method, which can determine an optimal threshold and neighbouring window size for every NSCT subband by the Stein's unbiased risk estimate (SURE).The proposed method can effectively reduce Gaussian noise in remotesensingimage and improve the image of the peak signal-to-noise ratio,This method utilizes the redundant and translation invariant of NSCT transform to inhibit the effect of pseudo Gibbs, and preserves the image texture and edge detail informations, thus obviously ameliorate the visual effect of the image.
image classification of remotesensing data is an important topic and long-term tasks in applications [1]. Markov random field(MRF) has more advantages in processing contextual information [2]. Bayesian approach enabl...
详细信息
image classification of remotesensing data is an important topic and long-term tasks in applications [1]. Markov random field(MRF) has more advantages in processing contextual information [2]. Bayesian approach enables the incorporation of prior model and likelihood distribution, this paper has formulated a Bayesian-MRF classification model based on MAP-ICM framework. It uses Potts model in label field and assume Gaussian distribution in observation field. According to maximum a posteriori(MAP) criterion, each new classified label can be obtained by the minimum of energy using Iterated Conditional Modes(ICM) algorithm. Finally, classification tasks are carried out by Bayesian-MRF classification model. Experimental results show that:(1) Clique potential parameters affect classification greatly. When it is 0.5, the classification accuracy reaches maximum with the best classification result for study area of Dali Erhai Lake basin using landsat TM data.(2) Bayesian MRF model have obvious advantages in classification for neighbourhood pixels so that it can separate Shadow class from Water class because the Shadow in mountain areas is very similar to Water in spectrum. In this case study, the best classification accuracy reaches 95.8%. The approaches and results will have important reference value for applications such as land use/cover classification, environment/ecological monitoring etc.
TEODORO, A., PAIS-BARBOSA, J., GONCALVES, H., VELOSO-GOMES, F and TAVEIRA-PINTO, F., 2011. Beach Hydromorphological Analysis Through remotesensing. In: Micallef, A. (ed.), MCRR3-2010 conference Proceedings, Journal o...
详细信息
TEODORO, A., PAIS-BARBOSA, J., GONCALVES, H., VELOSO-GOMES, F and TAVEIRA-PINTO, F., 2011. Beach Hydromorphological Analysis Through remotesensing. In: Micallef, A. (ed.), MCRR3-2010 conference Proceedings, Journal of Coastal Research, Special Issue, No. 61, pp. 44-51. Grosseto, Tuscany, Italy, ISSN 0749-0208. Beach hydromorphological classification is a complex subject. Different beach classification models were presented by several authors. However, fundamental parameters are usually unavailable. Therefore, a morphological analysis using remotely sensed data and imageprocessing techniques is a good approach to identify and to classify beach hydromorphologies. remotesensing data is an increasingly important component of natural resources monitoring programs. Its usefulness can be maximized by understanding the constraints and capabilities of the imagery and change detection techniques, related to the monitoring objectives. The aim of this study was to explore different remotely sensed data (aerial photographs and a satellite image) and different imageprocessing algorithms in order to identify coastal forms/patterns and further classify beach hydromorphological stage. To achieve that, different imageprocessing techniques were applied to remotely sensed data: pixel and object-based classification algorithms and a patternrecognition approach using artificial neural networks. A stretch of the northwest coast of Portugal was chosen as the study area. The data used in this study consisted in aerial photographs and an IKONOS-2 image. Based on the obtained results two main conclusions could be taken: the pixel-based classification (supervised classification algorithms) showed better results than the object-based classification algorithms;and the patternrecognition approach is the most effective and accurate methodology. Therefore, the association of remotesensing data and imageprocessing techniques is very useful in identifying coastal forms/patterns regarding the
It is well established that Optical remotesensing data from Sun-synchronous satellites of a rugged terrain always suffer from topographic effects. The result of this, people frequently perceive valleys as ridges and ...
详细信息
ISBN:
(纸本)9783642271823
It is well established that Optical remotesensing data from Sun-synchronous satellites of a rugged terrain always suffer from topographic effects. The result of this, people frequently perceive valleys as ridges and vice versa and hence sometimes incorrect image interpretations. The appearance of inverse topography has been termed as False Topographic Perception Phenomena (FTPP) by Dr A.K Saraf et al. (1996). For getting the correct image, the process of SRM, 180 degrees rotation or image negative is applied. But, it will be easy to us for studying the satellite image, if we can get the corrected image directly from the satellite. For this we have to apply the Fourier Transform in the satellite image system. It is observed that it would be much to us advantage if we could rotate an image in Fourier space instead of having to rotate the image in real space. At first, take the real part or logarithm of the Fourier transform of the image function for getting the magnitude of the image and then take the imaginary part of Fourier transform of the image function for getting the phase of the image. After getting the phase of the image, take the inverse function of the same and then only we will get the original satellite image. It will be possible to get everything all together when we will compile these applications together in a computer programing by the representation of software. Here, we are only going to give a brief idea about the application of Fourier transform to get the original image from satellite.
暂无评论