This paper presents a new method based on Semantic Structure Tree (SST) for remotesensingimage segmentation, in which, the semantic image analysis is used to construct the SST of the image. The leaves of the SST rep...
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ISBN:
(纸本)9780819483478
This paper presents a new method based on Semantic Structure Tree (SST) for remotesensingimage segmentation, in which, the semantic image analysis is used to construct the SST of the image. The leaves of the SST represent the semantics of the image and serve as human semantic understanding of the image. The root of the tree is the whole image. The SST uses grammar rules to construct a hierarchy structure of the image and gives a complete high-level semantics contents description of the image. Experimental results show that the tree can give efficient description of the semantic content of the remotesensingimage, and can be well used in remotesensingimage segmentation.
This paper addresses issues related to classification of images in complex spaces. The image is represented in terms of a phase and amplitude components. The classifier optimizes functions of joint real and imaginary ...
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ISBN:
(纸本)078037536X
This paper addresses issues related to classification of images in complex spaces. The image is represented in terms of a phase and amplitude components. The classifier optimizes functions of joint real and imaginary conditional probability density functions. Bound on the total probability of errors in terms of Rayliegh quotient is derived and compared to the cases where non-complex amplitude-only signal is used. Examples of application of the proposed approach on polarimetric radar imagery indicate several orders of magnitude improvement in performance.
As a rigorous mathematical formulation of the correspondence technique given in(1) and inspired from the data fusion methods, a new approach to detect the robust estimated number of clusters is proposed in this paper....
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ISBN:
(纸本)081943826X
As a rigorous mathematical formulation of the correspondence technique given in(1) and inspired from the data fusion methods, a new approach to detect the robust estimated number of clusters is proposed in this paper. The idea is to make a correspondence between clusters of different classification results obtained with different numbers of clusters, which are superior or equal to the number of land-cover classes. To formulate this idea in a rigorous mathematical framework, we consider the classification results as classifiers we want to combine to obtain the more precise classification result. The combination procedure used is inspired from the recent development in artificial intelligence methods of classifiers combination. Since the Bayesian method uses more information on classifiers in the combination of their results;we have adopted this method in the elaboration of our classifiers combination approach. We demonstrate our methodology by classifying real SAR data provided by the SIR-C sensors.
The land use and land cover classification is an important and hot research topic in remotesensingimageprocessing. How to use information effectively in remotesensing data to categorize different land use and land...
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ISBN:
(纸本)9781479983391
The land use and land cover classification is an important and hot research topic in remotesensingimageprocessing. How to use information effectively in remotesensing data to categorize different land use and land cover scenes needs urgent attention. In this paper, we analysis the Bag-of-Word model based feature extracting method systematically, and propose the Saliency Map Cooperated (SMC) coding strategy according characters of remotesensingimages. The proposed SMC takes into account both the primary objects and partial of large scale objects in remotesensingimages, with little effecting on texture dominated images. Extensive experimental results show the efficiency of the proposed SMC coding strategy.
Nowadays, reconstruction of satellite images is one of the important challenges in technology. Sometimes, remotesensingimages have dead pixels or pixel missing gives poor visual quality. It is mainly due to the pres...
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ISBN:
(纸本)9781509047406
Nowadays, reconstruction of satellite images is one of the important challenges in technology. Sometimes, remotesensingimages have dead pixels or pixel missing gives poor visual quality. It is mainly due to the presence of clouds, fogs, or shadows since these images are acquired by sensors at different seasons. The other serious problems in remotesensingimages are instrumentation error, registration error and losses of image data during transmission. To get a good visual quality images, degraded remotesensingimages are processed by using the application of inpainting. The main objectives of this image inpainting approaches are to fill lost parts of images, delete unwanted objects, remove noise in images and to enhance images quality. image completion or image inpainting process is one in which the damaged portions are reconstructed or to fill the lost regions using data collected from surrounding areas in original image. So far, a number of inpainting algorithms are available. This paper gives a detailed survey of some inpainting methods which are suitable for remotely sensed images.
Classifier fusion approaches are receiving increasing attention for their capability of improving classification performances. At present, the usual operation mechanism for classifier fusion is the "combination&q...
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ISBN:
(纸本)081943826X
Classifier fusion approaches are receiving increasing attention for their capability of improving classification performances. At present, the usual operation mechanism for classifier fusion is the "combination" of classifier outputs. improvements in performances are related to the degree of "error diversity" among combined classifiers. Unfortunately, in remote-sensingimage recognition applications, it may be difficult to design an ensemble that exhibit an high degree of error diversity. Recently, some researchers have pointed out the potentialities of "dynamic classifier selection" (DCS) as an alternative operation mechanism. DCS techniques are based on a function that selects the most appropriate classifier for each input pattern. The assumption of uncorrelated errors is not necessary for DCS because an "optimal" classifier selector always selects the most appropriate classifier for each test pattern. The potentialities of DCS have been motivated so far by experimental results on ensemble of classifiers trained using the same feature set, in this paper, we present an approach to multisensor remote-sensingimage classification based on DCS. A selection function is presented aimed at choosing among classifiers created using different feature sets. The experimental results obtained in the classification of remote-sensingimages and comparisons with different combination methods are reported.
today's sensors are like eyes in the sky, thanks to the growth of satellite remotesensing technologies. Therefore, we see a steady evolution of the usage of different types of sensor, from airborne and satellites...
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ISBN:
(纸本)9781538652398
today's sensors are like eyes in the sky, thanks to the growth of satellite remotesensing technologies. Therefore, we see a steady evolution of the usage of different types of sensor, from airborne and satellites platforms which are generating large quantities of remotesensingimage for divers applications such as;smart city, disaster management, military intelligence and others. As a result, the rate of growth in the amount of data by satellite is increasing dramatically. The velocity has exceeded 1TB per day and it will certainly increase in the future. However, it becomes crucial for these huge volume data to be stored. So, how to store and manage it efficiently becomes a real challenge because traditional ways have intensive issues;they are expensive and difficult to extend. Therefore, we need some scalable and parallel models for remotesensing data storage and processing. In this paper, we describe a scalable and distributed architecture for massive remotesensing data storage based on three No SQL databases (Apache Cassandra, Apache HBase, MongoBD). Also, a Hadoop-based framework is proposed to manage the big remotesensing data in a distributed and parallel manner.
This paper has systematically studied data fusion technology based on D-S evidence theory, and analyzed the method to construct BPA of sensor. Detection fusion has been done for several situations based on dual-sensor...
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ISBN:
(纸本)081943826X
This paper has systematically studied data fusion technology based on D-S evidence theory, and analyzed the method to construct BPA of sensor. Detection fusion has been done for several situations based on dual-sensor system, and improvement of data fusion on ROC curves has been simulated. We have processed the small target sequential images created by infrared scene generator and reached the expected results.
Plane is an important target category in remotesensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote se...
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Plane is an important target category in remotesensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remotesensingimage has been very high and we can get more detailed information for detecting the remotesensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remotesensing target detection and proposed an algorithm with end to end deep network, which can learn from the remotesensingimages to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.
One unique feature in the remotesensing problems is that a significant amount of data are available, from which desired information must be extracted. Transform methods offer effective procedures to derive the most s...
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ISBN:
(纸本)081944667X
One unique feature in the remotesensing problems is that a significant amount of data are available, from which desired information must be extracted. Transform methods offer effective procedures to derive the most significant information for further processing or human interpretation and to extract important features for pattern classificaiton. In this paper a survey of the use of major transforms in remotesensing is presented. These transforms have significant effects on pattern recognition as features derived from orthogonal or related transforms tend to be very effective for classification, and on data reduction and compression. After the introduction, we will examine the empirical orthogonal function, the discrete Karhunen-Loeve transform and related transforms, the wavelet transform, and the component analysis.
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