image Registration is the first step towards using remote sensed images for any purpose. Despite numerous techniques being developed for image registration, only a handful has proved to be useful for registration of r...
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ISBN:
(纸本)9783642136801
image Registration is the first step towards using remote sensed images for any purpose. Despite numerous techniques being developed for image registration, only a handful has proved to be useful for registration of remotesensingimages due to their characteristic of being computationally heavy. Recent flux in technology has prompted a legion of approaches that may suit divergent remotesensing applications. This paper presents a comprehensive survey of such literatures including recently developed techniques.
Blur and noise are two common distortion factors which affect remotesensingimage quality. And make it difficult to assess the remotesensingimage quality. The Structure Similarity(SSIM) algorithm is simple, high ef...
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ISBN:
(纸本)9781450364607
Blur and noise are two common distortion factors which affect remotesensingimage quality. And make it difficult to assess the remotesensingimage quality. The Structure Similarity(SSIM) algorithm is simple, high efficient and accurate. However, it does not work well when there is cross distortion in the image. To deal with the problem of SSIM algorithm treating different regions of image identically, this paper considered the perceptual characteristics to different content and masking effect. The proposed method is the perceptual weighting used in the region of interest and based on SSIM algorithm. The experiment shows that, compared with the Peak signal-Noise Rate(PSNR) index, the proposed index has good consistence with the Structure Similarity(SSIM) index, and can make an effective and correct evaluation of image with both noise and blur. This is an accurate and reliable no-reference remotesensingimage quality assessment mothed, which is easy to implement.
remotesensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications. Recent advances in multimodal self-supervised pretraining hav...
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ISBN:
(数字)9781665496209
ISBN:
(纸本)9781665496209
remotesensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications. Recent advances in multimodal self-supervised pretraining have resulted in state-of-the-art methods which surpass vision models trained solely on optical imagery. In the remotesensing field, there is a wealth of overlapping 2D and 3D modalities which can be exploited to supervise representation learning in vision models. In this paper we propose Contrastive Surface-image Pretraining (CSIP) for joint learning using optical RGB and above ground level (AGL) map pairs. We then evaluate these pretrained models on several building segmentation and change detection datasets to show that our method does, in fact, extract features relevant to downstream applications where natural and artificial surface information is relevant.(1)
In this paper, we present a review of some commonly used methods for signal interpolation and/or estimation, from a set of randomly chosen samples. Most of these methods were originally devised for ID signals. First w...
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ISBN:
(纸本)0819455202
In this paper, we present a review of some commonly used methods for signal interpolation and/or estimation, from a set of randomly chosen samples. Most of these methods were originally devised for ID signals. First we extend these methods to 2D and then perform a comparative study. Our experimental results show good interpolation/reconstruction performances of some methods for sampling ratios as small as 5% of the original number of pixels.
Linear feature extraction is an important problem for remotesensingimageprocessing, and it is very difficult to extract those linear features embedded in strong noise or when the SNR (signal to noise) is low like t...
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ISBN:
(纸本)9780769529295
Linear feature extraction is an important problem for remotesensingimageprocessing, and it is very difficult to extract those linear features embedded in strong noise or when the SNR (signal to noise) is low like the complicated environment of remotesensingimage. In this paper an algorithm based on wedgelet decomposition is proposed to extract linear features from remotesensingimage. Firstly, beamlets can be generated by recursive dyadic partitioning, vertex marking and connecting in different scales, and beamlet transform is implemented as one important parameter to generate edge map of linear feature. Secondly, each dyadic square is split into two wedgelet segments, and wedgelet decomposition is implemented as the other important parameter to generate edge map of linear feature. The propose method can detect lines with any orientation, location and length in different scales. Experimental results show that the proposed method can extract linear features accurately from remotesensingimage. It can be suited to remotesensingimageprocessing and in practice it has surprisingly powerful and apparently unprecedented capabilities.
In this study, a supportive method for afforestation planning process of partially forested areas using hyperspectral remotesensingimagery has been proposed. The algorithm has been tested on a scene covering METU ca...
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ISBN:
(纸本)9781628418538
In this study, a supportive method for afforestation planning process of partially forested areas using hyperspectral remotesensingimagery has been proposed. The algorithm has been tested on a scene covering METU campus area that is acquired by high resolution hyperspectral push-broom sensor operating in visible and NIR range of the electromagnetic spectrum. The main contribution of this study to the literature is segmentation of partially forested regions with a semi-supervised classification of specific tree species based on chlorophyll content quantified in hyperspectral scenes. In addition, the proposed method makes use of various hyperspectral imageprocessing algorithms to improve identification accuracy of image regions to be planted.
At present. remotesensingimage vehicle detection based on deep learning has achieved certain results. but most of them rely on powerful PC computing power and cannot be deployed in satellites, so they cannot provide...
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ISBN:
(纸本)9781510642768;9781510642751
At present. remotesensingimage vehicle detection based on deep learning has achieved certain results. but most of them rely on powerful PC computing power and cannot be deployed in satellites, so they cannot provide support for satellite in-orbit detection. Aiming at this problem, this paper proposes a remotesensingimage vehicle detection method based on YOLOv5 model and successfully deploys it in Jetson TX2 embedded equipment that can be deployed on a satellite platform. Experiments have proved that the algorithm proposed in this article detects vehicle targets in a 12000* 12000 pixels wide remotesensingimage in an embedded device, and the detection time is only about 1 minute and 20 seconds at the fastest.
remotesensingimages acquired by modern multichannel sensors are usually corrupted by the mixed noise that contains both signal-independent and signal-dependent components. Information about the characteristics of th...
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ISBN:
(纸本)9781510621626
remotesensingimages acquired by modern multichannel sensors are usually corrupted by the mixed noise that contains both signal-independent and signal-dependent components. Information about the characteristics of this noise is usually unknown a-priori and, to be used in further imageprocessing, it is retrieved from images using special blind methods. One of such methods is considered in the paper and its modification taking into account the high level of inter-channel correlation of remotesensingimages is proposed. The method is based on line fitting into a set of cluster centers determined on basis of the fourth-order statistical moment analysis in overlapping image blocks. The modification consists in simultaneous evaluation of noise parameters for a group of channels (at least, three) of a multichannel image. To obtain the cluster noise variance estimates for a channel image, it is needed to solve a system of linear equations. The system contains the noise variance estimates evaluated in the difference images obtained for all possible pairs of channel images without repetitions. The effectiveness of the proposed modification is confirmed by numerical simulation results for TID2008 database images and by approbation results on AVIRIS hyperspectral images. It is shown that the obtained noise parameter estimation results are in good agreement with the results provided by the best existing methods whereas the operating speed of the proposed modified method is considerably higher in comparison to the analog.
Many image compression techniques have been developed for remotesensingimagery over the last thirty years. What are considered as standard techniques such as the use of principal component analysis, discrete cosine ...
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ISBN:
(纸本)0819429597
Many image compression techniques have been developed for remotesensingimagery over the last thirty years. What are considered as standard techniques such as the use of principal component analysis, discrete cosine transform, predictive coding, etc. have shown their limitations. Wavelet transform techniques have been increasingly used in recent years. In this paper a new and efficient technique is presented that provides a nearly lossless compression of the multichannel remotesensingimagery by combining the use of wavelet decomposition, non-uniform quantization, arithmetic coding, and geometric vector quantizer (GVQ) to achieve the compression task with very minimal loss. The detailed procedures will be illustrated with real remotesensingimages.
This article presents an overview of hyperspectral image analysis and processing techniques based on remotesensing. image analysis methods will be explained in detail. A general framework is presented for working wit...
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