Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Abramov, Sergey; Krivenko, Sergey; Kurekin, Andrey A.; Pogrebnyak, Oleksiy

doi:10.1016/j.eswa.2013.05.061

Cited by 30 publications

(26 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…First, RS images are still often subject to visual inspection and analysis (interpreting). Second, efficiency of automatic classification characterized by, e.g., probability of correct classification is strictly connected with edge/detail preservation ability of filters where this property is characterized well by visual quality metrics [2]. This is especially true for classes associated with textural and small sized objects as roads, urban areas, etc.…”

Section: Metricsmentioning

confidence: 99%

See 1 more Smart Citation

A neural network based predictor of filtering efficiency for image enhancement

Rubel

Naumenko

2014

2014 IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS)

Self Cite

View full text Add to dashboard Cite

Image filtering is widely used in remote sensing applications to improve obj ect visibility or for other purposes.However, filtering does not always occur efficient enough and serving image enhancement purposes well. Thus, it is reasonable to have a simple but rather accurate predictor of filtering efficiency. Such a predictor can be based on statistics of DCT coefficients in image blocks. For improved prediction, we propose to apply several local statistics aggregated by a trained neural network. This way allows providing high accuracy prediction of image enhancement not only in terms of standard quality criteria but also in terms of metrics of image visual quality.

show abstract

Section: Metricsmentioning

confidence: 99%

“…There are numerous filters developed so far. However, no one researcher or user can be confident that filtering will improve RS data in the sense of RS data increased usefulness for solving classification tasks [2] or better visual quality if data are subject to interpreting by humans [3].…”

Section: Introductionmentioning

confidence: 99%

A neural network based predictor of filtering efficiency for image enhancement

Rubel

Naumenko

2014

2014 IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, there exists the following problem in multichannel sensing-images in one or a few components are corrupted by noise [4,6,7] (actually, noise is present in all images, but its influence in some components is negligible, as will be shown later). If a noise is intensive (input peak signal-to-noise ratio (PSNR) is low), it is worth applying pre-filtering in order to enhance RS data and to improve the performance of the next RS data processing, such as classification, segmentation, parameter estimation, and so on [4,8].…”

Section: Introductionmentioning

confidence: 99%

“…They can be classified into component-wise, vectorial (three-dimensional, 3D), and hybrid. Component-wise denoising is the simplest among them, allowing parallel processing of component images [7][8][9][10][11]. However, similar to and exploit some information from the reference (for example, about positions of edges [22]) or to incorporate reference image(s) into processing directly.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Component Images of Multispectral Data by Denoising with Reference

et al. 2019

Self Cite

View full text Add to dashboard Cite

Multispectral remote sensing data may contain component images that are heavily corrupted by noise and the pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images—component images having relatively high quality and that are similar to the image subject to pre-filtering. Here, we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. To provide high efficiency of denoising, reference images have to be transformed using linear or nonlinear transformations. This paper proposes a practical approach to doing this. Examples of denoising tests and real-life images demonstrate high efficiency of the proposed approach.

show abstract

“…Meanwhile, there are also several papers where the authors have already done several steps in this direction. 16,[19][20][21][22][24][25][26] Obviously, to take noise statistics into account, it is necessary to have it at disposal or to estimate the statistics with appropriate accuracy. Such estimation has to be performed either from calibration data (if available) or from obtained RS data.…”

Section: Introductionmentioning

confidence: 99%

Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Zemliachenko

Kozhemiakin

Uss

et al. 2014

J. Appl. Remote Sens

View full text Add to dashboard Cite

Abstract. A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and "difference" images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15-20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.

show abstract

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Cited by 30 publications

References 31 publications

A neural network based predictor of filtering efficiency for image enhancement

A neural network based predictor of filtering efficiency for image enhancement

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Contact Info

Product

Resources

About