Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods

Journaux, Ludovic; Foucherot, Irène; Gouton, Pierre

doi:10.1117/1.2212108

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…Nonlinear DR techniques have been used by the remote sensing community predominantly for application to single date hyperspectral data, and have been found to provide improved performance for classification, target discrimination and end-member extraction, when compared with linear DR techniques (Bachmann, Ainsworth, & Fusina, 2005;Feilhauer, Faude, & Schmidtlein, 2011;Han & Goodenough, 2005;Zhang, Zhang, Tao, & Huang, 2013). A few studies have applied nonlinear DR techniques to single date multispectral satellite data (Journaux, Foucherot, & Gouton, 2006). However, nonlinear DR techniques have not been applied to multispectral satellite time series.…”

Section: Introductionmentioning

confidence: 99%

Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction

Yan

Roy

2015

Remote Sensing of Environment

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Dimensionality reduction (DR) is a widely used technique to address the curse of dimensionality when highdimensional remotely sensed data, such as multi-temporal or hyperspectral imagery, are analyzed. Nonlinear DR algorithms, also referred to as manifold learning algorithms, have been successfully applied to hyperspectral data and provide improved performance compared with linear DR algorithms. However, DR algorithms cannot handle missing data that are common in multi-temporal imagery. In this paper, the Laplacian Eigenmaps (LE) nonlinear DR algorithm was refined for application to multi-temporal satellite data with large proportions of missing data. Refined LE algorithms were applied to 52-week Landsat time series for three study areas in Texas, Kansas and South Dakota that have different amounts of missing data and land cover complexity. A series of random forest classifications were conducted on the refined LE DR bands using varying proportions of training data provided by the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL); these classification results were compared with conventional metricsbased random forest classifications. Experimental results show that compared with the metrics approach, higher per-class and overall classification accuracies were obtained using the refined LE DR bands of multispectral reflectance time series, and the number of training samples required to achieve a given degree of classification accuracy was also reduced. The approach of applying the refined LE to multispectral reflectance time series is promising in that it is automated and provides dimensionality-reduced data with desirable classification properties. The implications of this research and possibilities for future algorithm development and application are discussed.

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Section: Introductionmentioning

confidence: 99%

Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction

Yan

Roy

2015

Remote Sensing of Environment

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“…Other methods of data reduction could have been used for this purpose, such as independent component analysis or the projection pursuit, among the linear projections, or other nonlinear projection methods such as the curvilinear component analysis [14]. However, PCA is still the mostly used method for reducing the number of spectral bands and has provided good results under the scope of the proposed methodology.…”

Section: Discussionmentioning

confidence: 99%

Automatic Image Registration Through Image Segmentation and SIFT

Gonçalves

Côrte-Real

Gonçalves

2011

IEEE Trans. Geosci. Remote Sensing

200

100

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Automatic image registration (AIR) is still a present challenge for the remote sensing community. Although a wide variety of AIR methods have been proposed in the last few years, there are several drawbacks which avoid their common use in practice. The recently proposed scale invariant feature transform (SIFT) approach has already revealed to be a powerful tool for the obtention of tie points in general image processing tasks, but it has a limited performance when directly applied to remote sensing images. In this paper, a new AIR method is proposed, based on the combination of image segmentation and SIFT, complemented by a robust procedure of outlier removal. This combination allows for an accurate obtention of tie points for a pair of remote sensing images, being a powerful scheme for AIR. Both synthetic and real data have been considered in this work for the evaluation of the proposed methodology, comprising medium and high spatial resolution images, and single-band, multispectral, and hyperspectral images. A set of measures which allow for an objective evaluation of the geometric correction process quality has been used. The proposed methodology allows for a fully automatic registration of pairs of remote sensing images, leading to a subpixel accuracy for the whole considered data set. Furthermore, it is able to account for differences in spectral content, rotation, scale, translation, different viewpoint, and change in illumination. Index Terms-Automatic image registration (AIR), image segmentation, optical images, scale invariant feature transform (SIFT).

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“…The general affine transformation is given by (10) By defining only the B matrix, this transformation can carry out pure translation:…”

Section: Keypoint Matchingmentioning

confidence: 99%

Remote Sensing Image Matching using Sift And Affine Transformation

Kuriakose¹,

Viswan²

2013

IJCA

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This paper presents remote sensing image matching using sift algorithm and affine transformation. The novelty in our approach is to find the features in the reference image and then match the input image with that of reference image using Affine Transformation. Both synthetic and real data have been considered in this work for the evaluation of the proposed methodology. After registering the image, the outliers are removed. A speeded up affine invariant detector is proposed in this paper for local feature extraction. The experimental results show that the proposed algorithm decreases the redundancy of key points and speeds up the implementation. It is able to account for differences in spectral content, rotation, scale, translation, different viewpoint, and change in illumination. The proposed technique improves the computational efficiency and decrease the storage requirement.

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Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods

Cited by 7 publications

References 30 publications

Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction

Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction

Automatic Image Registration Through Image Segmentation and SIFT

Remote Sensing Image Matching using Sift And Affine Transformation

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