A new model for automatic normalization of multitemporal satellite images using Artificial Neural Network and mathematical methods

Sadeghi, Vahid; Ebadi, Hamid; Ahmadi, Farshad

doi:10.1016/j.apm.2013.01.006

Cited by 51 publications

(47 citation statements)

References 8 publications

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“…Consider the nonlinear quadratic Riccati differential equation [20][21][22][23]: This is a special form of Eq. (1) with A(t) = 0, B(t) = 1, C(t) = 1 and T = 1.…”

Section: Case Study Imentioning

confidence: 99%

“…Examples include modeling for automatic normalization of multitemporal satellite images [23], solving thin plate bending problems [24], modeling of thermotransport phenomenon in metal alloys [25], prediction of roll force and roll torque in hot strip rolling processes [26], and automatic diagnosis of Hashimoto's disease [27], etc. Neural network models optimized with global and local search methodologies have been broadly used for solving linear and nonlinear differential equations [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An efficient computational intelligence approach for solving fractional order Riccati equations using ANN and SQP

Raja

Manzar

Samar

2015

Applied Mathematical Modelling

110

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a b s t r a c tA new computational intelligence technique is presented for solution of non-linear quadratic Riccati differential equations of fractional order based on artificial neural networks (ANNs) and sequential quadratic programming (SQP). The power of feed forward ANNs in an unsupervised manner is exploited for mathematical modeling of the equation; training of weights is carried out with an efficient constrained optimization technique based on the SQP algorithm. The proposed scheme is evaluated on two initial value problems of the Riccati fractional order equation with integer and non-integer derivatives. Comparison of results with the exact solution, and with reference numerical methods demonstrates the correctness of the proposed methodology. Performance of the proposed scheme is also validated using results of statistical analysis based on a sufficiently large number of independent runs.

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“…Consider the nonlinear quadratic Riccati differential equation [20][21][22][23]: This is a special form of Eq. (1) with A(t) = 0, B(t) = 1, C(t) = 1 and T = 1.…”

Section: Case Study Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficient computational intelligence approach for solving fractional order Riccati equations using ANN and SQP

Raja

Manzar

Samar

2015

Applied Mathematical Modelling

110

View full text Add to dashboard Cite

show abstract

“…The formula Equation (24) indicates that the variable carries information of the ratio of the variation. Since the accumulated contribution of the first k variables (∑ k i=1 ω i ) is called the accumulative contribution ratio, the last few variables in U i − V i contain the unchanged pixels between the image-pair, and we can select them as NIFs.…”

Section: Kcca Transformation and Nifs Extractionmentioning

confidence: 99%

“…However, such a linear radiometric normalization cannot eliminate the nonlinear spectral differences, especially the surface reflectance differences [24]. In fact, beside PIFs, there are another two kinds of features/targets in image sequences.…”

Section: Introductionmentioning

confidence: 99%

kCCA Transformation-Based Radiometric Normalization of Multi-Temporal Satellite Images

Bai

Tang

2018

Remote Sensing

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Radiation normalization is an essential pre-processing step for generating high-quality satellite sequence images. However, most radiometric normalization methods are linear, and they cannot eliminate the regular nonlinear spectral differences. Here we introduce the well-established kernel canonical correlation analysis (kCCA) into radiometric normalization for the first time to overcome this problem, which leads to a new kernel method. It can maximally reduce the image differences among multi-temporal images regardless of the imaging conditions and the reflectivity difference. It also perfectly eliminates the impact of nonlinear changes caused by seasonal variation of natural objects. Comparisons with the multivariate alteration detection (CCA-based) normalization and the histogram matching, on Gaofen-1 (GF-1) data, indicate that the kCCA-based normalization can preserve more similarity and better correlation between an image-pair and effectively avoid the color error propagation. The proposed method not only builds the common scale or reference to make the radiometric consistency among GF-1 image sequences, but also highlights the interesting spectral changes while eliminates less interesting spectral changes. Our method enables the application of GF-1 data for change detection, land-use, land-cover change detection etc.

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“…This normalization method was based on modeling using invariant pixels known as radiometric control set samples (RCSS) from the subject image and the reference image, which is a better approach than using global image statistics [25][26][27]. To select the RCSS, no-change pixels were selected through scattergrams used in no-change (NC) regression, which is the conventional linear regression method [28].…”

Section: Introductionmentioning

confidence: 99%

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Seo

Kim

et al. 2017

Remote Sensing

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Abstract:Efforts have been made to detect both naturally occurring and anthropogenic changes to the Earth's surface by using satellite remote sensing imagery. There is a need to maintain the homogeneity of radiometric and phenological conditions to ensure accuracy in change detection, but images to assess long-term changes in time-series data that satisfy such conditions are difficult to obtain. For this reason, image normalization is essential. In particular, the normalizing compositive conditions require nonlinear modeling, and random forest (RF) techniques can be utilized for this normalization. This study employed Landsat-5 Thematic Mapper satellite images with temporal, radiometric and phenological differences, and obtained Radiometric Control Set Samples by selecting no-change pixels between the subject image and reference image using scattergrams. In the obtained no-change regions, RF regression was modeled, and normalized images were obtained. Next, normalization performance was evaluated by comparing the results against the following conventional linear regression methods: mean-standard deviation regression, simple regression, and no-change regression. The normalization performance of RF regression was much higher. In addition, for an additional usefulness evaluation in normalization, the normalization performance was compared with other nonlinear ensemble regressions, i.e. Adaptive Boosting regression and Stochastic Gradient Boosting regression, which confirmed that the normalization performance of RF regression was significantly higher. In other words, it was found to be highly useful for normalization when compared to other nonlinear ensemble regressions. Finally, as a result of performing change detection, normalized subject images generated by RF regression showed the highest accuracy, which indicated that the proposed method (where the image was normalized using RF regression) may be useful in change detection between multi-temporal image datasets.

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A new model for automatic normalization of multitemporal satellite images using Artificial Neural Network and mathematical methods

Cited by 51 publications

References 8 publications

An efficient computational intelligence approach for solving fractional order Riccati equations using ANN and SQP

An efficient computational intelligence approach for solving fractional order Riccati equations using ANN and SQP

kCCA Transformation-Based Radiometric Normalization of Multi-Temporal Satellite Images

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

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