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

Abstract

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.

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

“…At this time, feature selection is independently performed for each band, since the features affecting each band are different [25]. Furthermore, as the number of input features increases, the complexity of the model and noise increases, and thus it is important to select optimal features [46,47].…”

“…As mentioned above, the changes that are caused by the growth of vegetation are the most typical nonlinear changes in the radiometric characteristics. In other words, a nonlinear relationship is required in order to perform normalization for the radiometric differences as well as the phenological differences [25,26]. In this study, the nonlinear normalization is performed while using an MLP that is a representative algorithm for modeling nonlinear relationships [33,48].…”

“…Furthermore, it increases the overall complexity of the algorithm and the training time [50]. Therefore, after considering the nature of the normalization, with limits of training time, only one hidden layer is considered [21,25]. As for the selection of the number of neurons for the hidden layer, the comparison experiments are conducted by setting the number of neurons to 2, 3, 4, 5, 6, 7, 8, 9, 10, and 15.…”

“…However, most remote sensing data are nonlinearly distributed, and the surface of the earth is composed of a complex mixture of natural and man-made features that exhibit nonlinear characteristics [22][23][24]. Changes that are caused by vegetation in particular have the most typical characteristics, including nonlinearity, which induces serious disturbances when change detection is performed [25,26]. In addition, since optical satellite imagery, which is the main source in change detection, is affected by clouds and atmospheric conditions, it is difficult to acquire images that meet the temporal requirements [27,28].…”

“…In addition, since optical satellite imagery, which is the main source in change detection, is affected by clouds and atmospheric conditions, it is difficult to acquire images that meet the temporal requirements [27,28]. In such cases, images with phenological differences as well as radiometric differences should be utilized [25,26]. In other words, compositive normalizations, including phenological and radiometric properties, should be conducted.This study proposes a nonlinear relative radiometric normalization method while considering the phenological as well as radiometric properties for high-resolution satellite images in order to overcome these limitations.…”