Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest

Abstract

ABSTRACT:Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

An Airport Knowledge-Based Method for Accurate Change Analysis of Airport Runways in VHR Remote Sensing Images

“…In Images 2, 3, and 6, the quality of runway extraction is above 90%, while the correctness of the proposed method for Images 1, 4, and 5 is not as (e) the results of runway reduction using our proposed runway change analysis method; (f) the results of runway increase using our proposed runway change analysis method; (g) change detection results based on [23]; (h) change detection results based on OCVA; (i) change detection results based on [24]; and (j) ground truths of runway changes. (d) the results of new-temporal test image using our proposed runway extraction method; (e) the results of runway reduction using our proposed runway change analysis method; (f) the results of runway increase using our proposed runway change analysis method; (g) change detection results based on [23]; (h) change detection results based on OCVA; (i) change detection results based on [24]; and (j) ground truths of runway changes. (e) the results of runway reduction using our proposed runway change analysis method; (f) the results of runway increase using our proposed runway change analysis method; (g) change detection results based on [23]; (h) change detection results based on OCVA; (i) change detection results based on [24]; and (j) ground truths of runway changes.…”

“…(d) the results of new-temporal test image using our proposed runway extraction method; (e) the results of runway reduction using our proposed runway change analysis method; (f) the results of runway increase using our proposed runway change analysis method; (g) change detection results based on [23]; (h) change detection results based on OCVA; (i) change detection results based on [24]; and (j) ground truths of runway changes. (e) the results of runway reduction using our proposed runway change analysis method; (f) the results of runway increase using our proposed runway change analysis method; (g) change detection results based on [23]; (h) change detection results based on OCVA; (i) change detection results based on [24]; and (j) ground truths of runway changes.…”

“…The results of the proposed runway change analysis method for Images 7 and 8 are illustrated in Figures 11 and 12. To validate the effectiveness of our method, comparison results obtained by change detection method based on level set [23], object-based change vector analysis (OCVA), visual saliency and random forest [24], and ground truth data of runway changes obtained by manual vectorization are also presented in Figures 11 and 12. The quantitative results of the proposed method and state-of-the-art methods are, respectively, given in Tables 6 and 7.…”

“…Figure 11. Test Data 7 (Osan Air Base, Korea): (a) original image taken in 2016; (b) original image taken in 2019; (c) the results of old-temporal test image using our proposed runway extraction method; (d) the results of new-temporal test image using our proposed runway extraction method;(e) the results of runway reduction using our proposed runway change analysis method; (f) the results of runway increase using our proposed runway change analysis method; (g) change detection results based on[23]; (h) change detection results based on OCVA; (i) change detection results based on[24]; and (j) ground truths of runway changes.…”