Objective

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The resolution of a remote sensing image increases with the rapid development of remote sensing technology. A high-resolution image has high spatial and temporal resolutions

providing conditions for real-time monitoring and change detection. Change detection in remote sensing is used to detect changes in the same regions at different periods. The purpose of this study is to find a change in the same region between specific periods. However

with the improvement in the spatial resolution of remote sensing images

the difference in the spatial texture information of the same object increases. The features of remote sensing images are becoming increasingly complex and diverse. Traditional change detection methods experience difficulty in obtaining good results from high-resolution remote sensing images. Unsupervised deep learning algorithms suffer from difficulty in extracting valid features and making them equal in areas wherein different objects have the same spectra and similar objects have different spectra. To improve the change detection accuracy of high-resolution remote sensing images

particularly for effective judgment in areas with considerable texture differences in different periods at the same location

this study proposes a deep learning and superpixel segmentation method for high-resolution remote sensing image change detection.

Method

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The algorithm of the deep learning and superpixel-based method for high-resolution remote sensing image change detection exhibits the following characteristics. First

finite labeled data are divided into patches as training samples. Simultaneously

a multiscale patch feature fusion network-based convolutional neural network (MPFF-CNN) is designed. The final model parameters and patch size are determined by testing the different sizes of patches. A preliminary change detection result of the test image can be obtained using this network. Second

the superpixel segmentation algorithm is used to segment the test image into many nonoverlapping homogenous regions and transfer the segmentation result to the preliminary test result to obtain the change detection result with a segmentation mark. An optimal segmentation scale is difficult to obtain in high-resolution remote sensing images. Thus

a multiscale segmentation algorithm is used in this work. Third

the quantities of changed and unchanged pixels of each superpixel in the change detection result with the segmentation marker are statistically evaluated using the hand-crank voting algorithm. All the pixel values of the superpixel are replaced with the pixel values. The categories are more under this superpixel. All the change detection results are stacked to a hypercolumn. Lastly

the quantities of changed and unchanged pixels of each spectrum in the hypercolumn is counted using the hand-crank voting algorithm. If the number of changed pixels is more than that of the unchanged pixels in each spectrum

then the spatial position of the spectrum has a pixel value of 1; otherwise

the pixel value is 0.

Result

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To estimate the accuracy of the proposed change detection algorithm

experiments are conducted on the Guangdong change detection dataset and the Hong Kong change detection dataset. Four widely used methods are selected for comparison

including the fuzzy C-means-based

support vector machine-based

deep belief network-based

and long short-term memory-based methods. The experimental results of change detection in both datasets indicate that MPFF-CNN exhibits an outstanding performance

which is better than the results of the CNN-based model at a single patch size. The change detection results obtained by the multiple superpixel scale are better than those of the single superpixel scale in both datasets. The percentages of correct classification and kappa obtained by deep learning combined with superpixel (MPFF-CNN-SP) are 97% and 80% for the Guangdong change detection dataset

respectively. MPFF-CNN-SP is 1% and 6% higher than MPFF-CNN. MPFF-CNN-SP is better than all the compared algorithms. Similarly

the percentages of correct classification and kappa obtained by MPFF-CNN-SP for the Hong Kong change detection dataset are 99% and 81%

respectively. MPFF-CNN-SP is 1% and 8% higher than MPFF-CNN. MPFF-CNN-SP is superior to all the compared algorithms.

Conclusion

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The multiscale patch feature fusion method is better than the CNN-based method of training a single patch size. The multiscale patch feature fusion network-based CNN in this work can adequately gain the spatial information and other effective features of patches. Moreover

it does not cause severe overfitting. The multiscale superpixel segmentation results after double hand-raising voting algorithms are significantly stronger than the segmentation results of a single-scale superpixel segmentation. The deep learning and superpixel method transforms the detection unit from patch to superpixel

effectively judging different texture information of the same object and significantly improving change detection accuracy.