Abstract
Conventional geographic object-based image analysis (GEOBIA) land cover classification methods by using very high resolution images are hardly applicable due to their complex ground truth and manually selected features, while convolutional neural networks (CNNs) with many hidden layers provide the possibility of extracting deep features from very high resolution images. Compared with pixel-based CNNs, superpixel-based CNN classification, carrying on the idea of GEOBIA, is more efficient. However, superpixel-based CNNs are still problematic in terms of their processing units and accuracies. Firstly, the limitations of salt and pepper errors and low boundary adherence caused by superpixel segmentation still exist; secondly, this method uses the central point of the superpixel as the classification benchmark in identifying the category of the superpixel, which does not allow classification accuracy to be ensured. To solve such problems, this paper proposes a region-based majority voting CNN which combines the idea of GEOBIA and the deep learning technique. Firstly, training data was manually labeled and trained; secondly, images were segmented under multiresolution and the segmented regions were taken as basic processing units; then, point voters were generated within each segmented region and the perceptive fields of points voters were put into the multi-scale CNN to determine their categories. Eventually, the final category of each region was determined in the region majority voting system. The experiments and analyses indicate the following: 1. region-based majority voting CNNs can fully utilize their exclusive nature to extract abstract deep features from images; 2. compared with the pixel-based CNN and superpixel-based CNN, the region-based majority voting CNN is not only efficient but also capable of keeping better segmentation accuracy and boundary fit; 3. to a certain extent, region-based majority voting CNNs reduce the impact of the scale effect upon large objects; and 4. multi-scales containing small scales are more applicable for very high resolution image classification than the single scale.
Highlights
With the development of remote sensing sensor technology such as WorldView 4 (WV-4), GaoFen-2, SuperView-1 etc., the spatial resolution of remote sensing images has been increasing year by year
A great deal of progress has been made in the field of Geographic object-based image analysis (GEOBIA); the classification features need to be manually selected before they are put into the classifier for classification
On the one hand, the computational efficiency can be greatly improved by reducing the number of spatial processing units to about one hundredth of those of pixel-convolutional neutral networks (CNNs), so that a large amount of computational time and hard disk space can be saved; on the other hand, the boundary errors caused by superpixel-CNN can be avoided in some situations, and the overall accuracy of the region-based majority voting CNN (RMV-CNN) image classification results is about 2% or even 4% higher than SEEDS-CNN
Summary
With the development of remote sensing sensor technology such as WorldView 4 (WV-4), GaoFen-2, SuperView-1 etc., the spatial resolution of remote sensing images has been increasing year by year. RMV-CNN introduces good boundary fitting segmentation into multi-scale CNN image classification through a new majority voting strategy to overcome the limitation of region shape for CNNs. on the one hand, the computational efficiency can be greatly improved by reducing the number of spatial processing units to about one hundredth of those of pixel-CNN, so that a large amount of computational time and hard disk space can be saved; on the other hand, the boundary errors caused by superpixel-CNN can be avoided in some situations, and the overall accuracy of the RMV-CNN image classification results is about 2% or even 4% higher than SEEDS-CNN.
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