A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification

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.

BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

“…Zhou et al proposed So-CNN for urban functional zone fine division with VHR remote sensing images [22]. Lv et al proposed a new method for region-based majority voting CNNs for very high-resolution image classification [23]. As an important issue of ground feature extraction, automatic building extraction has also obtained many results in the application of convolutional neural networks.…”

Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective

“…Then, the spectral-spatial features from each scale patch are extracted through the multi-scale CNNs [32]. A region-based majority voting CNN is proposed in [33]. First, the images are segmented under multiple resolutions into multiple regions as the basic processing units.…”

“…However, the current multi-scale research focuses on the multi-scale feature extraction and fusion of the same training sample image [24][25][26][27][28][29][30][31]. There are few studies on how to consider the semantic analysis of variable regions in different spatial extents [32][33][34][35].…”

Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery

“…However, one of the limitations was that a wrong label could be assigned to the block as the process depended on the center of gravity of the irregular object. The work by Lv et al [23] explores a technique of majority voting for CNNs for very high resolution image classification. In Zhang et al [24], a patch-based object based CNN having multiple input windows is used for land use application and majority voting is used to classify the segments.…”