Min Wang
Nanjing Normal University
9 Papers
10 Citations
Min Wang is an academic researcher from Nanjing Normal University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 5, co-authored 8 publications.
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Papers
Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification
TL;DR: The superpixel extraction via SEEDS method was found to be the optimal superpixel segmentation approach for CNN classification, and the scale effect on CNN classification accuracy was investigated by comparing the four super pixel segmentation methods.
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A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification
TL;DR: A region-based majority voting CNN which combines the idea of GEOBIA and the deep learning technique is proposed, capable of keeping better segmentation accuracy and boundary fit and can fully utilize their exclusive nature to extract abstract deep features from images.
64
Object-Scale Adaptive Convolutional Neural Networks for High-Spatial Resolution Remote Sensing Image Classification
TL;DR: A novel method called object-scale adaptive convolutional neural network (OSA-CNN), which combines OBIA with CNN, is proposed for HSR image classification, which effectively enhances the image classification accuracy.
CNN-Based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data
TL;DR: Experimental analysis results suggested that the proposed method was promising for object-based classification, which smoothed and improved the fusion of point cloud and VHSRIs and thus performed well in follow-up classification.
44
Semi-Supervised Adversarial Semantic Segmentation Network Using Transformer and Multiscale Convolution for High-Resolution Remote Sensing Imagery
TL;DR: A novel semi-supervised adversarial semantic segmentation network is developed for remote sensing information extraction and it is suggested that the approach effectively improves the accuracy of semantic segmentsation.