RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA

7 citations as recorded by crossref.

1. Improving the accuracy of object classification on the ground based on a combination of sensory modalities using binary cross-tropism as a loss function. V. Legkobyt et al.
2. Improving Mineral Classification Using Multimodal Hyperspectral Point Cloud Data and Multi-Stream Neural Network A. Rizaldy et al.
3. AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture T. Anand et al.
4. A novel gradient foster shared-representation convolutional network optimization for multi-modalities A. Shikalgar & S. Sonavane
5. PolSAR Image Semantic Segmentation Based on Deep Transfer Learning—Realizing Smooth Classification With Small Training Sets W. Wu et al.
6. Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery E. Protopapadakis et al.
7. Building Detection and Outlining in Multi-Modal Remote Sensor Data: A Stratified Approach M. Böge et al.