Search Results for author:
Found 10 papers, 5 papers with code
Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks
To this end, we propose a new multi-modality network (MultiModNet) for land cover mapping of multi-modal remote sensing data based on a novel pyramid attention fusion (PAF) module and a gated fusion unit (GFU).
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation
Capturing global contextual representations by exploiting long-range pixel-pixel dependencies has shown to improve semantic segmentation performance.
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation
We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation.
Self-Constructing Graph Convolutional Networks for Semantic Labeling
Here, we propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings and to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs.
Dense Dilated Convolutions Merging Network for Land Cover Classification
In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task.
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network
This pushes the network towards learning more robust representations that are expected to boost the ultimate performance of the main task.
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images
We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and complex shaped objects with similar color and textures, such as buildings, surfaces/roads, and trees in very high resolution remote sensing images.
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function.
Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks
Techniques to improve urban land cover classification performance in remote sensing include fusion of data from different sensors with different data modalities.
Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data.
