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Found 11 papers, 6 papers with code
MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture
After the backbone feature extraction, two feature maps are learned for each task.
GMM-IL: Image Classification using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes
Current deep learning classifiers, carry out supervised learning and store class discriminatory information in a set of shared network weights.
Facing the Void: Overcoming Missing Data in Multi-View Imagery
In some scenarios, a single input image may not be enough to allow the object classification.
AiRound and CV-BrCT: Novel Multi-View Datasets for Scene Classification
Despite a large number of public repositories for both georeferenced photographs and aerial images, there is a lack of benchmark datasets that allow the development of approaches that exploit the benefits and complementarity of aerial/ground imagery.
Fully Convolutional Open Set Segmentation
In semantic segmentation knowing about all existing classes is essential to yield effective results with the majority of existing approaches.
Towards Open-Set Semantic Segmentation of Aerial Images
A more uncommon source of images exploited in the remote sensing field are satellite and aerial images.
An Introduction to Deep Morphological Networks
Results show that the proposed DeepMorphNets is a promising technique that can learn distinct features when compared to the ones learned by current deep learning methods.
Spatio-Temporal Vegetation Pixel Classification By Using Convolutional Networks
Plant phenology studies rely on long-term monitoring of life cycles of plants.
Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks
A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties.
Exploiting ConvNet Diversity for Flooding Identification
Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities.
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors.
