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Found 9 papers, 4 papers with code
Mitigate Target-level Insensitivity of Infrared Small Target Detection via Posterior Distribution Modeling
Infrared Small Target Detection (IRSTD) aims to segment small targets from infrared clutter background.
Click on Mask: A Labor-efficient Annotation Framework with Level Set for Infrared Small Target Detection
Due to the small size of infrared targets, manual annotation consumes more resources and restricts the development of this field.
ILNet: Low-level Matters for Salient Infrared Small Target Detection
Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN's representation ability.
Robust Interference Mitigation techniques for Direct Position Estimation
Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required.
Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics
To evaluate the proposed methodology we consider a water mapping task where real data acquired by the Landsat and MODIS instruments are fused generating high spatial-temporal resolution image estimates.
Recursive classification of satellite imaging time-series: An application to water mapping, land cover classification and deforestation detection
Our method also improves the performance of deep learning-based classifiers without the need for additional training data.
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices
Here, ML blocks replace the individual processing blocks of an OFDM receiver, and we specifically describe this swapping for the legacy channel estimation, symbol demapping, and decoding blocks with Neural Networks (NNs).
Online multi-resolution fusion of space-borne multispectral images
Satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena.
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
Autoencoder (AEC) networks have recently emerged as a promising approach to perform unsupervised hyperspectral unmixing (HU) by associating the latent representations with the abundances, the decoder with the mixing model and the encoder with its inverse.
