Hyperspectral feature selection for SOM prediction using deep reinforcement learning and multiple subset evaluation strategies
L Zhao, K Tan, X Wang, J Ding, Z Liu, H Ma, B Han - Remote Sensing, 2022 - mdpi.com
It has been widely certified that hyperspectral images can be effectively used to monitor soil
organic matter (SOM). Though numerous bands reveal more details in spectral features …
organic matter (SOM). Though numerous bands reveal more details in spectral features …
An artificial intelligence dataset for solar energy locations in India
Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is
critical to mitigate climate change. As a result, India has set ambitious goals to install 500 …
critical to mitigate climate change. As a result, India has set ambitious goals to install 500 …
Local context normalization: Revisiting local normalization
Normalization layers have been shown to improve convergence in deep neural networks,
and even add useful inductive biases. In many vision applications the local spatial context of …
and even add useful inductive biases. In many vision applications the local spatial context of …
Visual sensation and perception computational models for deep learning: State of the art, challenges and prospects
Visual sensation and perception refers to the process of sensing, organizing, identifying, and
interpreting visual information in environmental awareness and understanding …
interpreting visual information in environmental awareness and understanding …
Machine learning for glacier monitoring in the Hindu Kush Himalaya
Glacier mapping is key to ecological monitoring in the hkh region. Climate change poses a
risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this …
risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this …
Adversarial attacks against a satellite-borne multispectral cloud detector
Data collected by Earth-observing (EO) satellites are often afflicted by cloud cover. Detecting
the presence of clouds—which is increasingly done using deep learning—is crucial …
the presence of clouds—which is increasingly done using deep learning—is crucial …
On the defense against adversarial examples beyond the visible spectrum
Machine learning (ML) models based on RGB images are vulnerable to adversarial attacks,
representing a potential cyber threat to the user. Adversarial examples are inputs …
representing a potential cyber threat to the user. Adversarial examples are inputs …
Generating natural adversarial remote sensing images
Over the last years, remote sensing image (RSI) analysis has started resorting to using deep
neural networks to solve most of the commonly faced problems, such as detection, land …
neural networks to solve most of the commonly faced problems, such as detection, land …
Optimizing honey traffic using game theory and adversarial learning
Enterprises are increasingly concerned about adversaries that slowly and deliberately
exploit resources over the course of months or even years. A key step in this kill chain is …
exploit resources over the course of months or even years. A key step in this kill chain is …
A realistic approach for network traffic obfuscation using adversarial machine learning
Adversaries are becoming more sophisticated and standard countermeasures such as
encryption are no longer enough to prevent traffic analysis from revealing important …
encryption are no longer enough to prevent traffic analysis from revealing important …