AIMC Topic: Remote Sensing Technology

Clear Filters Showing 141 to 150 of 302 articles

Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing.

Sensors (Basel, Switzerland)
Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission's success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in t...

Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.

Nature communications
New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which ar...

Measuring the crop water demand and satisfied degree using remote sensing data and machine learning method in monsoon climatic region, India.

Environmental science and pollution research international
Supply of water is one of the most significant determinants of regional crop production and human food security. To promote sustainable management of agricultural water, the crop water requirement assessment (CropWRA) model was introduced as a tool f...

Self-supervision assisted multimodal remote sensing image classification with coupled self-looping convolution networks.

Neural networks : the official journal of the International Neural Network Society
Recently, remote sensing community has seen a surge in the use of multimodal data for different tasks such as land cover classification, change detection and many more. However, handling multimodal data requires synergistically using the information ...

Combining Deep Learning and Hydrological Analysis for Identifying Check Dam Systems from Remote Sensing Images and DEMs in the Yellow River Basin.

International journal of environmental research and public health
Identifying and extracting check dams is of great significance for soil and water conservation, agricultural management, and ecological assessment. In the Yellow River Basin, the check dam, as a system, generally comprises dam locations and dam-contr...

A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring.

IEEE transactions on biomedical circuits and systems
Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used...

Recognizing and monitoring infectious sources of schistosomiasis by developing deep learning models with high-resolution remote sensing images.

Infectious diseases of poverty
BACKGROUND: China is progressing towards the goal of schistosomiasis elimination, but there are still some problems, such as difficult management of infection source and snail control. This study aimed to develop deep learning models with high-resolu...

Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification.

Sensors (Basel, Switzerland)
As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks ...

Remote Sensing and Nonlinear Auto-regressive Neural Network (NARNET) Based Surface Water Chemical Quality Study: A Spatio-Temporal Hybrid Novel Technique (STHNT).

Bulletin of environmental contamination and toxicology
In recent days, the quality of water in inland water bodies has been threatened by various natural and anthropogenic activities. Henceforth, the continuous monitoring of water quality is mandatory to control the pollution level in surface water bodie...

Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images.

Computational intelligence and neuroscience
Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote ...