AIMC Topic: Remote Sensing Technology

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Artificial Intelligence-Based Deep Fusion Model for Pan-Sharpening of Remote Sensing Images.

Computational intelligence and neuroscience
During the past two decades, many remote sensing image fusion techniques have been designed to improve the spatial resolution of the low-spatial-resolution multispectral bands. The main objective is fuse the low-resolution multispectral (MS) image an...

A Target Detection Algorithm for Remote Sensing Images Based on Deep Learning.

Contrast media & molecular imaging
In order to improve the accuracy of remote sensing image target detection, this paper proposes a remote sensing image target detection algorithm DFS based on deep learning. Firstly, dimension clustering module, loss function, and sliding window segme...

Water tank and swimming pool detection based on remote sensing and deep learning: Relationship with socioeconomic level and applications in dengue control.

PloS one
Studies have shown that areas with lower socioeconomic standings are often more vulnerable to dengue and similar deadly diseases that can be spread through mosquitoes. This study aims to detect water tanks installed on rooftops and swimming pools in ...

Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects.

Environmental monitoring and assessment
The use of neural network (NN) models for remote sensing (RS) retrieval of landscape biophysical and biochemical properties has become popular in the last decade. Recently, the emergence of "big data" that can be generated from remotely sensed data a...

Lightweight Deep Neural Network Method for Water Body Extraction from High-Resolution Remote Sensing Images with Multisensors.

Sensors (Basel, Switzerland)
Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction ...

Learning a Transform Base for the Multi- to Hyperspectral Sensor Network with K-SVD.

Sensors (Basel, Switzerland)
A promising low-cost solution for monitoring spectral information, e.g., on agricultural fields, is that of wireless sensor networks. In contrast to remote sensing, these can achieve more continuous monitoring due to their long-term deployment and ar...

Explainable machine learning models of major crop traits from satellite-monitored continent-wide field trial data.

Nature plants
Four species of grass generate half of all human-consumed calories. However, abundant biological data on species that produce our food remain largely inaccessible, imposing direct barriers to understanding crop yield and fitness traits. Here, we asse...

Deep learning-based remote sensing estimation of water transparency in shallow lakes by combining Landsat 8 and Sentinel 2 images.

Environmental science and pollution research international
Water transparency is a key indicator of water quality as it reflects the turbidity and eutrophication in lakes and reservoirs. To carry out remote sensing monitoring of water transparency rapidly and intelligently, deep learning technology was used ...

Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network.

Sensors (Basel, Switzerland)
Deep learning-based object detection in remote sensing images is an important yet challenging task due to a series of difficulties, such as complex geometry scene, dense target quantity, and large variant in object distributions and scales. Moreover,...

Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds.

PloS one
Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs' feeding trails, which are unvegetated winding tracks left after feeding, have been used ...