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Remote Sensing Technology

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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 ...

A Method for Extracting Building Information from Remote Sensing Images Based on Deep Learning.

Computational intelligence and neuroscience
Semantic segmentation of remote sensing images is an important issue in remote sensing tasks. Existing algorithms can extract information more accurately, but it is difficult to capture the contours of objects and further reveal the interaction infor...

CAW: A Remote-Sensing Scene Classification Network Aided by Local Window Attention.

Computational intelligence and neuroscience
Remote-sensing image scene data contain a large number of scene images with different scales. Traditional scene classification algorithms based on convolutional neural networks are difficult to extract complex spatial distribution and texture informa...

Transformer based on channel-spatial attention for accurate classification of scenes in remote sensing image.

Scientific reports
Recently, the scenes in large high-resolution remote sensing (HRRS) datasets have been classified using convolutional neural network (CNN)-based methods. Such methods are well-suited for spatial feature extraction and can classify images with relativ...

Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism.

Computational intelligence and neuroscience
Cropland extraction from remote sensing images is an essential part of precise digital agriculture services. This paper proposed an SSGNet network of multiscale fused extraction of cropland based on the attention mechanism to address issues with comp...

An Efficient Deep Learning Mechanism for the Recognition of Olive Trees in Jouf Region.

Computational intelligence and neuroscience
Olive trees grow all over the world in reasonably moderate and dry climates, making them fortunate and medicinal. Pesticides are required to improve crop quality and productivity. Olive trees have had important cultural and economic significance sinc...

Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical analysis.

Scientific reports
Continuous technological growth and the corresponding environmental implications are triggering the enhancement of advanced environmental monitoring solutions, such as remote sensing. In this paper, we propose a new method for the spatial point patte...