AIMC Topic: Remote Sensing Technology

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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...

New deep learning method for efficient extraction of small water from remote sensing images.

PloS one
Extracting water bodies from remote sensing images is important in many fields, such as in water resources information acquisition and analysis. Conventional methods of water body extraction enhance the differences between water bodies and other inte...

Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review.

Water research
Detection and identification of macroplastic debris in aquatic environments is crucial to understand and counter the growing emergence and current developments in distribution and deposition of macroplastics. In this context, close-range remote sensi...

Evaluating the Forest Ecosystem through a Semi-Autonomous Quadruped Robot and a Hexacopter UAV.

Sensors (Basel, Switzerland)
Accurate and timely monitoring is imperative to the resilience of forests for economic growth and climate regulation. In the UK, forest management depends on citizen science to perform tedious and time-consuming data collection tasks. In this study, ...

Bioinspired Scene Classification by Deep Active Learning With Remote Sensing Applications.

IEEE transactions on cybernetics
Accurately classifying sceneries with different spatial configurations is an indispensable technique in computer vision and intelligent systems, for example, scene parsing, robot motion planning, and autonomous driving. Remarkable performance has bee...