Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: altimeter; atmospheric turbulence; attention mechanism; attention pyramid; autoencoder; capacity estimation; carbon sequestration; cloud detection; cloud matting; cloud removal; CNN; computer vision; convolutional neural network; curriculum learning; data fusion; deep learning; depthwise separable convolutional neural networks; digital surface model; dilated convolution; discriminative representation learning; Earth Sciences, Geography, Environment, Planning; ensemble learning; evaluation methods; evaporation duct; feature separation; few-shot learning; fine-grained image classification; FlexibleNet; Gaussian process regression; Geography; global correlation information; image reconstruction; image super-resolution; improved transformer encoder; improved Tversky loss; interdimensional attention; lightweight convolutional neural network; live fuel moisture content; low-light image enhancement; LSTM; maritime communication; mode detection; model design; multi-dimensional prediction model; multi-scale supervision; multi-source remote sensing; multimodal; multiscale feature fusion; mutual information; n/a; noise suppression deblurring; optical remote sensing; orbital angular momentum; peri-urban forests; photovoltaics; principal component analysis; reconstruction refinement; Reference, Information and Interdisciplinary subjects; remote image; remote sensing; Research and information: general; retinex theory; robust deep learning; SAR; SAR target recognition; sea surface temperature; self-attention; semantic segmentation; semi-supervised learning; significant wave height; solar farm; solar panel; sound detection; spatiotemporal fusion; spectrogram augmentation; Transformer; turbulence degradation; two-step convolution model; vehicle detection
• ISBN: 303657946X; 9783036579467; 3036579478; 9783036579474
• DOI: 10.3390/books978-3-0365-7947-4

This reprint provides research on how technologies such as artificial intelligence-based machine learning and deep learning can be applied to remote sensing. Through this, we can see the process of solving the existing problems of image and image signal processing for remote sensing. These techniques are computationally intensive and require the help of high-performance computing devices. With the development of devices such as GPUs, remote sensing technology, and aerial sensing technology, it is possible to monitor the Earth with high-resolution images and to obtain vast amounts of Earth observation data. The papers published in this reprint describe recent advances in big data processing and artificial intelligence-based technologies for remote sensing technology.