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Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …
management, environmental modelling and assessment, and agricultural production …
Spectral–spatial morphological attention transformer for hyperspectral image classification
In recent years, convolutional neural networks (CNNs) have drawn significant attention for
the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the …
the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the …
Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals
Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …
Spectral–spatial feature tokenization transformer for hyperspectral image classification
In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …
category. In the recent past, convolutional neural network (CNN)-based HSI classification …
Mst++: Multi-stage spectral-wise transformer for efficient spectral reconstruction
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or
wider convolutional neural networks (CNNs) to learn the end-to-end map** from the RGB …
wider convolutional neural networks (CNNs) to learn the end-to-end map** from the RGB …
Multimodal fusion transformer for remote sensing image classification
Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …
promising performance when compared with convolutional neural networks (CNNs). As a …
Hyperspectral image classification with multi-attention transformer and adaptive superpixel segmentation-based active learning
Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …
are widely used in hyperspectral image classification (HSIC). Some of these methods have …
Mask-guided spectral-wise transformer for efficient hyperspectral image reconstruction
Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from
a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The …
a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The …
Morphological transformation and spatial-logical aggregation for tree species classification using hyperspectral imagery
Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …
contribute to a more accurate identification of materials and land covers. However, most …