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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 …
Auto-encoders in deep learning—a review with new perspectives
S Chen, W Guo - Mathematics, 2023 - mdpi.com
Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …
development of neural networks. The auto-encoder is a key component of deep structure …
Remote-sensing scene classification via multistage self-guided separation network
In recent years, remote-sensing scene classification is one of the research hotspots and has
played an important role in the field of intelligent interpretation of remote-sensing data …
played an important role in the field of intelligent interpretation of remote-sensing data …
Rsgpt: A remote sensing vision language model and benchmark
The emergence of large-scale large language models, with GPT-4 as a prominent example,
has significantly propelled the rapid advancement of artificial general intelligence and …
has significantly propelled the rapid advancement of artificial general intelligence and …
Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities
Remote sensing image scene classification, which aims at labeling remote sensing images
with a set of semantic categories based on their contents, has broad applications in a range …
with a set of semantic categories based on their contents, has broad applications in a range …
Global context-aware progressive aggregation network for salient object detection
Deep convolutional neural networks have achieved competitive performance in salient
object detection, in which how to learn effective and comprehensive features plays a critical …
object detection, in which how to learn effective and comprehensive features plays a critical …
Salient object detection in the deep learning era: An in-depth survey
As an essential problem in computer vision, salient object detection (SOD) has attracted an
increasing amount of research attention over the years. Recent advances in SOD are …
increasing amount of research attention over the years. Recent advances in SOD are …
Vision-language models in remote sensing: Current progress and future trends
The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-
4) have sparked a wave of interest and research in the field of large language models …
4) have sparked a wave of interest and research in the field of large language models …
When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs
Remote sensing image scene classification is an active and challenging task driven by
many applications. More recently, with the advances of deep learning models especially …
many applications. More recently, with the advances of deep learning models especially …
Scene classification with recurrent attention of VHR remote sensing images
Scene classification of remote sensing images has drawn great attention because of its wide
applications. In this paper, with the guidance of the human visual system (HVS), we explore …
applications. In this paper, with the guidance of the human visual system (HVS), we explore …