Results 41 to 50 of about 417 (152)

Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation

Remote Sensing, 2018
High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to ...
Tianming Zhan, Le Sun, Yang Xu, Guowei Yang, Yan Zhang, Zebin Wu   +5 more
doaj   +1 more source

Multiscale Adaptive Convolution for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Convolutional neural network (CNN) is widely used in hyperspectral image (HSI) classification owing to their advantages of spatial-spectral features capture capability and learning depth features as well as their structural flexibility. Nevertheless, the
Qi Ren, Bing Tu, Qianming Li, Wangquan He, Yishu Peng   +4 more
doaj   +1 more source

GCD‐Net: A Gated and Cross‐Dynamically Enhanced Network for Accurate Extraction of Offshore Raft Aquaculture

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This study proposes a Gated and Cross‐Dynamically Enhanced Network (GCD‐Net) for accurate extraction of offshore raft aquaculture from Sentinel‐2 imagery. GCD‐Net integrates novel gated residual blocks, cross‐guided attention, and dynamic attention ASPP modules to enhance feature representation and boundary precision.
Yu Wang, Lianwei Li, Cunjin Xue, Zhi Zheng, Shiyu Wu   +4 more
wiley   +1 more source

Smart Brain Tumor Segmentation and Classification Using Inception ResNet‐V2 and Eagle Eye Vision in IoMT

Applied Computational Intelligence and Soft Computing, Volume 2026, Issue 1, 2026.
Brain tumors develop due to the unregulated proliferation of nerve tissues. Nonetheless, despite progress in deep learning models for medical image analysis, the precise segmentation of tumor patches and categorization of tumor types remain unresolved.
A. Ashwini, Emmanvel Raj M. Chirchi, V. Sheeja Kumari, P. Gururama Senthilvel, Frie Ayalew, Pramita Mishra   +5 more
wiley   +1 more source

Land Use Classification of High-Resolution Multispectral Satellite Images With Fine-Grained Multiscale Networks and Superpixel Postprocessing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Land use recognition from multispectral satellite images is fundamentally critical for geological applications, but the results are not satisfied.
Yaobin Ma, Xiaohua Deng, Jingbo Wei
doaj   +1 more source

Trees Outside Forests (TOFs): A Global Review of Methodological Advances, Limitations, and Framework Innovations

International Journal of Forestry Research, Volume 2026, Issue 1, 2026.
Trees outside forests (TOFs) represent an important natural and anthropogenic resource that contributes significantly to biodiversity conservation, climate resilience, and the protection of forest genetic resources (FGRs). Despite their ecological and socioeconomic importance, TOFs are increasingly declining due to land‐use changes, urbanization ...
Aman Dabral, Pawan Kumar Thakur, Maneesh S. Bhandari, Vishal Johar, Vinita Bisht, Shailesh Pandey, Rama Kant, Rajendra K. Meena, Himanshu Saini, Poulami Jha   +9 more
wiley   +1 more source

Dual-Scale Pixel Aggregation Transformer for Change Detection in Multitemporal Remote Sensing Images

Remote Sensing
Transformers have recently been applied to change detection (CD) of multitemporal remote sensing images because of their ability to model global information.
Kai Zhang, Ziqing Wan, Xue Zhao, Feng Zhang, Ke Liu, Jiande Sun   +5 more
doaj   +1 more source

Multiscale Color‐R2D2 Features and Alignment‐Based Filtering for Reliable Copy–Move Forgery Detection

International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.
This study presents a robust and descriptor‐agnostic framework for copy–move forgery detection (CMFD) that combines handcrafted and deep features within a unified geometric refinement stage. The proposed approach integrates a lightweight alignment‐based filtering (ABF) mechanism with multiple descriptors to reduce false positives left unresolved by ...
Yıldız Aydın, Richard Murray
wiley   +1 more source

Relieve the Demand for Labeled Data of Deep Learning Models for Hydraulic Conductivity Field Tasks in Groundwater Through Self‐Supervised Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 2, Issue 4, December 2025.
Abstract Deep learning (DL) has shown great potential in solving groundwater problems but often requires large labeled data sets, which are expensive and time‐consuming to obtain. In this study, we introduce a self‐supervised learning approach based on a masked autoencoder (MAE)—an encoder‐decoder architecture that reconstructs randomly masked input ...
Kai Ji, Zhengkun Zhou, Bo Zou, Ruiting Yang, Baoqing Zhang   +4 more
wiley   +1 more source

Using Deep Learning in Infrared Images to Enable Human Gesture Recognition for Autonomous Vehicles

IEEE Access, 2020
The realization of a novel human gesture recognition algorithm is essential to enable the effective collision avoidance of autonomous vehicles. Compared to visible spectrum cameras, the use of infrared imaging can enable more robust human gesture ...
Keke Geng, Guodong Yin
doaj   +1 more source