Hyperspectral anomaly detection using deep learning: A review
Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots
in the field of remote sensing. Because HSI's features of integrating image and spectrum …
in the field of remote sensing. Because HSI's features of integrating image and spectrum …
ABNet: Adaptive balanced network for multiscale object detection in remote sensing imagery
Benefiting from the development of convolutional neural networks (CNNs), many excellent
algorithms for object detection have been presented. Remote sensing object detection …
algorithms for object detection have been presented. Remote sensing object detection …
Sliding dual-window-inspired reconstruction network for hyperspectral anomaly detection
Hyperspectral anomaly detection (HAD) aims to identify anomalous objects that deviate from
surrounding backgrounds in an unlabeled hyperspectral image (HSI). Most available neural …
surrounding backgrounds in an unlabeled hyperspectral image (HSI). Most available neural …
Deep feature aggregation network for hyperspectral anomaly detection
Hyperspectral anomaly detection (HAD) is a challenging task since it identifies the anomaly
targets without prior knowledge. In recent years, deep learning methods have emerged as …
targets without prior knowledge. In recent years, deep learning methods have emerged as …
Hyperspectral anomaly detection with relaxed collaborative representation
Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …
abundant spectral and spatial information contained in hyperspectral images. Recently …
Hyperspectral anomaly detection: a performance comparison of existing techniques
ABSTRACT Anomaly detection in Hyperspectral Imagery (HSI) has received considerable
attention because of its potential application in several areas. Numerous anomaly detection …
attention because of its potential application in several areas. Numerous anomaly detection …
Lraf-net: Long-range attention fusion network for visible–infrared object detection
Visible–infrared object detection aims to improve the detector performance by fusing the
complementarity of visible and infrared images. However, most existing methods only use …
complementarity of visible and infrared images. However, most existing methods only use …
C2Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection
Object detection on visible (RGB) and infrared (IR) images, as an emerging solution to
facilitate robust detection for around-the-clock applications, has received extensive attention …
facilitate robust detection for around-the-clock applications, has received extensive attention …
Memory-augmented autoencoder with adaptive reconstruction and sample attribution mining for hyperspectral anomaly detection
Hyperspectral anomaly detection (HAD) aims to identify targets that are significantly different
from their surrounding background, employing an unsupervised paradigm. Recently …
from their surrounding background, employing an unsupervised paradigm. Recently …
Weakly supervised video anomaly detection via self-guided temporal discriminative transformer
Weakly supervised video anomaly detection is generally formulated as a multiple instance
learning (MIL) problem, where an anomaly detector learns to generate frame-level anomaly …
learning (MIL) problem, where an anomaly detector learns to generate frame-level anomaly …