Deep learning-based object detection techniques for remote sensing images: A survey
Z Li, Y Wang, N Zhang, Y Zhang, Z Zhao, D Xu, G Ben… - Remote Sensing, 2022 - mdpi.com
Object detection in remote sensing images (RSIs) requires the locating and classifying of
objects of interest, which is a hot topic in RSI analysis research. With the development of …
objects of interest, which is a hot topic in RSI analysis research. With the development of …
Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—Part 2: Recommendations and best practices
Convolutional neural network (CNN)-based deep learning (DL) has a wide variety of
applications in the geospatial and remote sensing (RS) sciences, and consequently has …
applications in the geospatial and remote sensing (RS) sciences, and consequently has …
Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made …
Sudden-onset natural and man-made disasters represent a threat to the safety of human life
and property. Rapid and accurate building damage assessment using bitemporal high …
and property. Rapid and accurate building damage assessment using bitemporal high …
Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark
Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains
a few pixels. State-of-the-art object detectors do not provide satisfactory results on tiny …
a few pixels. State-of-the-art object detectors do not provide satisfactory results on tiny …
Lightweight remote sensing change detection with progressive feature aggregation and supervised attention
Remote sensing change detection (RSCD) aims to explore surface changes from co-
registered pair of images. However, the high cost of memory and computation in previous …
registered pair of images. However, the high cost of memory and computation in previous …
A new spatial-oriented object detection framework for remote sensing images
Although the orientation and scale properties of the objects in remote sensing images have
been widely considered in the modern deep learning-based object detection methods, the …
been widely considered in the modern deep learning-based object detection methods, the …
Generalized few-shot object detection in remote sensing images
Recently few-shot object detection (FSOD) in remote sensing images (RSIs) has drawn
increasing attention. However, the current FSOD methods in RSIs merely focus on the …
increasing attention. However, the current FSOD methods in RSIs merely focus on the …
Coupling of deep learning and remote sensing: a comprehensive systematic literature review
M Yasir, W Jianhua, L Shanwei, H Sheng… - … Journal of Remote …, 2023 - Taylor & Francis
This study is conducted in accordance with a systematic literature review (SLR) protocol.
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …
Remote sensing change detection via temporal feature interaction and guided refinement
Remote sensing change detection (RSCD), which identifies the changed and unchanged
pixels from a registered pair of remote sensing images, has enjoyed remarkable success …
pixels from a registered pair of remote sensing images, has enjoyed remarkable success …
DS-YOLOv8-Based object detection method for remote sensing images
L Shen, B Lang, Z Song - Ieee Access, 2023 - ieeexplore.ieee.org
The improved YOLOv8 model (DCN_C2f+ SC_SA+ YOLOv8, hereinafter referred to as DS-
YOLOv8) is proposed to address object detection challenges in complex remote sensing …
YOLOv8) is proposed to address object detection challenges in complex remote sensing …