Optical Remote Sensing Images Research Articles

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

Extraction of Surface Water Bodies using Optical Remote Sensing Images: A Review

Extraction of Surface Water Bodies using Optical Remote Sensing Images: A Review

Global and Multiscale Aggregate Network for Saliency Object Detection in Optical Remote Sensing Images

Salient Object Detection (SOD) is gradually applied in natural scene images. However, due to the apparent differences between optical remote sensing images and natural scene images, directly applying the SOD of natural scene images to optical remote sensing images has limited performance in global context information. Therefore, salient object detection in optical remote sensing images (ORSI-SOD) is challenging. Optical remote sensing images usually have large-scale variations. However, the vast majority of networks are based on Convolutional Neural Network (CNN) backbone networks such as VGG and ResNet, which can only extract local features. To address this problem, we designed a new model that employs a transformer-based backbone network capable of extracting global information and remote dependencies. A new framework is proposed for this question, named Global and Multiscale Aggregate Network for Saliency Object Detection in Optical Remote Sensing Images (GMANet). In this framework, the Pyramid Vision Transformer (PVT) is an encoder to catch remote dependencies. A Multiscale Attention Module (MAM) is introduced for extracting multiscale information. Meanwhile, a Global Guiled Brach (GGB) is used to learn the global context information and obtain the complete structure. Four MAMs are densely connected to this GGB. The Aggregate Refinement Module (ARM) is used to enrich the details of edge and low-level features. The ARM fuses global context information and encoder multilevel features to complement the details while the structure is complete. Extensive experiments on two public datasets show that our proposed framework GMANet outperforms 28 state-of-the-art methods on six evaluation metrics, especially E-measure and F-measure. It is because we apply a coarse-to-fine strategy to merge global context information and multiscale information.

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

The reactivation of old landslides can be triggered by heavy destructive earthquakes, heavy rainfall, and ongoing human activities, thereby resulting in the occurrence of secondary landslides. However, most existing models are designed for detecting nascent landslides and there are few algorithms for old landslide detection. In this paper, we introduce a novel landslide detection model known as YOLOv8-CW, built upon the YOLOv8 (You Only Look Once) architecture, to tackle the formidable challenge of identifying old landslides. We replace the Complete-IoU loss function in the original model with the Wise-IoU loss function to mitigate the impact of low-quality samples on model training and improve detection recall rate. We integrate a CBAM (Convolutional Block Attention Module) attention mechanism into our model to enhance detection accuracy. By focusing on the southwest river basin of the Sichuan–Tibet area, we collect 558 optical remote sensing images of old landslides in three channels from Google Earth and establish a dataset specifically for old landslide detection. Compared to the original model, our proposed YOLOv8-CW model achieves an increase in detection accuracy of 10.9%, recall rate of 6%, and F1 score from 0.66 to 0.74, respectively. These results demonstrate that our improved model exhibits excellent performance in detecting old landslides within the Sichuan–Tibet area.

Ship Target Detection in Optical Remote Sensing Images Based on E2YOLOX-VFL

In this research, E2YOLOX-VFL is proposed as a novel approach to address the challenges of optical image multi-scale ship detection and recognition in complex maritime and land backgrounds. Firstly, the typical anchor-free network YOLOX is utilized as the baseline network for ship detection. Secondly, the Efficient Channel Attention module is incorporated into the YOLOX Backbone network to enhance the model’s capability to extract information from objects of different scales, such as large, medium, and small, thus improving ship detection performance in complex backgrounds. Thirdly, we propose the Efficient Force-IoU (EFIoU) Loss function as a replacement for the Intersection over Union (IoU) Loss, addressing the issue whereby IoU Loss only considers the intersection and union between the ground truth boxes and the predicted boxes, without taking into account the size and position of targets. This also considers the disadvantageous effects of low-quality samples, resulting in inaccuracies in measuring target similarity, and improves the regression performance of the algorithm. Fourthly, the confidence loss function is improved. Specifically, Varifocal Loss is employed instead of CE Loss, effectively handling the positive and negative sample imbalance, challenging samples, and class imbalance, enhancing the overall detection performance of the model. Then, we propose Balanced Gaussian NMS (BG-NMS) to solve the problem of missed detection caused by the occlusion of dense targets. Finally, the E2YOLOX-VFL algorithm is tested on the HRSC2016 dataset, achieving a 9.28% improvement in mAP compared to the baseline YOLOX algorithm. Moreover, the detection performance using BG-NMS is also analyzed, and the experimental results validate the effectiveness of the E2YOLOX-VFL algorithm.

Research Progress of Deep Learning Algorithms for Object Detection in Optical Remote Sensing Images

Research Progress of Deep Learning Algorithms for Object Detection in Optical Remote Sensing Images

Improved YOLOv5s With Coordinate Attention for Small and Dense Object Detection From Optical Remote Sensing Images

Improved YOLOv5s With Coordinate Attention for Small and Dense Object Detection From Optical Remote Sensing Images

A Decoupled Head and Multiscale Coordinate Convolution Detection Method for Ship Targets in Optical Remote Sensing Images

A Decoupled Head and Multiscale Coordinate Convolution Detection Method for Ship Targets in Optical Remote Sensing Images

DETR with Improved DeNoising Training for Multi-Scale Oriented Object Detection in Optical Remote Sensing Images (Invited)

DETR with Improved DeNoising Training for Multi-Scale Oriented Object Detection in Optical Remote Sensing Images (Invited)

Multi-scale and Multi-dimensional Weighted Network for Salient Object Detection in Optical Remote Sensing Images

Multi-scale and Multi-dimensional Weighted Network for Salient Object Detection in Optical Remote Sensing Images

Feature Super-Resolution Fusion With Cross-Scale Distillation for Small-Object Detection in Optical Remote Sensing Images

Feature Super-Resolution Fusion With Cross-Scale Distillation for Small-Object Detection in Optical Remote Sensing Images

High-Resolution Feature Generator for Small-Ship Detection in Optical Remote Sensing Images

High-Resolution Feature Generator for Small-Ship Detection in Optical Remote Sensing Images

Adaptive Mid-Level Feature Attention Learning for Fine-Grained Ship Classification in Optical Remote Sensing Images

Adaptive Mid-Level Feature Attention Learning for Fine-Grained Ship Classification in Optical Remote Sensing Images

Not All Boxes Are Equal: Learning to Optimize Bounding Boxes With Discriminative Distributions in Optical Remote Sensing Images

Not All Boxes Are Equal: Learning to Optimize Bounding Boxes With Discriminative Distributions in Optical Remote Sensing Images

Semi-Supervised Scene Classification for Optical Remote Sensing Images via Label and Embedding Consistency

Semi-Supervised Scene Classification for Optical Remote Sensing Images via Label and Embedding Consistency

Burned Area Segmentation in Optical Remote Sensing Images Driven by U-shaped Multi-stage Masked Autoencoder

Burned Area Segmentation in Optical Remote Sensing Images Driven by U-shaped Multi-stage Masked Autoencoder

Feature enhancement network for cloud removal in optical images by fusing with SAR images

ABSTRACT Presence of cloud-covered pixels is inevitable in optical remote-sensing images. Therefore, the reconstruction of the cloud-covered details is important to improve the usage of these images for subsequent image analysis tasks. Aiming to tackle the issue of high computational resource requirements that hinder the application at scale, this paper proposes a Feature Enhancement Network(FENet) for removing clouds in satellite images by fusing Synthetic Aperture Radar (SAR) and optical images. The proposed network consists of designed Feature Aggregation Residual Block (FAResblock) and Feature Enhancement Block (FEBlock). FENet is evaluated on the publicly available SEN12MS-CR dataset and it achieves promising results compared to the benchmark and the state-of-the-art methods in terms of both visual quality and quantitative evaluation metrics. It proved that the proposed feature enhancement network is an effective solution for satellite image cloud removal using less computational and time consumption. The proposed network has the potential for practical applications in the field of remote sensing due to its effectiveness and efficiency. The developed code and trained model will be available at https://github.com/chenxiduan/FENet.

A Method for Extracting Photovoltaic Panels from High-Resolution Optical Remote Sensing Images Guided by Prior Knowledge

The extraction of photovoltaic (PV) panels from remote sensing images is of great significance for estimating the power generation of solar photovoltaic systems and informing government decisions. The implementation of existing methods often struggles with complex background interference and confusion between the background and the PV panels. As a result, the completeness and edge clarity of PV panel extraction results are compromised. Moreover, most previous studies have overlooked the unique color characteristics of PV panels. To alleviate these deficiencies and limitations, a method for extracting photovoltaic panels from high-resolution optical remote sensing images guided by prior knowledge (PKGPVN) is proposed. Firstly, aiming to address the problems related to missed extractions and background misjudgments, a Photovoltaic Index (PVI) based on visible images in the three-band is constructed to serve as prior knowledge to differentiate between PV panels and non-PV panels. Secondly, in order to strengthen information interaction between shallow features and deep features and enhance the accuracy and integrity of results, a Residual Convolution Hybrid Attention Module (RCHAM) is introduced into the skip-connection of the encoding–decoding structure. Finally, for the purpose of reducing the phenomenon of blurred edges, a multilevel Feature Loss (FL) function is designed to monitor the prediction results at different scales. Comparative experiments are conducted with seven methods, including U-Net, on publicly available datasets. The experimental results show that our PKGPVN achieves superior performance in terms of evaluation metrics such as IoU (above 82%), Precision (above 91%), Recall (above 89%), and F1-score (above 90%) on the AIR-PV dataset. Additionally, the ablation experiments illustrate the effectiveness of our key parts. The proposed method reduces the phenomena of missed extractions and background misjudgments effectively while producing highly accurate results with clear boundaries.

Landslide mapping based on a hybrid CNN-transformer network and deep transfer learning using remote sensing images with topographic and spectral features

Landslides frequently cause serious property damage and casualties. Therefore, it is crucial to have rapid and accurate landslide mapping (LM) to support post-earthquake landslide damage assessment and emergency rescue efforts. Many studies have been conducted in recent years on the application of automatic LM methods using remote sensing images (RSIs). However, existing methods face challenges in accurately distinguishing landslides due to the problems of large differences in features and scales among landslides, as well as similarities among different ground objects in optical RSIs. Here, we propose a semantic segmentation model called SCDUNet++, which combines the advantages of convolutional neural network (CNN) and transformer to enhance the discrimination and extraction of landslide features. Then, we constructed a multi-channel landslide dataset in the Luding and Jiuzhaigou earthquake areas using Sentinel-2 and NASADEM data. We evaluated the performance of SCDUNet++ on this dataset. The results showed that SCDUNet++ can extract and fuse spectral and topographic information more effectively. Compared with other state-of-the-art models, SCDUNet++ achieved the highest IoU and F1 score in all four test areas. In addition, the models achieved significant improvements in mapping the landslides of the Jiuzhaigou area after knowledge transfer and fine-tuning. Compared with direct prediction, eight models, namely DeepLabv3+, Segformer, TransUNet, SwinUNet, STUNet, UNet, UNet++, and SCDUNet++, demonstrated improvements in IoU ranging from 8.33% to 27.5% and F1 from 6.58% to 23.67% after implementing deep transfer learning (DTL). This finding highlights the significant practicality of using DTL for cross-domain LM in data-poor areas.

Topographic Correction of Optical Remote Sensing Images in Mountainous Areas: A systematic review

Topographic Correction of Optical Remote Sensing Images in Mountainous Areas: A systematic review