Arbitrary-oriented object detection via dense feature fusion and attention model for remote sensing super-resolution image

Due to increasingly complex factors of image degradation, inferring high-frequency details of remote sensing imagery is more difficult compared to ordinary digital photos. This paper proposes an adaptive multi-scale feature fusion network (AMFFN) for remote sensing image super-resolution. Firstly, the features are extracted from the original low-resolution image. Then several adaptive multi-scale feature extraction (AMFE) modules, the squeeze-and-excited and adaptive gating mechanisms are adopted for feature extraction and fusion. Finally, the sub-pixel convolution method is used to reconstruct the high-resolution image. Experiments are performed on three datasets, the key characteristics, such as the number of AMFEs and the gating connection way are studied, and super-resolution of remote sensing imagery of different scale factors are qualitatively and quantitatively analyzed. The results show that our method outperforms the classic methods, such as Super-Resolution Convolutional Neural Network(SRCNN), Efficient Sub-Pixel Convolutional Network (ESPCN), and multi-scale residual CNN(MSRN).

Download Full-text

A fast super-resolution image reconstruction algorithm based on NL&DCT feature fusion

Artificial Intelligence and Industrial Application ◽

10.2495/aiia140471 ◽

2015 ◽

Author(s):

X. H. Zeng ◽

S. W. Peng ◽

R. J. Lv ◽

S. L. Hou

Keyword(s):

Image Reconstruction ◽

Feature Fusion ◽

Super Resolution ◽

Reconstruction Algorithm ◽

Image Reconstruction Algorithm ◽

Resolution Image

Download Full-text

Small Object Detection from Remote Sensing Images with the Help of Object-Focused Super-Resolution Using Wasserstein GANs

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323236 ◽

2020 ◽

Author(s):

Luc Courtrai ◽

Minh-Tan Pham ◽

Chloe Friguet ◽

Sebastien Lefevre

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Super Resolution ◽

Small Object ◽

Remote Sensing Images ◽

Small Object Detection

Download Full-text

CGP Box: An effective direction representation strategy for oriented object detection in remote sensing images

International Journal of Remote Sensing ◽

10.1080/01431161.2021.1941389 ◽

2021 ◽

Vol 42 (17) ◽

pp. 6670-6691

Author(s):

Qiuyu Guan ◽

Zhenshen Qu ◽

Ming Zeng ◽

Jianxiong Shen ◽

Jingda Du

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Remote Sensing Images ◽

Oriented Object

Download Full-text

Attention and Feature Fusion SSD for Remote Sensing Object Detection

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3052575 ◽

2021 ◽

Vol 70 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Xiaocong Lu ◽

Jian Ji ◽

Zhiqi Xing ◽

Qiguang Miao

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Feature Fusion

Download Full-text

Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network

Remote Sensing ◽

10.3390/rs13091854 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1854

Author(s):

Syed Muhammad Arsalan Bashir ◽

Yi Wang

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Resolution Enhancement ◽

Super Resolution ◽

Detection Performance ◽

Image Resolution ◽

Small Object ◽

Remote Sensing Images ◽

Feature Aggregation ◽

Image Super Resolution

This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small objects by improving the current super-resolution (SR) framework by incorporating a cyclic generative adversarial network (GAN) and residual feature aggregation (RFA) to improve detection performance. The novelty of the method is threefold: first, a framework is proposed, independent of the final object detector used in research, i.e., YOLOv3 could be replaced with Faster R-CNN or any object detector to perform object detection; second, a residual feature aggregation network was used in the generator, which significantly improved the detection performance as the RFA network detected complex features; and third, the whole network was transformed into a cyclic GAN. The image super-resolution cyclic GAN with RFA and YOLO as the detection network is termed as SRCGAN-RFA-YOLO, which is compared with the detection accuracies of other methods. Rigorous experiments on both satellite images and aerial images (ISPRS Potsdam, VAID, and Draper Satellite Image Chronology datasets) were performed, and the results showed that the detection performance increased by using super-resolution methods for spatial resolution enhancement; for an IoU of 0.10, AP of 0.7867 was achieved for a scale factor of 16.

Download Full-text

Improved Oriented Object Detection in Remote Sensing Images Based on a Three-Point Regression Method

Remote Sensing ◽

10.3390/rs13224517 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4517

Author(s):

Falin Wu ◽

Jiaqi He ◽

Guopeng Zhou ◽

Haolun Li ◽

Yushuang Liu ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Poor Performance ◽

Regression Method ◽

Remote Sensing Images ◽

Sensing Applications ◽

Bounding Box ◽

Bounding Boxes ◽

Fully Connected ◽

Oriented Object

Object detection in remote sensing images plays an important role in both military and civilian remote sensing applications. Objects in remote sensing images are different from those in natural images. They have the characteristics of scale diversity, arbitrary directivity, and dense arrangement, which causes difficulties in object detection. For objects with a large aspect ratio and that are oblique and densely arranged, using an oriented bounding box can help to avoid deleting some correct detection bounding boxes by mistake. The classic rotational region convolutional neural network (R2CNN) has advantages for text detection. However, R2CNN has poor performance in the detection of slender objects with arbitrary directivity in remote sensing images, and its fault tolerance rate is low. In order to solve this problem, this paper proposes an improved R2CNN based on a double detection head structure and a three-point regression method, namely, TPR-R2CNN. The proposed network modifies the original R2CNN network structure by applying a double fully connected (2-fc) detection head and classification fusion. One detection head is for classification and horizontal bounding box regression, the other is for classification and oriented bounding box regression. The three-point regression method (TPR) is proposed for oriented bounding box regression, which determines the positions of the oriented bounding box by regressing the coordinates of the center point and the first two vertices. The proposed network was validated on the DOTA-v1.5 and HRSC2016 datasets, and it achieved a mean average precision (mAP) of 3.90% and 15.27%, respectively, from feature pyramid network (FPN) baselines with a ResNet-50 backbone.

Download Full-text