Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images

The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation. However, the segmentation model based on a convolutional neural network cannot capture the global features very well. A transformer, whose self-attention mechanism can supply each pixel with a global feature, makes up for the deficiency of the convolutional neural network. Therefore, a multi-scale adaptive segmentation network model (MSST-Net) based on a Swin Transformer is proposed in this paper. Firstly, a Swin Transformer is used as the backbone to encode the input image. Then, the feature maps of different levels are decoded separately. Thirdly, the convolution is used for fusion, so that the network can automatically learn the weight of the decoding results of each level. Finally, we adjust the channels to obtain the final prediction map by using the convolution with a kernel of 1 × 1. By comparing this with other segmentation network models on a WHU building data set, the evaluation metrics, mIoU, F1-score and accuracy are all improved. The network model proposed in this paper is a multi-scale adaptive network model that pays more attention to the global features for remote sensing segmentation.

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IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs11030286 ◽

2019 ◽

Vol 11 (3) ◽

pp. 286 ◽

Cited By ~ 24

Author(s):

Jiangqiao Yan ◽

Hongqi Wang ◽

Menglong Yan ◽

Wenhui Diao ◽

Xian Sun ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Object Detection ◽

Convolutional Neural Network ◽

State Of The Art ◽

Ground Truth ◽

Detection Performance ◽

Candidate Region ◽

Detection Accuracy ◽

Remote Sensing Images

Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance of small objects is poor when choosing a normal higher threshold, while alower threshold will result in poor location accuracy caused by a large quantity of false positives.To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.

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A light and faster regional convolutional neural network for object detection in optical remote sensing images

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2018.05.005 ◽

2018 ◽

Vol 141 ◽

pp. 208-218 ◽

Cited By ~ 49

Author(s):

Peng Ding ◽

Ye Zhang ◽

Wei-Jian Deng ◽

Ping Jia ◽

Arjan Kuijper

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Object Detection ◽

Convolutional Neural Network ◽

Optical Remote Sensing ◽

Remote Sensing Images

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A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040189 ◽

2020 ◽

Vol 9 (4) ◽

pp. 189 ◽

Cited By ~ 3

Author(s):

Hongxiang Guo ◽

Guojin He ◽

Wei Jiang ◽

Ranyu Yin ◽

Lei Yan ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Convolutional Neural Network ◽

Water Body ◽

Semantic Segmentation ◽

Water Bodies ◽

Water Extraction ◽

Remote Sensing Images ◽

Multi Scale

Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.

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