High-precision Registration Algorithm and Parallel Design Method for High-resolution Optical Remote Sensing Images

Automatic building extraction from high-resolution remote sensing images has many practical applications, such as urban planning and supervision. However, fine details and various scales of building structures in high-resolution images bring new challenges to building extraction. An increasing number of neural network-based models have been proposed to handle these issues, while they are not efficient enough, and still suffer from the error ground truth labels. To this end, we propose an efficient end-to-end model, EU-Net, in this paper. We first design the dense spatial pyramid pooling (DSPP) to extract dense and multi-scale features simultaneously, which facilitate the extraction of buildings at all scales. Then, the focal loss is used in reverse to suppress the impact of the error labels in ground truth, making the training stage more stable. To assess the universality of the proposed model, we tested it on three public aerial remote sensing datasets: WHU aerial imagery dataset, Massachusetts buildings dataset, and Inria aerial image labeling dataset. Experimental results show that the proposed EU-Net is superior to the state-of-the-art models of all three datasets and increases the prediction efficiency by two to four times.

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An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs13112052 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2052

Author(s):

Dongchuan Yan ◽

Guoqing Li ◽

Xiangqiang Li ◽

Hao Zhang ◽

Hua Lei ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

High Precision ◽

Large Scale ◽

Dam Failure ◽

Remote Sensing Images ◽

Feature Maps ◽

Tailings Pond ◽

Tailings Ponds

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.

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ShipRSImageNet: A Large-scale Fine-Grained Dataset for Ship Detection in High-Resolution Optical Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3104230 ◽

2021 ◽

pp. 1-1

Author(s):

Zhengning Zhang ◽

Lin Zhang ◽

Yue Wang ◽

Pengming Feng ◽

Ran He

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Large Scale ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection ◽

Fine Grained

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Fast and accurate denoising method applied to very high resolution optical remote sensing images

Image and Signal Processing for Remote Sensing XXIII ◽

10.1117/12.2277705 ◽

2017 ◽

Cited By ~ 5

Author(s):

Antoine Masse ◽

Sebastien Lefevre ◽

Renaud Binet ◽

Gwendoline Blanchet ◽

Stéphanie Artigues ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Denoising Method ◽

Very High

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Estimating the Volume of Oil Tanks Based on High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs11070793 ◽

2019 ◽

Vol 11 (7) ◽

pp. 793

Author(s):

Tong Wang ◽

Ying Li ◽

Shengtao Yu ◽

Yu Liu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

High Precision ◽

Relative Error ◽

Absolute Error ◽

Optical Remote Sensing ◽

Practical Applications ◽

Oil Tank ◽

Actual Height ◽

Oil Tanks

The purpose of this study is to obtain oil tank volumes from high-resolution satellite imagery to meet the need to measure oil tank volume globally. A preprocessed remote sensing HSV image is used to extract the shadow of the oil tank by Otsu thresholding, shadow area thresholding, and morphological closing. The oil tank shadow is crescent-shaped. Hence, a median method based on sub-pixel subdivision positioning is used to calculate the shadow length of the oil tank and then determine its height with high precision. The top of the tank and its radius in the image are identified using the Hough transform. The final tank volume is calculated using its height and radius. A high-resolution Gaofen 2 optical remote sensing image is used to evaluate the proposed method. The actual height and volume of the tank we tested were 21.8 m and 109,532 m3. The experimental results show that the mean absolute error of the height of the tank calculated by the median method is 0.238 m, the relative error is within 1.15%, and the RMES is 0.23. The result is better than the previous work. The absolute error between the calculated and the actual tank volumes ranges between 416 and 3050 m3, and the relative error ranges between 0.38% and 2.78%. These results indicate that the proposed method can calculate the volume of oil tanks with high precision and sufficient accuracy for practical applications.

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Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images

Applied Sciences ◽

10.3390/app9061130 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1130 ◽

Cited By ~ 4

Author(s):

Eric Wang ◽

Yueping Li ◽

Zhe Nie ◽

Juntao Yu ◽

Zuodong Liang ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Object Detection ◽

Fine Tuning ◽

Detection Accuracy ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Feature Extraction Method ◽

Feature Based ◽

Fusion Feature

With the rapid growth of high-resolution remote sensing image-based applications, one of the fundamental problems in managing the increasing number of remote sensing images is automatic object detection. In this paper, we present a fusion feature-based deep learning approach to detect objects in high-resolution remote sensing images. It employs fine-tuning from ImageNet as a pre-training model to address the challenge of it lacking a large amount of training datasets in remote sensing. Besides, we improve the binarized normed gradients algorithm by multiple weak feature scoring models for candidate window selection and design a deep fusion feature extraction method with the context feature and object feature. Experiments are performed on different sizes of high-resolution optical remote sensing images. The results show that our model is better than regular models, and the average detection accuracy is 8.86% higher than objNet.

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