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

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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Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

Remote Sensing ◽

10.3390/rs14010103 ◽

2021 ◽

Vol 14 (1) ◽

pp. 103

Author(s):

Dongchuan Yan ◽

Hao Zhang ◽

Guoqing Li ◽

Xiangqiang Li ◽

Hua Lei ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Object Detection ◽

Transfer Learning ◽

Near Infrared ◽

Image Data ◽

Remote Sensing Images ◽

Tailings Pond ◽

True Color ◽

Tailings Ponds

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.

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DETECTION OF ARTIFICIAL OBJECTS IN REMOTE SENSING IMAGE BASED ON DEEP LEARNING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-321-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 321-326 ◽

Cited By ~ 1

Author(s):

Y. Dai ◽

J. S. Xiao ◽

B. S. Yi ◽

J. F. Lei ◽

Z. Y. Du

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Object Detection ◽

Detection Method ◽

Remote Sensing Image ◽

Remote Sensing Images ◽

Feature Maps ◽

Aspect Ratios ◽

Efficient Detection

Abstract. Aiming at multi-class artificial object detection in remote sensing images, the detection framework based on deep learning is used to extract and localize the numerous targets existing in very high resolution remote sensing images. In order to realize rapid and efficient detection of the typical artificial targets on the remote sensing image, this paper proposes an end-to-end multi-category object detection method in remote sensing image based on the convolutional neural network to solve several challenges, including dense objects and objects with arbitrary direction and large aspect ratios. Specifically, in this paper, the feature extraction process is improved by utilizing a more advanced backbone network with deeper layers and combining multiple feature maps including the high-resolution features maps with more location details and low-resolution feature maps with highly-abstracted information. And a Rotating Regional Proposal Network is adopted into the Faster R-CNN network to generate candidate object-like regions with different orientations and to improve the sensitivity to dense and cluttered objects. The rotation factor is added into the regional proposal network to control the generation of anchor box’s angle and to cover enough directions of typical man-made objects. Meanwhile, the misalignment caused by the two quantifications operations in the pooling process is eliminated and a convolution layer is appended before the fully connected layer of the final classification network to reduce the feature parameters and avoid overfitting. Compared with current generic object detection method, the proposed algorithm focus on the arbitrary oriented and dense artificial targets in remote sensing images. After comprehensive evaluation with several state-of-the-art object detection algorithms, our method is proved to be effective to detect multi-class artificial object in remote sensing image. Experiments demonstrate that the proposed method combines the powerful features extracted by the improved convolutional neural networks with multi-scale features and rotating region network is more accurate in the public DOTA dataset.

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Extracting the Tailings Ponds from High Spatial Resolution Remote Sensing Images by Integrating a Deep Learning-Based Model

Remote Sensing ◽

10.3390/rs13040743 ◽

2021 ◽

Vol 13 (4) ◽

pp. 743

Author(s):

Jianjun Lyu ◽

Ying Hu ◽

Shuliang Ren ◽

Yao Yao ◽

Dan Ding ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Random Forest ◽

Spatial Resolution ◽

High Spatial Resolution ◽

High Accuracy ◽

Random Forest Algorithm ◽

Remote Sensing Images ◽

Tailings Pond ◽

Tailings Ponds

Due to a lack of data and practical models, few studies have extracted tailings pond margins in large areas. In addition, there is no public dataset of tailings ponds available for relevant research. This study proposed a new deep learning-based framework for extracting tailings pond margins from high spatial resolution (HSR) remote sensing images by combining You Only Look Once (YOLO) v4 and the random forest algorithm. At the same time, we created an open source tailings pond dataset based on HSR remote sensing images. Taking Tongling city as the study area, the proposed model can detect tailings pond locations with high accuracy and efficiency from a large HSR remote sensing image (precision = 99.6%, recall = 89.9%, mean average precision = 89.7%). An optimal random forest model and morphological processing were utilized to further extract accurate tailings pond margins from the target areas. The final map of the entire study area was obtained with high accuracy. Compared with the random forest algorithm, the total extraction time was reduced by nearly 99%. This study can be beneficial to mine monitoring and ecological environmental governance.

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Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽

10.3390/s18103232 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3232 ◽

Cited By ~ 17

Author(s):

Yan Liu ◽

Qirui Ren ◽

Jiahui Geng ◽

Meng Ding ◽

Jiangyun Li

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Large Scale ◽

Semantic Segmentation ◽

Remote Sensing Image ◽

Training Data ◽

Land Resources ◽

Remote Sensing Images ◽

Training Strategy ◽

The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

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Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

Remote Sensing ◽

10.3390/rs12182985 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2985 ◽

Cited By ~ 1

Author(s):

Yeneng Lin ◽

Dongyun Xu ◽

Nan Wang ◽

Zhou Shi ◽

Qiuxiao Chen

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Network Models ◽

Road Extraction ◽

Remote Sensing Images ◽

Proposed Model ◽

Wide Range ◽

Network Modules ◽

Very High

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

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Vehicle Detection in High Resolution Satellite Remote Sensing Images Based on Deep Learning

IEEE Access ◽

10.1109/access.2020.3017894 ◽

2020 ◽

Vol 8 ◽

pp. 153394-153402

Author(s):

Qulin Tan ◽

Juan Ling ◽

Jun Hu ◽

Xiaochun Qin ◽

Jiping Hu

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Satellite Remote Sensing ◽

Vehicle Detection ◽

Remote Sensing Images

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A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images

Remote Sensing Letters ◽

10.1080/2150704x.2018.1557791 ◽

2019 ◽

Vol 10 (4) ◽

pp. 381-390 ◽

Cited By ~ 12

Author(s):

Ye Li ◽

Lele Xu ◽

Jun Rao ◽

Lili Guo ◽

Zhen Yan ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Learning Method ◽

Remote Sensing Images ◽

Road Segmentation

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Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691314610104 ◽

2014 ◽

Vol 12 (05) ◽

pp. 1461010 ◽

Cited By ~ 1

Author(s):

Changmiao Hu ◽

Ping Tang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Surface ◽

Large Scale ◽

Near Infrared ◽

Low Frequency ◽

Median Filtering ◽

Remote Sensing Images ◽

Infrared Band ◽

Remote Sensing Image Processing

In recent years, China's demand for satellite remote sensing images increased. Thus, the country launched a series of satellites equipped with high-resolution sensors. The resolutions of these satellites range from 30 m to a few meters, and the spectral range covers the visible to the near-infrared band. These satellite images are mainly used for environmental monitoring, mapping, land surface classification and other fields. However, haze is an important factor that often affects image quality. Thus, dehazing technology is becoming a critical step in high-resolution remote sensing image processing. This paper presents a rapid algorithm for dehazing based on a semi-physical haze model. Large-scale median filtering technique is used to extract large areas of bright, low-frequency information from images to estimate the distribution and thickness of the haze. Four images from different satellites are used for experiment. Results show that the algorithm is valid, fast, and suitable for the rapid dehazing of numerous large-sized high-resolution remote sensing images in engineering applications.

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