Objective

2

The problem of environmental pollution in China has become increasingly serious with the rapid development of society and economy. Creating a beautiful ecological environment is becoming an important issue in the current national planning. Environmental pollution problems affect the sustainable development of the country and society. As an important source of pollution

cement plants must be effectively counted and monitored. With the development of satellite remote sensing

high-resolution and good quality images become available. At the same time

deep learning has made great progress in the field of target detection

and many excellent deep convolutional network models

such as Faster R-CNN

YOLO

SSD

and Mask R-CNN

have been proposed recently. In the object detection task on satellite images

huge differences lie in the plant area scales

the structures of equipment composition

and the orientations of each cement plant. Thus

various cement plants are presented with various appearances due to the complex natural geographical surroundings. Overcoming the problem of cement plant target detection and recognition using traditional artificial image feature methods is difficult. However

deep learning has achieved excellent performance in the field of image target recognition. The application of the deep convolutional network may be a brilliant method to locate cement plants on satellite images.

Method

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A method of detecting and locating cement plant position using high-resolution satellite images was proposed based on the convolutional neural network of Faster R-CNN framework for image target detection. First

we used GoogleMap API to download Google Earth satellite images. We developed this high-resolution satellite image dataset of cement factory target using GoogleMap web API

which contains 464 cement plant locations

according to the Beijing-Tianjin-Hebei cement dataset given by the Satellite Environment Center

Ministry of Environmental protection. Through the training and testing datasets of the cement plant in Beijing-Tianjin-Hebei with three different feature extracting modules (namely

VGG(visual geometry group network)

ZF

and ResNet)

we compared the testing results among the three CNN models. Three methods

which include image haze removal using dark channel prior

data augmentation

and adding negative training samples

were introduced to solve the problems of overfitting and reduce high false positive rate because of insufficient amount of training data. We also verified the influence of different numbers of negative training samples on model training. We used these features to assist cement plant target detection

considering the characteristics of cement buildings with evident cylindrical cement tanks and heating reaction tower buildings.

Result

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The visualized images of the convolutional feature map show that the identification of the deep convolution network for cement plant target detection is mainly based on special buildings in the plant area

such as cylindrical cement tanks

heating towers

and rectangular plants. The experimental results on the test set reveal that the ResNet achieves the best performance with the average accuracy rate of 74%. An optimization method by three methods is proposed to further enhance the detection of accurate rate and suppress the false positive rate. The precision of the promoted CNN model reached 94% in the augmented testing dataset

and the false positive rate was reduced to 14%. The true positive detection rate in the global cement plant dataset reached 96%

and the number of false detection of 10 000 random satellite images was reduced to 30 (0.3%). The actual cement plant target scan detection of the satellite images in the entire Shanghai area was conducted. To avoid dividing the cement plant into two image parts

we used the overlapping detection method

which also detected adjacent areas between satellite images. As a result

11 out of 16 registered cement plants

and 17 unregistered cement plants are detected.

Conclusion

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The cement plant has special buildings of different shapes and background

even varying over time

thereby resulting in relatively difficult detection task. However

the method of cement plant detection on satellite images based on deep convolutional networks can automatically learn to extract effective features and can identify the position of the target in the image. In addition

some optimization methods

including image preprocessing

data augmentation

and adding negative samples to promote the performance of the model

are adopted to improve the model detection accuracy and solve the problem of few training data. The geographic latitude and longitude coordinates of cement targets are easy to obtain based on the result of the image object detection and its position information because the satellite image is geocoded. We can also estimate the cement plant area using information of detected boxes. In the model generalization capability test experiment

the proposed method achieves good performance in the detection and location tasks in the global cement dataset. The scanning results of the entire satellite image set in Shanghai indicate that the deep convolution network target detection method not only can detect most registered cement plants but also multiple unregistered cement plants. The method provides a reliable reference for monitoring environmental pollution sources. Furthermore

this model can be easily converted to detect other architectural targets using transfer learning techniques.