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Black and odorous water seriously affects the ecological balance of rivers and the health of people living nearby. Satellite remote sensing technology with its advantages of a large range, long-time series, low cost, and high efficiency, has provided a new area for water quality detection. Much archived remote sensing satellite data can be further processed and used as a data source for black and odorous water detection. In this paper, Gaofen-2 remote sensing data with a spatial resolution of 1 m is leveraged as the data source. To enrich the data source in the northern coastal zone of China, we have built a high-quality remote sensing dataset, called the remote sensing images for black and odorous water detection (RSBD) dataset, which is collected from the Gaofen-2 satellite in Yantai, China. In addition, we propose a network with an encoder-decoder discriminant structure for black and odorous water detection. In the network, an augmented attention module is designed to capture a more comprehensive semantic feature representation. Further, the median balancing loss function is adopted to solve the imbalance issues. Experimental results demonstrate that the network is superior to other state-of-the-art semantic segmentation methods on our dataset. [ABSTRACT FROM AUTHOR]

Satellite image classification is the core of satellite image interpretation. The traditional shallow learning classification algorithm is difficult to extract remote sensing image features effectively, resulting in low classification accuracy. The deep neural network can automatically realize the feature analysis. However, due to the similarity of the optical properties of the cloud and snow, the existing deep learning model is difficult to obtain satisfactory results for the cloud and snow classification in the plateau. Meanwhile, too many parameters of deep learning lead to slow detection speed, which is difficult to meet the real-time requirements of detection. Furthermore, the deep learning method is prone to model degradation. In view of the existing problems, this paper proposes a dilated multi-scale cascade forest method to realize the classification of satellite cloud images. Multi-scale scanning increases the diversity of feature vector extraction and improves the separability of features. The dilated structure is designed to expand the feature receptive field, it improves the efficiency of feature extraction without losing the texture and spatial correlation information of the original image. In addition, the number of layers of network cascading does not need to be customized, it can be determined automatically according to the performance of the system. The proposed method effectively improves the accuracy of cloud/snow classification. Experiments on the imagery of HuanJing satellites A and B (HJ-1A/1B) in China demonstrate that the proposed model can get better classification results than existing studies. The time complexity of the proposed model is lower than existing works, hence it is practical in real-time and accurate remote sensing image classification. [ABSTRACT FROM AUTHOR]

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Fujian Province, located in the southeastern coast of China, has a highly developed coastal marine net cage aquaculture industry. The scale and density of marine aquaculture, especially net cages aquaculture, in China are increasing, which has seriously affected coastal and nearshore environments. Thus, methods for accurately obtaining the area and density of aquaculture net cages hold great significance. Because of the scattered distribution of net cage aquaculture areas, it is difficult to obtain an accurate net cage aquaculture area with traditional remote-sensing extraction methods. Therefore, this paper proposes a method for accurately identifying of the area of aquaculture net cages by combining R3Det single-stage detector and fishnet segmentation technology. A constructed sensing net cage dataset was used for model training and a fishing net segmentation technology was used to segment the remote-sensing image into R3Det testable image. The experimental results showed that the extraction accuracy of this method for round and square net cages was 92.65% and 98.06%, respectively, and the extraction accuracy of the actual net cage culture area was 97.24%. We concluded that the total area of net cage aquaculture in Fujian Province in 2019–2020 was 4363.58 ha, mainly in square net cages. The area of aquaculture was greater in the northern and southern parts of the province and less in the middle. The density of aquaculture was high in the south and low in the north. Compared with traditional remote-sensing extraction methods and neural networks, this solution reduced the impact of similar features on the classification target, realized the application of high-precision, large-area remote-sensing images on the R3Det single-stage detector, and improved the recognition accuracy and the robustness of the model. [ABSTRACT FROM AUTHOR]

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing. [ABSTRACT FROM AUTHOR]

Stereo photogrammetric survey used to be used to extract the height of buildings, then to convert the height to number of stories through certain rules to estimate the number of stories of buildings by means of satellite remote sensing. In contrast, we propose a new method using deep learning to estimate the number of stories of buildings from monocular optical satellite image end to end in this paper. To the best of our knowledge, this is the first attempt to directly estimate the number of stories of buildings from monocular satellite images. Specifically, in the proposed method, we extend a classic object detection network, i.e., Mask R-CNN, by adding a new head to predict the number of stories of detected buildings from satellite images. GF-2 images from nine cities in China are used to validate the effectiveness of the proposed methods. The result of experiment show that the mean absolute error of prediction on buildings whose stories between 1–7, 8–20, and above 20 are 1.329, 3.546, and 8.317, respectively, which indicate that our method has possible application potentials in low-rise buildings, but the accuracy in middle-rise and high-rise buildings needs to be further improved. [ABSTRACT FROM AUTHOR]

Wan, J.X. and Ma, Y., 2020. Multi-scale spectral-spatial remote sensing classification of coral reef habitats using CNN-SVM. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 11-20. Coconut Creek (Florida), ISSN 0749-0208. In recent years, coral reefs have undergone serious degradation globally, prompting the use of remote sensing as an effective means to monitor these reefs on a large scale. Deep learning, which is a state-of-the-art image processing technique suitable for remote-sensing applications, can be used to learn nonlinear characteristics of images and is therefore applicable to the classification of small-scale coral reefs. This paper proposes a multi-scale method based on a convolutional neural network and support vector machine (CNN-SVM) to classify the coral reef habitats of Zhaoshu Island and Zhong Island in the Xisha Archipelago, China. This method combines spectrum, texture, and bidimensional empirical mode decomposition (BEMD) based scale separation algorithm to fully learn multi-scale information of coral reefs. Remote-sensing images captured by the WorldView-2 and Gaofen-2 (GF-2) satellites are used to evaluate the performance of the proposed CNN-SVM framework. The results indicate that the proposed method performs accurately and efficiently. Compared with SVM, random forest (RF), CNN, and CNN-RF, the overall accuracy is improved by 10.57 %, and the accuracy of classifying reef-clumping areas is improved by 17.44 %. [ABSTRACT FROM AUTHOR]