Abstract It is very challenging to accurately segment smoke images because smoke has some adverse vision characteristics, such as anomalous shapes, blurred edges, and translucency. Existing methods cannot fully focus on the texture details of anomalous shapes and blurred edges simultaneously. To solve these problems, a Dense Multi‐scale context and Asymmetric pooling Embedding Network (DMAENet) is proposed to model the smoke edge details and anomalous shapes for smoke segmentation. To capture the feature information from different scales, a Dense Multi‐scale Context Module (DMCM) is proposed to further enhance the feature representation capability of our network under the help of asymmetric convolutions. To efficiently extract features for long‐shaped objects, the authors use asymmetric pooling to propose an Asymmetric Pooling Enhancement Module (APEM). The vertical and horizontal pooling methods are responsible for enhancing features of irregular objects. Finally, a Feature Fusion Module (FFM) is designed, which accepts three inputs for improving performance. Low and high‐level features are fused by pixel‐wise summing, and then the summed feature maps are further enhanced in an attention manner. Experimental results on synthetic and real smoke datasets validate that all these modules can improve performance, and the proposed DMAENet obviously outperforms existing state‐of‐the‐art methods.

Wildfire is a severe natural disaster that poses a significant threat to the natural environment, as well as the safety of human life and property. The timely detection of wildfires plays a critical role in minimizing their detrimental impact. Himawari-8, a geostationary satellite equipped with an advanced Himawari imager (AHI) sensor, can provide full-disk data every 10 min, thus enabling near real-time and large-scale monitoring of wildfires. In this article, a wildfire detection method based on the spatiotemporal features of Himawari-8 data is proposed. First, a temporal convolutional network (TCN) is employed to predict the brightness temperature of the bands related to wildfire detection, achieving prediction results with a mean absolute error (MAE) of 0.28 K, a mean square error (MSE) of 0.30 K2, and a mean absolute percentage error (MAPE) of 0.10%. Then, various feature strategies are devised from spectral, spatial, and temporal aspects, and machine learning models are utilized for wildfire detection research. Among the considered strategies, strategy 4, which integrates spectral, spatial, and temporal features with the random forest (RF) algorithm, exhibits the most effective wildfire detection performance. It achieves a precision of 0.62, an omission of 0.34, and an F1-score of 0.64. Compared with the threshold method, precision increased by 0.05, omission decreased by 0.31, and F1-score increased by 0.21. To further evaluate practical applicability, the combination of strategy 4 and the RF is employed for wildfire detection near power grid transmission lines. In this scenario, out of the 295 real wildfires, 253 are successfully detected, resulting in a recall of 0.86. These experimental results affirm the effectiveness of the proposed method for wildfire detection.

Nowadays, Chinese power grid has developed very rapidly, and the transmission lines are massive. Our paper describes the use of an adaptive dynamic threshold algorithm and machine learning methods to detect wildfires in Yunnan province using Himawari-8. The algorithm extracts relevant features from the original NetCDF images and uses a dynamic threshold to identify wildfire pixels based on solar zenith angle and the proportion of cloud and non-vegetation pixels. Machine learning classifiers, including FCM+ SMOTE+SVM, are trained on the data using techniques to balance the dataset due to data imbalance. The improved classifier performs the best with a high accuracy for fire and non-fire pixels, outperforming other approaches including adaptive dynamic threshold, isolated forest, and one-class support vector machines. The FCM+SMOTE+SVM approach is shown to be robust for wildfire detection, but more data is needed to further improve its performance.

Wildfire is a serious natural disaster that poses a serious threat to the safety of human life and property. Currently, there are many researches related to satellite wildfire detection, but few can achieve near real-time monitoring results. Himawari-8 geostationary satellite can provide full disk data every 10 minutes, making near real-time monitoring of wildfires possible. In this paper, a wildfire detection method based on Himawari-8 for multi-temporal data is proposed. In our method, we use temporal convolutional network (TCN) to predict the brightness temperature and achieve excellent prediction results, the mean absolute error (MAE) is 0.28 K, mean square error (MSE) is 0.30 K2, and mean absolute percentage error (MAPE) is 0.10 %. Then, the predicted values combined with other features as model inputs, and machine learning classification models were used for wildfire detection. The experimental results showed that the combination of multi-layer perceptron (MLP) model and strategy 2 containing brightness temperature predicted values achieved an accuracy of 90.91% in wildfire detection.

The detection of electrical insulators in unmanned aerial vehicle (UAV) images using deep learning has made great progress in recent years, but little research has been conducted in the same field in remote sensing (RS) images. In this article, a novel method was proposed to detect insulators on 500-kV transmission towers in RS images. The proposed method consists of three components including 1) a super-resolution (SR) network to improve image resolution; 2) an object detection model to detect 110-, 220-, and 500-kV electrical power towers along transmission pipelines; and 3) a semantic segmentation network to identify insulators on the detected 500-kV towers. In addition, the online hard example mining (OHEM) method and class weight calculation method were utilized to handle the imbalanced data among different classes during training. The proposed model was evaluated on SuperView-1 and WorldView-3 satellite images collected in four regions. Experimental results show that the proposed method can effectively detect insulators in high-resolution satellite images and achieved the highest F1 score of 0.7952. The codes are available athttps://github.com/hardworking-jws/insulator-detection-remote-sensing

The potential safety hazards for the power grid caused by explosion of insulators occur again and again. Thus, the detecting and monitoring of insulators on the transmission towers is vital. In the paper, a novel method was proposed to detect insulators with high resolution satellites images. First, the SuperView-1 (0.5 m) and WorldView-3 (0.3 m) scenes of Yunnan were gathered, and then gram-schmidt method was used to fusion the original images. Second, a wide deep super resolution network (WDSR) is used to enhance the images resolution by 4 times. Third, fake color output and 1% linear stretched were applied to enhance image detail. Then, an object detection neural network based on feature pyramid networks (FPN) was used to detect transmission tower. Finally, a high-resolution network (HR-Net) was used to detect insulators on the tower. For comparison, three different class weight calculation methods and online hard example mining (OHEM) training methods of HR-Net were also proposed. HR-Net-c2-ohem final achieved highest 0.8001 of F1-Score. Therefore, our proposed method is robust to detect the insulators of transmission line tower with high resolution satellites images.

Remote sensing integrated with deep learning (DL) improves wildfire assessment. The research has been done to scrutinize the areas affected by disastrous wildfires in Yunnan using DL. Wildfire identification and demarcation of the affected area have been limited to primitive thresholding and outdated machine learning classification techniques. Therefore, the research work incorporated DL in the wildfire scrutiny, and several of the most important considerations are investigated. The proposed research objective is to exploit the recent advancement of capsule-based DL together with the wildfire domain. The proposed dense structure provides highly efficient detection and segmentation of the burned area (BA). The BA dense capsule network (BA_EnCaps) is employed to extract and localize the burned zone with an overall accuracy of 98%. The model is evaluated quantitatively using accuracy, binary_cross-entropy, dice_loss, and mean square error (mse). The research aims to utilize the segmentation model to estimate the BA with great results. BA-EnCaps shows excellent accuracy in discriminating the spectral indices for the burned zone. The proposed method surpasses other segmentation benchmark techniques (U-Net, U-Net3p, SegCaps, Deep U-Net, and U-Net+) by substantially lessening the computing power. Finally, BA_EnCaps is compared with standard segmentation techniques and shows that DL-based models can assess wildfire better than conventional algorithms.

When deploying network nodes, there are many redundant nodes, low network coverage and high energy consumption of network nodes, the node deployment method of wireless sensor networks(WSN) based on mobile edge computing is studied. WSN nodes are divided into anchor nodes and unknown nodes. Taking the location information of anchor nodes as a reference, the specific location of unknown nodes is obtained by trilateral measurement. Minimizing the node distance error is taken as the objective function, and the cuckoo search algorithm is used to solve it to obtain the final location result of the node. The mobile edge computing method is used to design the node deployment method of WSN to complete the node deployment. Simulation results show that the number of redundant nodes in this method is 3, maximum network coverage is 89%, maximum energy consumption of network nodes is 34.3J.

In the presented article, machine learning (ML) is employed on advanced Himawari imager (AHI) to examine real-time fire and map damaged zone over Yunnan, China. The main emphasis lies in employing machine learning as an alternative to primeval thresholding, extricating, and scrutinizing thermal anomaly using infrared (IR). Firstly, Himawari brightness temperature (BT), band ratio, albedo, and BT differences are utilized to scrutinize fire break out. Then, to ensure pixels are clear and free from clouds, the Himawari cloud product is implemented. Finally, machine learning models such as random forest (RF), artificial neural network (ANN), and time-series long short-term memory (LSTM), a deep learning model, are used to precisely classify the active fire pixels and achieved accuracies of 0.96%, 0.95%, 0.92% respectively. The results evaluated using another AHI wildfire product and inter-compared with multi-source fire products.

In recent years, the problem of land subsidence in urban areas has attracted more attention. differential interferometric synthetic aperture radar (D-InSAR) is a common surface deformation measurement technology. About our research, first of all, the processing effects of the ascending and descending images, VV polarization and VH polarization of Sentinel-1A data in the study area are compared. The ascending image with better coverage in the study area and VV polarization with better interference processing effect are selected. Second, the filtering algorithm and unwrapping algorithm in D-InSAR are contrasted. In terms of filtering algorithms, the improved Goldstein method with the coherence coefficient to adjust the power exponent of the weighting function in the frequency domain had the best filtering effect. In terms of unwrapping algorithms, there are fewer unwrapping islands in the minimum cost network flow method. Finally, D-InSAR is used to process the Sentinel-1A data to obtain the surface deformation results of downtown Chengdu. Therefore, the deformation of downtown Chengdu based on Sentinel-1A data is abtained.

In addition to GPS and leveling, there are also synthetic aperture radar (SAR) measurements in the field of remote sensing. Differential interferometric SAR (D-InSAR) is a surface deformation measurement technology developed from synthetic aperture radar interferometry (InSAR). In the research of D-InSAR, although the processing flow is certain, the selection of data and process method is not universal. In the process of InSAR interferometric data processing, phase unwrapping is the key link, which directly affects the accuracy of digital elevation model (DEM). In this paper, the phase unwrapping methods of dual pass D-InSAR are compared. There will be islands in the process of unwrapping, and the less the islanding, the better. Through the situation of islanding, the unwrapping effect of region growth method and minimum network cost flow method is compared. The results show that the best method is the minimum cost network flow method.

Wildfires may cause great casualties and heavy wildfires are becoming more and more frequently all over the world in recent years. However, due to the environmental limitation, high manual-dependent operation is often impractical with other limits. In this paper, a transfer learning neural network based on long short term memory (LSTM) was used to detect wildfire based on Himawari-8. The real time dynamic threshold value detection for cloud mask based on the modified Otsu algorithm was used to fast and accurately remove cloud areas where wildfire detection is failed due to signal blocking. Then, the experiments were conducted with LSTM and other models. The experimental results showed that our method was positive for wildfire detection.

Synthetic aperture radar (SAR) allows all-weather, day and night surveillance. Thus, it is of great significance for the ship detection and recognition. Because of the SAR special imaging mechanism, it is very difficult to extract the ship features with SAR image for the traditional target detection algorithm. In this paper, we proposed a approach which is composed of you only look once (YOLO) algorithm, sliding window detection strategy, and clustering algorithm. Firstly, the SAR images of GaoFen-3 and training dataset are gathered. Secondly, the experiments about the size of ship detection frame is carried out to find the optimum size of the frame for the training model. Thirdly, the ships are detected initially with YOLO v3 and fast region-based convolutional neural network (Fast-RCNN). Finally, the detected ships are clustered adaptively, and the experimental results of YOLO v3 and Fast-RCNN are compared and discussed at length. Our experimental results demonstrated that our method outperformed Fast-RCNN to detect the ships in the surface sea with low-resolution wide -band SAR images. Therefore, our approach is a robust method to detect the ships in the surface sea with SAR images.

Global warming results in the rapid melting of glacial lakes in the Himalayas. In this paper, we took the Karakoram range in the Himalayas as the study area, and we derived the glacial lake area in 2001, 2014, and 2017 with semantic segmentation algorithm. Firstly, five high-resolution images in Mount Lucania were collected from Google Earth. Secondly, each image was labelled, and 80000 training images with the size of 256×256 pixels were derived using augmentation approach. Thirdly, a model of the stacking network of U-Net and SegNet based on these training images to extract the glacial lake was trained. Then, another five images of Karakoram range were derived from Google Earth as a testing dataset to demonstrate the performance of training model. Finally, the glacial lakes were extracted from the images of Karakoram range in the same month in 2001, 2014 and 2017. Our result showed that the area of the glacial lake in Karakoram range increased rapidly from 2001 to 2014, but decreased from 2014 to 2017.

Hyperspectral image is usually composed of hundreds of bands rich of spatial and spectral information. And this is an advantage for the common remotely sensed data. Thus, the classification of hyperspectral image could be of great value. However, the dimensionality of hyperspectral image may lead to the curse of dimensionality phenomenon when it is directly used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we presented a novel classification framework with capsule network based on the spectral and spatial information of hyperspectral images. At first, we use principal components analysis (PCA) to reduce the dimensionalities of hyperspectral image. Then, we use the capsule network to classify hyperspectral image. Our experimental result showed the novel classification framework is more efficient than other six popular methods. Therefore, the capsule network method is robust for hyperspectral image classification.