Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples.

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Flood is one of the most damaging natural hazards, and timely detection of it is very important to save human lives and assess the level of damage. The occurrence of floods in cloudy weather conditions makes the use of radar-based sensors for real-time flood mapping inevitable. In the present study, the ETCI 2021 flood event detection competition dataset, organized by the NASA Advanced Concepts and Implementation Team in collaboration with the IEEE GRSS Geoscience Informatics Technical Committee, has been used. Moreover, we have utilized the U-Net and X-Net architecture as a segmentation model to map flooded regions. This study aimed to identify the optimum polarization of the Sentinel-1 satellite for flood detection. By examining and comparing the obtained results, it was observed that the VV polarization offered better results in both models. Furthermore, U-Net had better performance than X-Net in both polarizations.

Abstract. Change Detection (CD) is one of the most crucial applications in remote sensing which identifies meaningful changes from bitemporal images taken from the same location. Enhancing the temporal efficiency and accuracy of this task is of great importance and one way to achieve this is through transfer learning. In this study, we investigate the influence of transferring pre-trained weights on the performance of a Siamese CD network using a benchmark dataset. For this purpose, an autoencoder with the same encoder architecture as in the Siamese model is trained on the whole dataset. Then, the encoder weights are transferred from the autoencoder and the Siamese model is trained in two modes. In the first mode, the transferred weights are frozen and only the decoder section of the Siamese models is trained while the second mode trains the whole model without freezing any part of the model. Moreover, the Siamese model is also trained without using the pre-trained weights to set the basis for comparisons. The results indicate that freezing the encoder results in a relatively lower performance but offers a considerable amount of temporal efficiency in the training phase. On the other hand, training the whole model after the weight transfer acquires the best result with an improvement of 12.43% in the Intersection over Union (IoU) metric.