This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly mean. The SST prediction problem is formulated as a time series regression problem. The proposed network architecture is composed of two kinds of layers: an LSTM layer and a full-connected dense layer. The LSTM layer is utilized to model the time series relationship. The full-connected layer is utilized to map the output of the LSTM layer to a final prediction. The optimal setting of this architecture is explored by experiments and the accuracy of coastal seas of China is reported to confirm the effectiveness of the proposed method. The prediction accuracy is also tested on the SST anomaly data. In addition, the model's online updated characteristics are presented.

Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model. LSTM naturally has the ability to learn the temporal relationship of time series data. Besides temporal information, spatial information is also included in our LSTM model. The local correlation and global coherence of each pixel can be expressed and retained by patches with fixed dimensions. The proposed model essentially combines the temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer. Experimental results on two data sets, i.e., one Advanced Very High Resolution Radiometer SST data set covering China Coastal waters and one National Oceanic and Atmospheric Administration High-Resolution SST data set covering the Bohai Sea, confirmed the effectiveness of the proposed model.

Cloud occlusion is a common problem in the satellite remote sensing (RS) field and poses great challenges for image processing and object detection. Most existing methods for cloud occlusion recovery extract the surrounding information from the single corrupted image rather than the historical RS image records. Moreover, the existing algorithms can only handle small and regular-shaped obnubilation regions. This letter introduces a deep convolutional generative adversarial network to recover the RS sea surface temperature images with cloud occlusion from the big historical image records. We propose a new loss function for the inpainting network, which adds a supervision term to solve our specific problem. Given a trained generative model, we search for the closest encoding of the corrupted image in the low-dimensional space using our inpainting loss function. This encoding is then passed through the generative model to infer the missing content. We conduct experiments on the RS image data set from the national oceanic and atmospheric administration. Compared with traditional and machine learning methods, both qualitative and quantitative results show that our method has advantages over existing methods.

High storage and computational costs obstruct deep neural networks to be deployed on resource-constrained devices. Knowledge distillation (KD) aims to train a compact student network by transferring knowledge from a larger pretrained teacher model. However, most existing methods on KD ignore the valuable information among the training process associated with training results. In this article, we provide a new collaborative teaching KD (CTKD) strategy which employs two special teachers. Specifically, one teacher trained from scratch (i.e., scratch teacher) assists the student step by step using its temporary outputs. It forces the student to approach the optimal path toward the final logits with high accuracy. The other pretrained teacher (i.e., expert teacher) guides the student to focus on a critical region that is more useful for the task. The combination of the knowledge from two special teachers can significantly improve the performance of the student network in KD. The results of experiments on CIFAR-10, CIFAR-100, SVHN, Tiny ImageNet, and ImageNet datasets verify that the proposed KD method is efficient and achieves state-of-the-art performance.

Turbid underwater environment poses great difficulties for the applications of vision technologies. One of the biggest challenges is the complicated noise distribution of the underwater images due to the serious scattering and absorption. To alleviate this problem, this work proposes a deep pixel‐to‐pixel networks model for underwater image enhancement by designing an encoding–decoding framework. It employs the convolution layers as encoding to filter the noise, while uses deconvolution layers as decoding to recover the missing details and refine the image pixel by pixel. Moreover, skip connection is introduced in the networks model in order to avoid low‐level features losing while accelerating the training process. The model achieves the image enhancement in a self‐adaptive data‐driven way rather than considering the physical environment. Several comparison experiments are carried out with different datasets. Results show that it outperforms the state‐of‐the‐art image restoration methods in underwater image defogging, denoising and colour enhancement.