Remotely sensed land surface temperature (LST) with spatial continuity and high spatiotemporal resolution (hereafter referred to as high resolution ) is a crucial parameter for studying the thermal environment and has important applications in many fields. However, adverse atmospheric conditions, sensor malfunctioning, and scanning gaps between orbits frequently introduce spatial discontinuities into satellite-retrieved LST products. For a single sensor, a tradeoff occurs between temporal and spatial resolutions; therefore, it is almost impossible to obtain images in high resolution.

Due to the tradeoff between spatial and temporal resolutions commonly encountered in remote sensing, no single satellite sensor can provide fine spatial resolution land surface temperature (LST) products with frequent coverage. This situation greatly limits applications that require LST data with fine spatiotemporal resolution. Here, a deep learning-based spatiotemporal temperature fusion network (STTFN) method for the generation of fine spatiotemporal resolution LST products is proposed. In STTFN, a multiscale fusion convolutional neural network is employed to build the complex nonlinear relationship between input and output LSTs. Thus, unlike other LST spatiotemporal fusion approaches, STTFN is able to form the potentially complicated relationships through the use of training data without manually designed mathematical rules making it is more flexible and intelligent than other methods. In addition, two target fine spatial resolution LST images are predicted and then integrated by a spatiotemporal-consistency (STC)-weighting function to take advantage of STC of LST data. A set of analyses using two real LST data sets obtained from Landsat and moderate resolution imaging spectroradiometer (MODIS) were undertaken to evaluate the ability of STTFN to generate fine spatiotemporal resolution LST products. The results show that, compared with three classic fusion methods [the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the spatiotemporal integrated temperature fusion model (STITFM), and the two-stream convolutional neural network for spatiotemporal image fusion (StfNet)], the proposed network produced the most accurate outputs [average root mean square error (RMSE) < 1.40 °C and average structural similarity (SSIM) > 0.971].

Water body extraction from remote sensing images is an important task. Deep learning has become a more popular method for extracting water bodies from remote sensing images. However, these methods are usually aimed at a specific sensor and are not applicable. Thus, we proposed a new network, called the dense-local-feature-compression (DLFC) network aiming at extracting water body from different remote sensing images automatic. In this network, each layer of the network can receive the feature maps of all layers before it by the densely connected module of DenseNet. The concatenate operation on the feature dimension is used when connecting across layers. It can realize the different levels of features reuse. The local-feature-compression module is introduced before concatenate operation. It can obtain the more abstract features further by the convolution operation. Through the DLFC, we can fuse the spatial and spectral information for the remote sensing images that can extract water body from different remote sensing images. Besides, we construct a new water body dataset based on GaoFen-2 (GF-2) remote sensing images. The proposed DLFC achieved excellent performance with GF-2, GaoFen-6, Sentinel-2, and ZY-3 remote sensing images. Compared with the traditional water body extraction method and contemporary networks, the DLFC exhibits noticeable improvement. The results indicate that the DLFC can realize water body extraction from multisource remote sensing images automatically and rapidly.

Traditionally, clean reference images are needed to train the networks when applying the deep learning techniques to tackle image denoising tasks. However, this idea is impracticable for the task of synthetic aperture radar (SAR) image despeckling, since no real-world speckle-free SAR data exist. To address this issue, this article presents a noisy reference-based SAR deep learning filter, by using complementary images of the same area at different times as the training references. In the proposed method, to better exploit the information of the images, parameter-sharing convolutional neural networks are employed. Furthermore, to mitigate the training errors caused by the land-cover changes between different times, the similarity of each pixel pair between the different images is utilized to optimize the training process. The outstanding despeckling performance of the proposed method was confirmed by the experiments conducted on several multitemporal data sets, when compared with some of the state-of-the-art SAR despeckling techniques. In addition, the proposed method shows a pleasing generalization ability on single-temporal data sets, even though the networks are trained using finite input-reference image pairs at a different imaging area.

Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this task by using both high-level and low-level feature maps. However, it is difficult to utilize different level features rationally with the present deep learning networks. To tackle this problem, a novel network based on DenseNets and the attention mechanism was proposed, called the dense-attention network (DAN). The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module. The proposed encoder–decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only red–green–blue (RGB) images demonstrated that the proposed DAN achieved a higher score (96.16% overall accuracy (OA), 92.56% F1 score, 90.56% mean intersection over union (MIOU), less training and response time and higher-quality value) when compared with other deep learning methods.