Change detection (CD) by comparing two bi-temporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention in the community. However, existing unsupervised CD approaches rarely consider the seasonal and style differences incurred by the illumination and atmospheric conditions in multi-temporal images. To this end, we propose a change detection with domain shift setting for remote sensing images. Furthermore, we present a novel unsupervised CD method using a light-weight transformer, called UCDFormer. Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency. After image translation, we can generate the difference map between the translated before-event image and the original after-event image. Then, a novel reliable pixel extraction module is proposed to select significantly changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy c-means clustering and adaptive threshold. Finally, a binary change map is obtained based on these selected pixel pairs and a binary classifier. Experimental results on different unsupervised CD tasks with seasonal and style changes demonstrate the effectiveness of the proposed UCDFormer. For example, compared with several other related methods, UCDFormer improves performance on the Kappa coefficient by more than 12%. In addition, UCDFormer achieves excellent performance for earthquake-induced landslide detection when considering large-scale applications. The code is available at https://github.com/zhu-xlab/UCDFormer.

Cloud detection in optical imagery has drawn remarkable attention in the era of big Earth observation data analytic. While multiple supervised learning models have been developed for such purpose, large volumes of paired training samples annotated at the pixel level are essential to ensure the model’s generalization capacity. However, constructing a comprehensive cloud detection training database is a tedious and time-consuming process. To tackle this dilemma, we simply regard cloud-contaminated remote sensing (RS) imagery as the combination of cloud and background domains and propose a cloud detection framework based on image-to-image domain translation network (DTNet) to separate cloud-contaminated RS imagery into two target domains of cloud and background object images without using any paired and pixel-level annotation training data. The framework was evaluated with multispectral images from two types of sensors, Landsat-8 Operational Land Imager (OLI) (30 m) and GaoFen-1 (16 m), and demonstrated superior or comparable performance compared with several state-of-the-art cloud detection models.

Most convolutional neural network (CNN)-based cloud detection methods are built upon the supervised learning framework that requires a large number of pixel-level labels. However, it is expensive and time-consuming to manually annotate pixelwise labels for massive remote sensing images. To reduce the labeling cost, we propose an unsupervised domain adaptation (UDA) approach to generalize the model trained on labeled images of source satellite to unlabeled images of the target satellite. To effectively address the domain shift problem on cross-satellite images, we develop a novel UDA method based on grouped features alignment (GFA) and entropy minimization (EM) to extract domain-invariant representations to improve the cloud detection accuracy of cross-satellite images. The proposed UDA method is evaluated on “Landsat-$8~\rightarrow $ZY-3” and “GF-$1\rightarrow $ZY-3” domain adaptation tasks. Experimental results demonstrate the effectiveness of our method against existing state-of-the-art UDA approaches. The code of this paper has been made available online (https://github.com/nkszjx/grouped-features-alignment).

The detection of clouds in remote sensing (RS) images is an important task, and convolutional neural networks (CNNs) have been used to perform it. However, supervised cloud detection CNNs rely heavily on a large number of samples annotated at pixel level to tune their parameter. Annotating RS images is a labor-intensive procedure and requires expert-level human knowledge. To reduce the labeling cost, we propose an unsupervised domain adaptation (UDA) approach to enable the model trained on labeled source satellite images to generalize to unlabeled target satellite images. Specifically, we propose a fine-grained feature alignment (FGFA) domain adaptation strategy that encourages a cloud detection network to extract domain-invariant representations, which improves the accuracy of cloud detection in unlabeled target satellite images. The proposed FGFA strategy consists of two steps: 1) fine-grained class-relevant feature selection based on an attention-guided mechanism and 2) class-relevant feature alignment (FA) based on a proposed grouped FA approach. Experimental results on the “Landsat-$8~\rightarrow $ZY-3” and “GF-$1\rightarrow $ZY-3” domain adaptation tasks demonstrate the effectiveness of our method and its superiority to existing state-of-the-art UDA approaches.

Optical satellite images are often affected by haze atmospheric conditions, which degrades the quality of remote sensing (RS) data and reduces the accuracy of interpretation and classification. Hence, haze removal becomes a necessary preprocessing step for most of the applications of RS image. In this article, we propose a novel haze removal method for Landsat-8 OLI multispectral image based on an optimized atmospheric scattering model. We focus on adaptively estimating the haze transmission map of each band by taking into account the effect of both wavelength and haze atmospheric conditions (haze particle size and haze particle concentration) thus improving dehazing performance. The experimental results on Landsat-8 OLI multispectral images show that the proposed dehazing model is able to remove haze successfully and significantly improve the image visibility as well as correct the spectral bias to some degree. Moreover, this method is simple and feasible, and has good practical value.

Cloud detection is a crucial preprocessing step for optical satellite remote sensing (RS) images. This article focuses on the cloud detection for RS imagery with cloud-snow coexistence and the utilization of the satellite thumbnails that lose considerable amount of high resolution and spectrum information of original RS images to extract cloud mask efficiently. To tackle this problem, we propose a novel cloud detection neural network with an encoder-decoder structure, named CDnetV2, as a series work on cloud detection. Compared with our previous CDnetV1, CDnetV2 contains two novel modules, that is, adaptive feature fusing model (AFFM) and high-level semantic information guidance flows (HSIGFs). AFFM is used to fuse multilevel feature maps by three submodules: channel attention fusion model (CAFM), spatial attention fusion model (SAFM), and channel attention refinement model (CARM). HSIGFs are designed to make feature layers at decoder of CDnetV2 be aware of the locations of the cloud objects. The high-level semantic information of HSIGFs is extracted by a proposed high-level feature fusing model (HFFM). By being equipped with these two proposed key modules, AFFM and HSIGFs, CDnetV2 is able to fully utilize features extracted from encoder layers and yield accurate cloud detection results. Experimental results on the ZY-3 satellite thumbnail data set demonstrate that the proposed CDnetV2 achieves accurate detection accuracy and outperforms several state-of-the-art methods.

Multispectral remote sensing (RS) images are often contaminated by the haze that degrades the quality of RS data and reduces the accuracy of interpretation and classification. Recently, the emerging deep convolutional neural networks (CNNs) provide us new approaches for RS image dehazing. Unfortunately, the power of CNNs is limited by the lack of sufficient hazy-clean pairs of RS imagery, which makes supervised learning impractical. To meet the data hunger of supervised CNNs, we propose a novel haze synthesis method to generate realistic hazy multispectral images by modeling the wavelength-dependent and spatial-varying characteristics of haze in RS images. The proposed haze synthesis method not only alleviates the lack of realistic training pairs in multispectral RS image dehazing but also provides a benchmark data set for quantitative evaluation. Furthermore, we propose an end-to-end RSDehazeNet for haze removal. We utilize both local and global residual learning strategies in RSDehazeNet for fast convergence with superior performance. Channel attention modules are incorporated to exploit strong channel correlation in multispectral RS images. Experimental results show that the proposed network outperforms the state-of-the-art methods for synthetic data and real Landsat-8 OLI multispectral RS images.

Cloud detection is one of the important tasks for remote sensing image (RSI) preprocessing. In this paper, we utilize the thumbnail (i.e., preview image) of RSI, which contains the information of original multispectral or panchromatic imagery, to extract cloud mask efficiently. Compared with detection cloud mask from original RSI, it is more challenging to detect cloud mask using thumbnails due to the loss of resolution and spectrum information. To tackle this problem, we propose a cloud detection neural network (CDnet) with an encoder-decoder structure, a feature pyramid module (FPM), and a boundary refinement (BR) block. The FPM extracts the multiscale contextual information without the loss of resolution and coverage; the BR block refines object boundaries; and the encoder-decoder structure gradually recovers segmentation results with the same size as input image. Experimental results on the ZY-3 satellite thumbnails cloud cover validation data set and two other validation data sets (GF-1 WFV Cloud and Cloud Shadow Cover Validation Data and Landsat-8 Cloud Cover Assessment Validation Data) demonstrate that the proposed method achieves accurate detection accuracy and outperforms several state-of-the-art methods.