Band selection for hyperspectral images helps improve the efficiency of data processing and even the accuracy of classification. It is to reduce the dimensionality of a hyperspectral image by selecting representative bands. In such a process, the quantification of band similarity is the fundamental issue, and it is usually achieved by using an information-theoretic measure, such as mutual information or relative entropy. However, these measures are incapable of quantifying similarity in terms of both composition and configuration. To solve this problem, the Boltzmann entropy (BE), which captures both configurational and compositional information, is employed in this letter. More precisely, the difference in BE between two bands is used for such quantification. The corresponding search strategy is designed for band selection. Experimental evaluation was carried out using remote sensing images for classification. The results clearly demonstrate the superiority of the proposed band selection method over traditional information-theoretic methods: an increase of up to 27% in classification accuracy was observed when using the difference in relative BE with 20 selected bands. In addition, another comparison with some state-of-the-art methods was conducted. The results show that the proposed method is still very competitive; it outperformed all the others when the number of selected bands ranges from 18 to 23. This letter, the first of its kind, reveals that the BE may form a new base for information-theoretic approaches to image processing and even for spatial information science in a broad sense.

Quick and accurate building damage assessment following a disaster is critical to making a preliminary estimate of losses. Remote sensing image analysis based on convolutional neural network and its relatives has shown a growing potential in this task, but faces the challenge of collecting dense pixel-level annotations. In this letter, we propose a novel weakly supervised semantic segmentation (WSSS) method based on image-level labels for pixel-wise damaged building extraction from post-earthquake high resolution remote sensing (HRRS) images. The proposed method aims to improve the quality of the class activation map (CAM) to boost model performance. To be specific, a multi-scale dependence (MSD) module and a spatial correlation refinement (SCR) module are designed by considering the special characteristics of damaged building, and are integrated into an encoder-decoder network. The former is used for complete and dense localization of damaged buildings in CAM, and the latter contributes to noise suppression. Extensive experimental evaluations over three datasets are conducted to confirm the effectives of the proposed approach. Both generated CAMs and extracted damaged buildings results of our methods are better than that of current state-of-the-art methods.

Coronavirus disease 2019 (COVID-19) has emerged as a growing focus of global attention and a critical factor in public-health decision making. Towards fighting the COVID-19 outbreak, countries worldwide and international organisations have taken various actions, including promoting the transparency of and public access to disease data. In such public communications, maps have played an important role in that a map is worth a thousand words. Most of these have taken the form of a choropleth map. Here, we propose employing cartograms to visualise both the expansion and spread of COVID-19. We designed a combination of six circular cartograms containing the data of confirmed cases every 48 hours from 24 January to 3 February 2020. Such a design conveys both spatial and temporal information more intuitively and efficiently, so it can be expected to facilitate better public participation in the fight against COVID-19.

Weakly supervised semantic segmentation (WSSS) methods based on image-level labels can relieve the tedious pixel-level annotation burden, and these methods are mainly based on class activation maps (CAMs). However, it is challenging to generate high-quality CAMs for high-resolution remotely sensed imagery (HRSI). In this article, we propose a WSSS method for building extraction from HRSI using image-level labels. The proposed method, termed as the MSG-SR-Net, integrates two novel modules, i.e., multiscale generation (MSG) and superpixel refinement (SR), to obtain high-quality CAMs so as to provide reliable pixel-level training samples for subsequent semantic segmentation steps. The MSG module is proposed to use global semantic information to guide the learning of multiple features across different levels, and then, respectively, to utilize multilevel features for generating multiscale CAMs. This component can effectively suppress the interference of the class-irrelevant noise and strengthen the use of profitable information in multilevel features. The SR module is designed to take advantage of superpixels to improve multiscale CAMs in target integrity and details preserving. Extensive experiments on two public building datasets demonstrated that the proposed modules made the MSG-SR-Net obtain more integral and accurate CAMs for building extraction. Moreover, experimental results also showed the proposed method achieved excellent performance with over 67% in F1-score, and outperformed other weakly supervised methods in effectiveness and generalization ability.

The classification of very-high-resolution (VHR) remote sensing images is essential in many applications. However, high intraclass and low interclass variations in these kinds of images pose serious challenges. Fully convolutional network (FCN) models, which benefit from a powerful feature learning ability, have shown impressive performance and great potential. Nevertheless, only classification results with coarse resolution can be obtained from the original FCN method. Deep feature fusion is often employed to improve the resolution of outputs. Existing strategies for such fusion are not capable of properly utilizing the low-level features and considering the importance of features at different scales. This paper proposes a novel, end-to-end, fully convolutional network to integrate a multiconnection ResNet model and a class-specific attention model into a unified framework to overcome these problems. The former fuses multilevel deep features without introducing any redundant information from low-level features. The latter can learn the contributions from different features of each geo-object at each scale. Extensive experiments on two open datasets indicate that the proposed method can achieve class-specific scale-adaptive classification results and it outperforms other state-of-the-art methods. The results were submitted to the International Society for Photogrammetry and Remote Sensing (ISPRS) online contest for comparison with more than 50 other methods. The results indicate that the proposed method (ID: SWJ_2) ranks #1 in terms of overall accuracy, even though no additional digital surface model (DSM) data that were offered by ISPRS were used and no postprocessing was applied.