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This paper aims to present a novel feature design that is able to precisely describe salient objects in images. With the development of space survey, sensor and information acquisition technologies, more complex objects appear in high-resolution remote sensing images. Traditional visual features are no longer precise enough to describe the images.

A novel remote sensing image retrieval method based on VSP (visual salient point) features is proposed in this paper. A key point detector and descriptor are used to extract the critical features and their descriptors in remote sensing images. A visual attention model is adopted to calculate the saliency map of the images, separating the salient regions from the background in the images. The key points in the salient regions are then extracted and defined as VSPs. The VSP features can then be constructed. The similarity between images is measured using the VSP features.

According to the experiment results, compared with traditional visual features, VSP features are more precise and stable in representing diverse remote sensing images. The proposed method performs better than the traditional methods in image retrieval precision.

This paper presents a novel remote sensing image retrieval method based on VSP features.

For some time past satellites have been orbiting the Earth and sending back images of it to ground stations. These remote‐sensing images are now accumulating in large numbers: present plans for satellite launches indicate that the growth rate will continue to rise further into the 1990s. In consequence, satellite remote‐sensing data are raising urgent questions concerning their efficient storage and rapid retrieval. The experience of handling of remote‐sensing images is likely to influence future developments in computer graphics information more generally. We review here the current position regarding these problems and the steps being taken to overcome them.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

This paper aims to present a novel approach of image super-resolution based on deep–shallow cascaded convolutional neural networks for reconstructing a clear and high-resolution (HR) remote sensing image from a low-resolution (LR) input.

The proposed approach directly learns the residuals and mapping between simulated LR and their corresponding HR remote sensing images based on deep and shallow end-to-end convolutional networks instead of assuming any specific restored models. Extra max-pooling and up-sampling are used to achieve a multiscale space by concatenating low- and high-level feature maps, and an HR image is generated by combining LR input and the residual image. This model ensures a strong response to spatially local input patterns by using a large filter and cascaded small filters. The authors adopt a strategy based on epochs to update the learning rate for boosting convergence speed.

The proposed deep network is trained to reconstruct high-quality images for low-quality inputs through a simulated dataset, which is generated with Set5, Set14, Berkeley Segmentation Data set and remote sensing images. Experimental results demonstrate that this model considerably enhances remote sensing images in terms of spatial detail and spectral fidelity and outperforms state-of-the-art SR methods in terms of peak signal-to-noise ratio, structural similarity and visual assessment.

The proposed method can reconstruct an HR remote sensing image from an LR input and significantly improve the quality of remote sensing images in terms of spatial detail and fidelity.

This paper aims to give an oversight of what is being done by researchers in GIS and remote sensing (field) to explore minerals. The main objective of this review is to explore how GIS and remote sensing have been beneficial in identifying mineral deposits for easier and cost-effective mining.

The approach of this research used Web of Science to generate a database of published articles on the application of GIS and remote sensing techniques for mineral exploration. The literature was further digested, noting the main findings, adopted method, illustration and research scales.

When applied alone, each technique seems effective, but it is important to know that combining different methods is more effective in identifying ore deposits.

This paper also examined and provided possible solutions to both current and future perspective issues relating to the application of GIS and remote sensing to mineral exploration. The authors believe that the conclusions and recommendations drawn from case studies and literature review will be of great importance to geoscientists and policymakers.

This paper aims to study the removal of wide-stripe noise in hyperspectral remote sensing images. There is a great deal of stripe noises in short-wave infrared hyperspectral remote sensing image, especially wide-stripe noise, which brings great challenge to the interpretation and application of hyperspectral images.

To remove the noise and to reduce the impact based on in-depth study of the mechanism of the stripe noise generation and its distribution characteristics, this paper proposed two statistical local processing and moment matching algorithms for the elimination of wide-stripe noise, namely, the gradient mean moment matching (GMMM) algorithm and the gradient interpolation moment matching (GIMM) algorithm.

The experiments were carried out with the practical short-wave infrared hyperspectral image data and good experiment results were obtained. Experiments show that both can reduce the impact of wide-stripe noise, and the filtering effect and the application range of the GIMM algorithm is better than that of the GMMM algorithm.

Using new methods to deal with the hyperspectral remote sensing images, it can effectively improve the quality of hyperspectral images and improve their utilization efficiency and value.

Describes the application of the Action Workflow approach to analyse a European project for monitoring agriculture from high resolution satellite images by using remote sensing techniques. The objective of this study was to improve and maintain co‐ordination between the organizations involved in the remote sensing process which evolved from a pilot phase and became operational. The study of the existing process was supported by the workflow product ActionWorkflow Analyst. Breakdowns and possible weaknesses identified in the previous process are reported. On the basis of a critical process review, the process is rationalized with an explicit visibility of customers, suppliers, organizational roles, activities and their communicative relationship. The re‐engineered process improves the service to customers in the European Commission (i.e. DG VI and Eurostat) and reduces transaction costs due to notably less customer/supplier interactions. Furthermore, there is a clear distinction between recurrent operations (done with more efficiency than in the actual process) and strategic services (with an added value to the management of ad hoc requests in the previous process). The newly designed business process has been prototyped using the workflow suite by Action Technologies. Evaluates the appropriateness of the ActionWorkflow approach and the systems ActionWorkflow Analyst, Builder and Manager for Lotus Notes.

The importance of thoroughly monitoring the state of the environment in Mediterranean ecosystems has long been recognised. With regard to the spatial extension of large areas threatened by various degradation processes it becomes obvious that terrestrial observation alone is hardly able to cope with this task. Remote sensing with air‐ or spaceborne sensor systems provides a comprehensive spatial coverage, is intrinsically synoptic, and collects objective, repetitive data and is thus ideally suited for monitoring environmentally sensitive areas. The major problem associated with its use is to quantitatively interpret a measured signal that has interacted with remote objects in terms of the properties of these objects. In parallel to the advances in remote sensing geographical information systems (GIS) have emerged as a fully functional support for resource management tasks. As an example for tracing and analysing environmental change with coupled remote sensing and GIS approaches we present a case study on the island of Crete which was carried out in the framework of research programmes supported by the European Union. Although it is known that grazing in Crete dramatically increased during the last two decades, it was not well understood how grazing pressure differs spatially and in how far it altered the landscape of Crete. One of the major rangeland areas of central Crete, the Psiloritis Mountains, have been selected to serve as a test site for answering these questions. On the basis of an extended Landsat‐TM and ‐MSS data set acquired between 1977 and 1996 it has been shown that time series analysis techniques based on vegetation fractions derived from spectral unmixing can substantiate a spatio‐temporal interpretation of degradation processes. In areas under massive grazing pressure such processes can be linked to the respective driving forces by GIS‐based analyses of natural and socio‐economic boundary conditions.

The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images.

First, the baseline network based on the U-net is trained under a large-scale dataset of remote sensing imagery. Then, the cross-modal training data are used to fine-tune the first two convolutional layers of the pre-trained network to achieve the adaptation to the local features of the cross-modal data. For the cross-modal data of different band, an autoencoder is designed to achieve data conversion and local feature extraction.

The experimental results show the effectiveness and practicability of the proposed method. Compared with the ordinary method, the proposed method gets much better metrics.

The originality is the transfer learning strategy that fine-tunes the low-level layers for the cross-modal data application. The proposed method can achieve satisfied road segmentation with a small amount of cross-modal training data, so that is has a good application value. Still, for the similar application of cross-modal data, the idea provided by this paper is helpful.

The purpose of this research is to produce the landslide susceptibility map of Fraser's Hill and its surroundings in Pahang (Malaysia), utilizing remote sensing data and Geographic Information System (GIS) as a way to monitor sustainable highland development.

Ancillary data are collected, processed, and constructed into a spatial database in a GIS platform to produce the satellite image. The factors chosen that influence landslide occurrence are land cover, vegetation index (NDVI), precipitation, and geology. Landslide‐hazardous areas are analyzed and mapped using the landslide‐occurrence factors through the heuristic approach Analytic Hierarchy Process (AHP).

It is demonstrated that the integration of remote sensing data and GIS database is of assistance in managing land‐use planning of sustainable development. The verification with the existing landslides record shows a noteworthy accuracy.

The list of data/maps reflects a considerable understanding of the basic cartographic information that is needed to effectively deal with the landslide problem.

This approach indicates a potential long‐term application of remote sensing and GIS in managing sustainable highland development by monitoring the hazard‐susceptibility area.

The value of the work is in its integration and utilization of remote sensing and GIS to provide sustainable development which can be developed to aid landslide warning systems.