Figure 9 - uploaded by Wei Xia
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Query by example: looking for "BAR2BUI" (bare land to Beijing airport) in a database of remote sensing images. (a) The target pair of query images. (b) Pairs of images retrieved using the proposed method based on Color Moments. The top 12 most similar pairs of images are shown.
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With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from ma...
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Context 1
... order of the retrieval of the image pairs was as shown in Figure 8(b). Figure 9 shows a typical query pair of "BAR2BUI" images (Figure 9(a)) and the corresponding images retrieved using the proposed method based on Color Moments. From these figures, it can be seen that, using multiple features, the new method produces promising results. ...
Context 2
... order of the retrieval of the image pairs was as shown in Figure 8(b). Figure 9 shows a typical query pair of "BAR2BUI" images (Figure 9(a)) and the corresponding images retrieved using the proposed method based on Color Moments. From these figures, it can be seen that, using multiple features, the new method produces promising results. ...
Context 3
... order of the retrieval of the image pairs was as shown in Figure 8(b). Figure 9 shows a typical query pair of "BAR2BUI" images (Figure 9(a)) and the corresponding images retrieved using the proposed method based on Color Moments. From these figures, it can be seen that, using multiple features, the new method produces promising results. ...
Context 4
... order of the retrieval of the image pairs was as shown in Figure 8(b). Figure 9 shows a typical query pair of "BAR2BUI" images (Figure 9(a)) and the corresponding images retrieved using the proposed method based on Color Moments. From these figures, it can be seen that, using multiple features, the new method produces promising results. ...
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With the rapid development of remote sensing technologies, the frequency of observations of the same location is increasing, and many satellites and sensors produced a large amount of time series images. These images make long-term change detection and dynamic characteristic estimation of ground features possible. However, conventional remote sensi...
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Considering the multiscale characteristics of the human visual system and any natural scene, the spatial autocorrelation of remotely sensed imagery, and the multilevel spatial structure of ground targets in remote sensing images, an information-measurement approach based on a single-level geometrical mapping model can only reflect partial feature information at a single level (e.g., global statistical information and local spatial distribution information). The single mapping model cannot validly characterize the information of the multilevel and multiscale features of the spatial structures inherent in remotely sensed images. Additionally, the validity, practicability, and application range of the results of single-level mapping models are greatly limited in practical applications. In this paper, we present the multilevel geometrical mapping entropy (MGME) model to evaluate the information content of related attribute characteristics contained in remotely sensed images. Subsequently, experimental images with different types of objects, including reservoir area, farmland, water area (i.e., water and trees), and mountain area, were used to validate the performance of the proposed method. Experimental results show that the proposed method can not only reflect the difference in the information of images in terms of spectrum features, spatial structural features, and visual perception but also eliminates the inadequacy of a single-level mapping model. That is, the multilevel mapping strategy is feasible and valid. Additionally, the vector set of the MGME method and its standard deviation (Std) value can be used to further explore and study the spatial dependence of ground scenes and the difference in the spatial structural characteristics of different objects.
With the emergence of huge volumes of high-resolution Hyperspectral Images (HSI)
produced by different types of imaging sensors, analyzing and retrieving these images require
effective image description and quantification techniques. Compared to remote sensing RGB images,
HSI data contain hundreds of spectral bands (varying from the visible to the infrared ranges) allowing
profile materials and organisms that only hyperspectral sensors can provide. In this article, we study
the importance of spectral sensitivity functions in constructing discriminative representation of
hyperspectral images. The main goal of such representation is to improve image content recognition
by focusing the processing on only the most relevant spectral channels. The underlying hypothesis
is that for a given category, the content of each image is better extracted through a specific set of
spectral sensitivity functions. Those spectral sensitivity functions are evaluated in a Content-Based
Image Retrieval (CBIR) framework. In this work, we propose a new HSI dataset for the remote
sensing community, specifically designed for Hyperspectral remote sensing retrieval and classification.
Exhaustive experiments have been conducted on this dataset and on a literature dataset. Obtained
retrieval results prove that the physical measurements and optical properties of the scene contained
in the HSI contribute in an accurate image content description than the information provided by the
RGB image presentation
Rapid urbanization and economic development have led to the development of heavy industry and structural re-equalization in mainland China. This has resulted in scattered and disorderly layouts becoming prominent in the region. Furthermore, economic development has exacerbated pressures on regional resources and the environment and has threatened sustainable and coordinated development in the region. The NASA Land Science Investigator Processing System (Land-SIPS) Visible Infrared Imaging Radiometer (VIIRS) 375-m active fire product (VNP14IMG) was selected from the Fire Information for Resource Management System (FIRMS) to study the spatiotemporal patterns of heavy industry development. Furthermore, we employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-order VNP14IMG and constructed heat source objects. Lastly, we used a threshold recognition model to identify heavy industry objects from normal heat source objects. Results suggest that the method is an accurate and effective way to monitor heat sources generated from heavy industry. Moreover, some conclusions about heavy industrial heat source distribution in mainland China at different scales were obtained. Those can be beneficial for policy-makers and heavy industry regulation.
