Mangrove ecosystems play a very important ecological role on land?ocean interfaces in tropical regions. These ecosystems comprise of various tree species and aquatic animals, protecting the environment and providing a...
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Mangrove ecosystems play a very important ecological role on land?ocean interfaces in tropical regions. These ecosystems comprise of various tree species and aquatic animals, protecting the environment and providing a habitat that supports many living organisms including humans. The identification of image regions in mangrove ecosystems plays a significant role in ecosystem monitoring and conservation. Recent studies have suggested oversegmentation of colour images using superpixels as a solution to the segmentation of image regions. This study used the SLIC superpixel algorithm and k-means clustering to segment images taken from a camera mounted on a drone from a mangrove ecosystem in Fiji. The SLIC superpixel algorithm performed well to demarcate image regions with similar colour and texture information into patches and to use k-means for the segmentation of the whole image. These results lend support to the use of superpixel algorithms for the segmentation of mangrove ecosystems. Understanding how superpixels can be used for the segmentation of drone images will assist conservation efforts in mangrove ecosystems.
Compressive spectral imaging (CSI) acquires coded projections of a spectral image by performing a modulation of the data cube followed by a spectral-wise integration. To avoid the spectral image reconstruction procedu...
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ISBN:
(纸本)9781538662496
Compressive spectral imaging (CSI) acquires coded projections of a spectral image by performing a modulation of the data cube followed by a spectral-wise integration. To avoid the spectral image reconstruction procedure, this paper proposes a classification approach that extracts features directly from multi-sensor CSI measurements. Particularly, the proposed method obtains the features by considering the spectral information extracted from Hyperspectral CSI measurements, and the local spatial information extracted by clustering the Multispectral CSI measurements using a superpixel algorithm. This approach is evaluated on Pavia University and Salinas Valley datasets. Extensive simulations show that considering the local spatial information boosts the overall accuracy up to 3% in comparison with traditional approaches that only uses the spectral information. Furthermore, the computation time of the approach that reconstructs, fuses and classifies takes approximately 87:43 [s], while classifying directly from multi-sensor compressive measurements takes only 0:74 [s], achieving similar classification results.
作者：
Wang, NingChen, FangYu, BoWang, LeiChinese Acad Sci
Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 Dengzhuang South Rd Beijing 100094 Peoples R China Univ Chinese Acad Sci
Beijing 100049 Peoples R China Chinese Acad Sci
Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Chinese Acad Sci
Aerosp Informat Res Inst Hainan Key Lab Earth Observat Sanya 572029 Peoples R China
superpixel segmentation algorithms are widely used in the image processing field. The size of the large-scale images usually exceeds the memory of a single machine given that the size of image data has increased rapid...
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superpixel segmentation algorithms are widely used in the image processing field. The size of the large-scale images usually exceeds the memory of a single machine given that the size of image data has increased rapidly in recent years. This leads to big challenges for implementing sequential superpixel segmentation methods, although these algorithms have good scalability. Additionally, segmentation of large-scale images over a distributed cluster is a feasible solution. Nevertheless, it is challenging to transplant sequential superpixel algorithms directly to a distributed environment, as usually there are incomplete object problems in the border area of image tiles. To overcome the incomplete object problems, one approach is to build a distributed strategy based on a sequential SLIC superpixel segmentation algorithm over a distributed cluster organized by Apache Spark. In our research, the decomposed image tiles were divided into two categories-even tiles and odd tiles. The even tiles were first segmented by the SLIC algorithm, then the cluster centers and buffer sizes of even tiles were extracted and switched to odd tiles. During the shuffle stage, the odd tiles acquired pixels from adjacent even tiles according to the buffer sizes, and then the buffered odd tiles were segmented by the SLIC algorithm with the help of the shared cluster centers. The superpixels with shared cluster centers were generated in even tiles and remained in order to enlarge the odd tiles rather than redundant computing of specific areas to modify incomplete superpixels well. Specifically, this strategy employs the shared variables to transmit intermediate results and the shuffle operations were carried out among approximately half of the entire image tiles, which reduces the communications further. The distributed strategy was evaluated in terms of the accuracy and execution efficiency, which revealed that the proposed strategy could not only get better F-measure values but is also implem
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