Pixel and texture based classification using a well-defined and efficient architecture is considered as a major challenge. Nowadays, a large number of satellite images are received within a fraction of seconds, howeve...
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Pixel and texture based classification using a well-defined and efficient architecture is considered as a major challenge. Nowadays, a large number of satellite images are received within a fraction of seconds, however processing such images to identify the land cover and land use is considered as a tedious process. To achieve this objective with high accuracy, an algorithm of cellular automata (ACA) is introduced in this proposed approach. The pixel-based classification is carried out with parallelepiped and maximum likelihood classifier, whereas the texture-based classification is accomplished using softmaxregression (SR) classifier. By incorporating ACA, the accuracy of these classification techniques is improved and the performance is then evaluated. This overall classification process is performed to understand the land cover and land use of Kerala. The classification accuracy attained using ACA-based parallelepiped, maximum likelihood and SR is found higher than classical parallelepiped, maximum likelihood, and SR algorithms. The final result reveals that the texture-based ACA classification provides a higher classification accuracy rate (96.8%) than the pixel-based ACA classification (90.98%).
Localization based on channel state information (CSI) fingerprints in multiple-input multiple-output (MIMO) systems is one of the most promising positioning technologies. In this letter, a machine learning approach is...
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Localization based on channel state information (CSI) fingerprints in multiple-input multiple-output (MIMO) systems is one of the most promising positioning technologies. In this letter, a machine learning approach is developed for hierarchical localization exploiting multipath MIMO-fingerprints. By using multipath MIMO CSI in the time domain, instead in the frequency domain, our hierarchical scheme uses coarse-to-fine process and can achieve accurate positioning, which is demonstrated by simulation results.
Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clus...
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Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clustering ensemble to capture the whole target shape. In order to further identify clouds, snow-covered lands, and bright buildings on remote-sensing images, they first implement an Ostu threshold to get high grey-level sub-regions, and then extract the descriptors of these sub-regions and put them into the softmax regression classifier. Regarding these methods, the authors conduct experiments using GF-1 remote-sensing images. The results demonstrate the effectiveness and excellency of their proposed method.
This paper proposes a method based on the bag-of-words (BoW) and the softmaxregression for microscopic image classification. Essentially, the locality-constrained linear coding (LLC) is adopted for local feature enco...
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ISBN:
(纸本)9781467368537
This paper proposes a method based on the bag-of-words (BoW) and the softmaxregression for microscopic image classification. Essentially, the locality-constrained linear coding (LLC) is adopted for local feature encoding. Compared with the traditionally adopted vector quantization (VQ) in the BoW framework, the LLC encodes local structures of microscopic images with lower quantization errors and generates a sparse image representation. This enables the use of linear classifiers with low computational complexity. A softmax regression classifier is then adopted to address the multi-categorical classification task where the confidence of categorical prediction is quantified by posterior probabilities. Compared with other linear classifiers (such as the linear SVM) which only assign labels to images, such probabilistic outputs provide extra quantitative information to analyze misclassified images. Our experiments on the 2D-Hela and the PAP smear data sets show significant performance improvement of the proposed method comparing with competing methods using different features and classifiers under the BoW framework.
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