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In order to maximally make use of the limited capacities of storage and transmission, it's necessary for onboard computer to adaptively compress the cloud regions with lower quality compared with the regions without cloud cover. Therefore, the information processing unit needs to recognize the cloud regions before compress the image. To meet this requirement of satellite imaging payload, a novel approach...
Understanding a scene provided by very high resolution (VHR) satellite imagery has become a more and more challenging problem. In this paper, we propose a new method for scene classification based on saliency computing of patches sampling from the VHR images. Sparse principal component analysis (sPCA) is then adopted to select the corresponding informative salient patches for image scene representation...
Understanding the scenes provided by very high resolution satellite (VHR) imagery has become a critical task. In this letter, we propose a new informative feature selection method for VHR scene classification. First, scale-invariant feature transform and speeded up robust feature operators are used to extract local features from the original VHR images to construct a visual dictionary. A sparse principal...
Most of the existing methods for generating a visual dictionary SIFT based on local characteristics, and adopt the common K-means clustering method to get the visual dictionary. But when the image vector dimension of the local feature is growing higher, the vector distribution of the local characteristics becomes sparse, resulting in the high correlation distance between the image vectors and reducing...
This paper focuses on road sign classification for creating accurate and up-to-date inventories of traffic signs, which is important for road safety and maintenance. This is a challenging multi-class classification task, as a large number of different sign types exist which only differ in minor details. Moreover, changes in viewpoint, capturing conditions and partial occlusions result in large intra-class...
We present an efficient image categorization and retrieval system applied to medical image databases, in particular large radiograph archives. The methodology presented is based on local patch representation of the image content and a bag-of-features approach for defining image categories, with a kernel based SVM classifier. In a recent international competition the system was ranked as one of the...
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