In many scene classification applications, the variety of surface objects, high within-category diversity and between-category similarity carry challenges for the classification Framework. Most of CNN-based classification methods only extract image features from a single network layer, which may cause the completed image information difficult to extract in complex scenes. We propose a novel transfer deep combined convolutional activations (TDCCA) to integrate both the low-level and high-level features. Extensive comparative experiments are conducted on UC Merced database, Aerial Image database and NWPU-RESISC45 database. The results reveal that our proposed TDCCA achieves higher experimental accuracies than other up-to-date popular methods.
Unmanned aerial vehicles have been widely used in military and civil areas, which requires vision processing in explicit usage scenario. Existence of haze or fog can influence the context awareness capability of the aerial vehicles and makes affectation on target tasks. The captured images in hazy scenes suffer from degradation problems including poor contrast, color distortion, incomplete information, which lead to many difficulties in the follow-up processing. A simple and effective single image dehazing algorithm based on atmospheric scattering model and the optimum of image quality evaluation is proposed in this paper. Three image quality evaluation parameters: image entropy, standard deviation, and Fourier amplitude are combined to establish and the image quality evaluation function. On the basis of quality evaluation function, the image with the optimum of quality evaluation among the potential defogging images is chosen as the best result. Results show that this method has lower computational complexity, simplified operations and improved real-time performance.
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