Disaster relief, police work, and environmental monitoring all benefit from satelliteimages. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that ne...
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Disaster relief, police work, and environmental monitoring all benefit from satelliteimages. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satelliteimages. The results of this classifier model are better than those of existing approaches.
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