LEE- Uydu Haberleşme ve Uzaktan Algılama Lisansüstü Programı

Bu topluluk için Kalıcı Uri

http://hdl.handle.net/11527/19486

Gözat

Konu "defense" ile LEE- Uydu Haberleşme ve Uzaktan Algılama Lisansüstü Programı'a göz atma
  • Öge
    Aircraft detection using deep learning
    (Graduate School, 2022) Mutlu, Utku ; Pınar Kent, Sedef ; 732793 ; Satellite Communication and Remote Sensing Programme
    For many years, it has been thought whether computers can think like humans and perform these intellectual tasks autonomously. Artificial intelligence studies were started on this idea and today, studies are carried out using this technology. Both machine learning and deep learning are subsets of artificial intelligence and deep learning is actually subset of machine learning. Deep learning is used in the development of technologies such as image recognition, virtual assistant, natural language processing, speech recognition, autonomous and robotic systems. Deep learning applications have been developed for years by using remote sensing images. Deep learning algorithms in remote sensing images are frequently used for the detection of objects such as aircraft, ships, buildings and other similar things for civil and military purposes. Owing to the development of high-performance hardware, the ease of access to big data and the rapid development of deep learning algorithms, progression of new projects have been satisfied with less time and lower cost. Remote sensing is the science of obtaining information about an area without being in contact with it. Remote sensing devices consist of sensor systems in satellites and aircraft. In 1858, the earliest aerial photo was acquired through a hot air balloon attached to the ground with one or more tether. In the early 1900s, aerial photographs were taken with cameras mounted on pigeons, and in 1909, aerial images were obtained with cameras mounted on airplanes for the first time in order to view larger areas. The term "remote sensing" was used for the first time in the 1950s. Remote sensing satellites provide information about the atmosphere, ocean, and land. As a result of the development of satellite sensors that can detect different parameters, the use of remote sensing images has become widespread in more comprehensive projects. The main areas where remote sensing is used are defense, agriculture, aviation, forestry, biodiversity and surface changes. Deep learning is a subset of the machine learning algorithm in artificial intelligence, emulating the working human brain as it processes data and creates patterns for use in decision making. In 1943, the first mathematical model of a neural network that imitates the thought process of the human brain was created by Walter Pitts and Warren McCulloch. An algorithm using a two-layer neural network for pattern recognition was developed by Frank Rosenblatt, and the first perceptron was presented in 1957. Alexey Ivakhnenko and V.G. Lapa published the first working neural network for supervised learning in 1965. Alexey Ivakhnenko described the 8-layer deep learning network in his publication in 1971. Artificial intelligence studies were interrupted between 1974 and 1980 due to the lack of hardware with sufficient processing power and memory to train multilayer networks. Neocognitron, a multilayer artificial neural network, was developed by Kunihiko Fukushima in 1980. The term "deep learning" was first used by Rina Dechter in 1986. Mike Schuster and Kuldip Paliwal introduced bidirectional recurrent neural networks in 1997, which connects two hidden layers, one for the positive time direction and the other for the negative time direction, to the same output. Fei-Fei Li started working on the ImageNet idea in 2006 because of the need for a large amount of labeled images for supervised learning. In 2009, Fei-Fei Li introduced ImageNet that is a database of a large quantity of labeled images.