The Identification of Seasonal Coastline Changes From Landsat 8 Satellite Data Using Artificial Neural Networks and K-Nearest Neighbor

Abstract

Coastline boundaries are constantly changing due to natural or human-induced events that take place in the world. Therefore it is necessary to correctly observe coastline boundaries. Remote sensing is one of the most frequently used methods to monitor the changes in coastal areas. In this study, it is aimed to solve the problem of choosing the right method for coastal change observation. This paper introduces a spatial pixel-based and object-based image classification approach to recognize changing areas in coastline. The coastline boundary changes occurred in a part of Yamula Dam Lake in Kayseri province were examined using three multispectral Landsat 8 satellite images of March, August and November 2016. Firstly, imageto-image registration processing was performed to register the three satellite images. Then, each satellite image was classified into two information classes either 'Lake' and 'Other Field' by using pixel-based Artificial Neural Networks (ANNs) and object-based K-Nearest Neighbor (KNN) method. Classification accuracies for ANNs method were obtained 99.97%, 99.90% and 99.80% respectively in March, August and November. As for the accuracies of the classification for the KNN method, in March, August and November were obtained 99.99%, 99.93% and 99.92% respectively. The change images were formed for March-August and August-November pairs by using the obtained classification images. The post classification comparison method was used to determine the changes in coastline boundaries. At the end of the study, seasonal changes from water to land and from land to water were detected. According to the result of the changes there is a 5,67 km 2 increase from March to August and a 3,14 km 2 decrease from August to November in Yamula Dam Lake.

Mapping summer SUHI and its impact on the environment using GIS and Remote Sensing techniques: A case study on Municipality of Prishtina (Kosovo)

“…These bands allow obtaining various kinds of information on the characteristics of the land surface, including vegetation cover. Two spectral bands for infrared wavelength are collected by the TIRS instrument (Kesikoğlu et al, 2020). In previous sensors (TM and ETM +), these were covered by a single band: band 10 (TIRS 1, 10.6-11.19 μm) and band 11 (TIRS 2, 11.5-12.51 μm), both with 100 m spatial resolution (Table 2) (NASA, 2018).…”

Capital Optical Character Recognition Using Neural Network Based on Gaussian Filter

“…A neural network or artificial neural network is a set of algorithms used to imitate the workings of the human nervous system. Artificial neural networks [18] are one of the deep learning algorithms, which become more intelligent over time. Artificial neural networks [4], [13], [15], [19]- [22]] can learn on their own through experience and can become more accurate as more data is provided.…”

“…The concept of artificial neural networks began in the 1940s and 1950s, when researchers began to understand how the brain processes information by studying the structure and function of the brain. In 1943, Warren McCulloch and Walter Pitts published a paper proposing that neurons in the brain could be represented as simple logic gates that accept inputs and produce outputs based on a set of rules [7], [18], [21], [24], [29]. Announced.…”