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Open AccessJournal ArticleDOI

Object based image analysis for remote sensing

TLDR
This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way.
Abstract
Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.

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Citations
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Journal ArticleDOI

Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art

TL;DR: A general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks.
Posted ContentDOI

System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

TL;DR: The wide spectrum of scientific applications of SAGA is highlighted in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.
Journal ArticleDOI

Remote Sensing Image Scene Classification: Benchmark and State of the Art

TL;DR: A large-scale data set, termed “NWPU-RESISC45,” is proposed, which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU).
Journal ArticleDOI

Global land cover mapping at 30 m resolution: A POK-based operational approach

TL;DR: In this article, an approach based on the integration of pixel-and object-based methods with knowledge (POK-based) has been developed to handle the classification process of 10 land cover types, i.e., firstly each class identified in a prioritized sequence and then results are merged together.
Journal ArticleDOI

Geographic Object-Based Image Analysis - Towards a new paradigm.

TL;DR: In this paper, the authors discuss the limitations of prevailing per-pixel methods when applied to high-resolution images and explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition.
References
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Book

The Structure of Scientific Revolutions

TL;DR: The Structure of Scientific Revolutions as discussed by the authors is a seminal work in the history of science and philosophy of science, and it has been widely cited as a major source of inspiration for the present generation of scientists.
Journal ArticleDOI

A review on image segmentation techniques

TL;DR: Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches, which addresses the issue of quantitative evaluation of segmentation results.
Journal ArticleDOI

A survey of image classification methods and techniques for improving classification performance

TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Journal ArticleDOI

Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

TL;DR: In this article, an object-oriented image analysis software, eCognition, is proposed to integrate remote sensing imagery and GIS for mapping, environmental monitoring, disaster management and civil and military intelligence.