Improved feature extraction from high-resolution remotely sensed imagery using object geometry

Momm, Henrique G.; Gunter, Bryan; Easson, Greg

doi:10.1117/12.850196

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…More studies apply an unsupervised approach, which might be due to their higher degree of automation. The supervised approaches taken from machine learning suffer from their extensive input requirements, such as the definition of features with corresponding object descriptors, labeling of objects, training a classifier and applying the trained classifier on test data [86,147]. Furthermore, the ability of machine learning approaches to classify an image into categories of different labels is not necessarily required in the scope of this workflow step, since the image only needs to be segmented.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

et al. 2016

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Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step-including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

et al. 2016

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“…More studies apply an unsupervised approach, which might be due to their higher degree of automation. The supervised approaches taken from machine learning suffer from their extensive input requirements, such as the definition of features with corresponding object descriptors, labeling of objects, training a classifier and applying the trained classifier on test data [94,157]. Furthermore, the ability of machine learning approaches to classify an image into categories of different labels is not necessarily required in the scope of this workflow step since the image only needs to be segmented.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Automating image-based cadastral boundary mapping

Crommelinck¹

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Unmanned aerial vehicles (UAV), also known as drones, Unmanned Aerial Systems (UAS), or Remotely Piloted Aircraft Systems (RPAS) are small aircraft 1.1.4 Boundary Delineation for UAV-based Cadastral Mapping In all case studies, cadastral boundaries are manually detected and delineated from orthoimages. This is realized either in an office with a small group of involved stakeholders for one parcel or in a community mapping approach for several parcels at once. All case studies lack an automatic approach to extract boundary features from the UAV data. An automatic or semi-automatic feature Objective (i) is addressed in chapters 1 and 3, objective (ii) in chapters 4, 5 and 6, and objective (iii) in chapters 7 and 8. The dissertation is structured as follows: Chapter 1 introduces the Ph.D. research. We describe the research gap to be addressed and formulate corresponding research objectives. Chapter 2 provides contextual information on the Ph.D. research. This is done by reviewing approaches for feature extraction from various application fields. These are synthesized into a hypothetical workflow applicable for automated boundary delineation from UAV data. The workflow consists of image segmentation, line extraction, and contour generation. Chapter 3 investigates which method performs best for image segmentation, which is the first step of the hypothetical workflow proposed in chapter 3. This is done by analyzing the transferability of gPb contour detection, a state-ofthe-art computer vision method, to UAV-based cadastral mapping. Chapter 4 investigates which method performs best for line extraction, which is the second step of the hypothetical workflow proposed in chapter 3. This is done by analyzing a superpixel approach, namely simple linear iterative * This chapter is based on: 44.

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Object-oriented approach of information extraction from panchromatic satellite images based on fuzzy logic

Gupta¹,

Bhadauria²

2014

2014 5th International Conference - Confluence the Next Generation Information Technology Summit (Confluence)

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This paper presents a fuzzy inference based object oriented classification approach for information extraction from panchromatic satellite images. The main problem concerned with high resolution satellite imagery is diverse composition of spectral resolution in urban mapping. Information extraction form these high resolution images are problematic due to discriminated object boundaries in the land use as well as the multiple resemblances of the different class segments. Satisfying results cannot be obtained with the use of traditional pixel based classifiers which rely on per pixel value analysis. This paper demonstrated the concept of fuzzy logic based object-oriented approach, in order to deal with this problematic situation. The concept of this approach is to determine a suitable threshold of the parameters adopted in the object oriented approach. Establishment of fuzzy membership functions for extraction of various surface objects such as bare soil, roads, water, vegetation, building etc using the provided built-in tools of eCognition software, is the main focus of this study. In this proposed approach, fuzzy logic (rules) is defined for every class based on the specifications mentioned in class hierarchy. Using these fuzzy rules each image object is tested against specifications. Each segment receives a membership value (degree) which acts as class representative. After deffuzification of the results every class is assigned associated segments with high similarity degree, using eCognition software. Improved overall accuracy of the presented system, making this approach feasible and efficient for extracting information from high resolution remotely sensed data.

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Improved feature extraction from high-resolution remotely sensed imagery using object geometry

Cited by 5 publications

References 7 publications

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

Automating image-based cadastral boundary mapping

Object-oriented approach of information extraction from panchromatic satellite images based on fuzzy logic

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