Information extraction from very high resolution satellite imagery over Lukole refugee camp, Tanzania

Giada, S.; Groeve, Tom De; Ehrlich, Daniele; Soille, Pierre

doi:10.1080/0143116021000035021

Cited by 111 publications

(56 citation statements)

References 11 publications

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“…Properties such as size, shape, texture, relationship with neighboring objects [113], as well as various combinations of these properties are used to aid the object extraction process [114,115]. OBIA has also been applied at multiple scales since it has been found to be useful in extracting semantically significant regions in remote sensing imagery [116].…”

Section: Object-based Image Analysismentioning

confidence: 99%

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

et al. 2018

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Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High-and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.

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Section: Object-based Image Analysismentioning

confidence: 99%

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

et al. 2018

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“…The implemented method detects destruction in a bitemporal image subset of a conflict area and builds on previous work [37,38]. The application of remote sensing for monitoring of human rights issues has been explored in a number of studies, and GEOBIA has proven to be a promising tool, e.g., for refugee camp monitoring [39,40] or damage assessment [41]. A comprehensive review of research and applications in this field (pixel-as well as object-based analyses) can be found in [42].…”

Section: Contribution and Overviewmentioning

confidence: 99%

Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers

Knoth

Nüst

2017

Remote Sensing

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Geographic Object-Based Image Analysis (GEOBIA) mostly uses proprietary software, but the interest in Free and Open-Source Software (FOSS) for GEOBIA is growing. This interest stems not only from cost savings, but also from benefits concerning reproducibility and collaboration. Technical challenges hamper practical reproducibility, especially when multiple software packages are required to conduct an analysis. In this study, we use containerization to package a GEOBIA workflow in a well-defined FOSS environment. We explore the approach using two software stacks to perform an exemplary analysis detecting destruction of buildings in bi-temporal images of a conflict area. The analysis combines feature extraction techniques with segmentation and object-based analysis to detect changes using automatically-defined local reference values and to distinguish disappeared buildings from non-target structures. The resulting workflow is published as FOSS comprising both the model and data in a ready to use Docker image and a user interface for interaction with the containerized workflow. The presented solution advances GEOBIA in the following aspects: higher transparency of methodology; easier reuse and adaption of workflows; better transferability between operating systems; complete description of the software environment; and easy application of workflows by image analysis experts and non-experts. As a result, it promotes not only the reproducibility of GEOBIA, but also its practical adoption.

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“…Mathematical morphology has been also used to detect objects of interest. Soille and Pesaresi in [24], [25] developed a method to extract roads. It consists in two stages: a pre-processing one, in order to remove noise from the image, and a processing phase in which a structuring element is defined according to the shape of the object to be extracted.…”

Section: Boykov and Jolly Proposed An Interactive Graph Cuts (Igc)mentioning

confidence: 99%

A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

Idrissi¹,

Merabet²,

Ruichek³

et al. 2015

ijacsa

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Abstract-In this paper, an efficient method for automatic and accurate detection of multiple objects from images using a region similarity measure is presented. This method involves the construction of two knowledge databases: The first one contains several distinctive textures of objects to be extracted. The second one is composed with textures representing background. Both databases are provided by some examples (training set) of images from which one wants to recognize objects. The proposed procedure starts by an initialization step during which the studied image is segmented into homogeneous regions. In order to separate the objects of interest from the image background, an evaluation of the similarity between the regions of the segmented image and those of the constructed knowledge databases is then performed. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. Experimental results obtained from the method applied to extract building roofs from orthophotoplans prove its robustness and performance over popular methods like K Nearest Neighbours (KNN) and Support Vector Machine (SVM).

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Information extraction from very high resolution satellite imagery over Lukole refugee camp, Tanzania

Cited by 111 publications

References 11 publications

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers

A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

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