Scene Semantic Understanding Based on the Spatial Context Relations of Multiple Objects

Zhong, Yanfei; Wu, Siqi; Zhao, Binghai

doi:10.3390/rs9101030

Cited by 20 publications

(6 citation statements)

References 34 publications

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“…Due to the rich and structural information offered by the continually growing spatial resolution, spectral and spectral-spatial characteristics have difficulty explaining the contextual information included in the pictures. [71], [72], [73], [74].…”

Section: S=linespace (θMinθmaxn) (31)mentioning

confidence: 99%

Remote sensing Image processing, DEEP learning Classification algorithms survey and review

Amer,

Rahouma,

Shahin

2024

Journal of Advanced Engineering Trends

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Image processing is considered a key in remote sensing fields. It is very wide and has many branches. One of the major and widely used techniques in image processing is image classification. Image classification is used to classify the extracted features from digital images into different classes based on different characteristics. Machine learning and deep learning are two main techniques to automate the processes of image processing and classification. This paper presents a survey of the recently used classification techniques using deep learning. This paper aims to provide a review of the newly presented theories and tools used in deep learning for remote sensing and satellite images from 2018 to 2022. Deep learning, as a subset of machine learning, needs high-processing devices like microprocessors, Field Programmable Gate Array (FPGA), and Graphics Processing Units (GPUs). Due to the progress in GPUs and other processors, it has become much easier to deal with deep learning (DL) for BIG DATA and satellite images.

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Section: S=linespace (θMinθmaxn) (31)mentioning

confidence: 99%

Remote sensing Image processing, DEEP learning Classification algorithms survey and review

Amer,

Rahouma,

Shahin

2024

Journal of Advanced Engineering Trends

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“…In order to fully understand the components and their relations in the scene, Yanfei Zhong et al [79] presented a bottom-up scene understanding framework based on a context relationship model of multiply spatial objects, which combined the symbiotic and positional relationships at the object level. Moreover, focusing on the contextual relations in the messy indoor scene, Wentong Liao et al [80] proposed a framework for the automatic generation of semantic scene graphs, which deduced reasonable support relations based on physical constraints and prior knowledge of spatial relations among variant objects.…”

Section: Semantic Graph-based Remote Sensing Scene Understandingmentioning

confidence: 99%

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

Sun

Dustdar

Ranjan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, remote sensing image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of remote sensing images is still lacking. Specifically, this article discusses the main challenges of remote sensing image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.

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“…The above studies show that classical models based on hand-created features are easily interpreted and assume that the extracted features are robust to the variances in the training data but need to be manually engineered [48]. Deep learning-based models, like CNN, present a generalized approach using feature extraction and classification in one trainable model.…”

Section: Potential Classification Modelsmentioning

confidence: 99%

Where’s the Rock: Using Convolutional Neural Networks to Improve Land Cover Classification

Petliak¹,

Cerovski-Darriau

Zaliva

et al. 2019

Remote Sensing

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While machine learning techniques have been increasingly applied to land cover classification problems, these techniques have not focused on separating exposed bare rock from soil covered areas. Therefore, we built a convolutional neural network (CNN) to differentiate exposed bare rock (rock) from soil cover (other). We made a training dataset by mapping exposed rock at eight test sites across the Sierra Nevada Mountains (California, USA) using USDA’s 0.6 m National Aerial Inventory Program (NAIP) orthoimagery. These areas were then used to train and test the CNN. The resulting machine learning approach classifies bare rock in NAIP orthoimagery with a 0.95 F 1 score. Comparatively, the classical OBIA approach gives only a 0.84 F 1 score. This is an improvement over existing land cover maps, which underestimate rock by almost 90%. The resulting CNN approach is likely scalable but dependent on high-quality imagery and high-performance algorithms using representative training sets informed by expert mapping. As image quality and quantity continue to increase globally, machine learning models that incorporate high-quality training data informed by geologic, topographic, or other topical maps may be applied to more effectively identify exposed rock in large image collections.

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Scene Semantic Understanding Based on the Spatial Context Relations of Multiple Objects

Cited by 20 publications

References 34 publications

Remote sensing Image processing, DEEP learning Classification algorithms survey and review

Remote sensing Image processing, DEEP learning Classification algorithms survey and review

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

Where’s the Rock: Using Convolutional Neural Networks to Improve Land Cover Classification

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