Embedded Implementation of VHR Satellite Image Segmentation

Li, Chao; Balla-Arabé, Souleymane; Ginhac, Dominique; Yang, Fan

doi:10.3390/s16060771

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…On the other hand, using segmentation prior to classification increases accuracy, since it helps extract features in an image using image partition, which improves the classification process. For example, landscape pattern recognition can be implemented using the partition of the bitmap satellite image by the optimisation technique of regrouping patches [63]. Moreover, the approaches of change detection based on image segmentation are often used for mapping based on remote sensing data [64].…”

Section: Research Goals and Gapsmentioning

confidence: 99%

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Lemenkova

2023

Analytics

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This paper presents the object detection algorithms GRASS GIS applied for Landsat 8-9 OLI/TIRS data. The study area includes the Sudd wetlands located in South Sudan. This study describes a programming method for the automated processing of satellite images for environmental analytics, applying the scripting algorithms of GRASS GIS. This study documents how the land cover changed and developed over time in South Sudan with varying climate and environmental settings, indicating the variations in landscape patterns. A set of modules was used to process satellite images by scripting language. It streamlines the geospatial processing tasks. The functionality of the modules of GRASS GIS to image processing is called within scripts as subprocesses which automate operations. The cutting-edge tools of GRASS GIS present a cost-effective solution to remote sensing data modelling and analysis. This is based on the discrimination of the spectral reflectance of pixels on the raster scenes. Scripting algorithms of remote sensing data processing based on the GRASS GIS syntax are run from the terminal, enabling to pass commands to the module. This ensures the automation and high speed of image processing. The algorithm challenge is that landscape patterns differ substantially, and there are nonlinear dynamics in land cover types due to environmental factors and climate effects. Time series analysis of several multispectral images demonstrated changes in land cover types over the study area of the Sudd, South Sudan affected by environmental degradation of landscapes. The map is generated for each Landsat image from 2015 to 2023 using 481 maximum-likelihood discriminant analysis approaches of classification. The methodology includes image segmentation by ‘i.segment’ module, image clustering and classification by ‘i.cluster’ and ‘i.maxlike’ modules, accuracy assessment by ‘r.kappa’ module, and computing NDVI and cartographic mapping implemented using GRASS GIS. The benefits of object detection techniques for image analysis are demonstrated with the reported effects of various threshold levels of segmentation. The segmentation was performed 371 times with 90% of the threshold and minsize = 5; the process was converged in 37 to 41 iterations. The following segments are defined for images: 4515 for 2015, 4813 for 2016, 4114 for 2017, 5090 for 2018, 6021 for 2019, 3187 for 2020, 2445 for 2022, and 5181 for 2023. The percent convergence is 98% for the processed images. Detecting variations in land cover patterns is possible using spaceborne datasets and advanced applications of scripting algorithms. The implications of cartographic approach for environmental landscape analysis are discussed. The algorithm for image processing is based on a set of GRASS GIS wrapper functions for automated image classification.

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Section: Research Goals and Gapsmentioning

confidence: 99%

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Lemenkova

2023

Analytics

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“…In order to potentially greater optimize the loop-body logic, we manipulate the source code in loop-level using loop flattening and loop merging (see Fig. (5)).…”

Section: Loop Manipulationmentioning

confidence: 99%

“…From the hardware point of view, the challenge of real-time image processing is to find the optimal platform satisfying the requirements of the image processing application among a large space of potential solutions. Its solution exploration therefore usually revolves around how to combine the software implementation with the hardware platform [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

High-level synthesis for FPGAs: code optimization strategies for real-time image processing

Benezeth

et al. 2017

J Real-Time Image Proc

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High-Level Synthesis (HLS) is a potential solution to increase the productivity of FPGA based real-time image processing development. It allows designers to reap the benefits of hardware implementation directly from the algorithm behaviors specified using C-like languages with high abstraction level. In order to close the performance gap between the manual and HLS based FPGA designs, various code optimization forms are made available in today's HLS tools. This paper proposes a HLS source code and directive manipulation strategy for real-time image processing by taking into account the applying order of different optimization forms. Experiment results demonstrate that our

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“…On the other hand, the application of the LB method in image processing can be regarded as a diffusion process; we will explain this viewpoint in Section 2.3 . While LB methods have been successfully applied to image denoising [ 20 ], inpainting [ 21 ] and segmentation [ 22 , 23 ], there is little research on medical image segmentation. As far as we know, these studies include: Segmentation of tumors in 3D ultrasound images [ 24 ]; using the LB algorithm to solve the distance regularization level set (DRLS) to complete the segmentation of intracranial giant aneurysm thrombus [ 25 , 26 ]; embedding local statistical information into the LB external force term to segment brain white matter [ 27 ]; using the principal component analysis (PCA) method to extract the main components of HC labels as the shape prior of the LB segmentation model [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI

Liu

Yan

2020

Sensors

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Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer’s disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results.

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Embedded Implementation of VHR Satellite Image Segmentation

Cited by 7 publications

References 39 publications

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

High-level synthesis for FPGAs: code optimization strategies for real-time image processing

A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI

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