Mixed Pixel Analysis for Flood Mapping Using Extended Support Vector Machine

Dey, Chandrama; Jia, Xiuping; Fraser, Donald; Wang, L.

doi:10.1109/dicta.2009.55

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Spectral unmixing models like indices-based spectral unmixing [23], multiple end-member spectral mixture analysis (MESMA) [7], linear spectral unmixing [24] and Gaussian mixture model [25] are adopted for estimating the proportion of partial inundation from mixed pixels. Previously, we investigated linear spectral unmixing on extended support vector machines (u-eSVM) [26,27] to extract proportions of flood water from both pure pixels (pixels containing flood water) and mixed pixels. Later in [28], we proposed a Bayesian approach to enhance the previous classification results using u-eSVM by representing the probability values of flooding of each pixel instead of representing the flood fractional coverage of each pixel in the flood classification map.…”

Section: Introductionmentioning

confidence: 99%

Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

et al. 2019

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Remote sensing technology in recent years has been regarded the most important source to provide substantial information for delineating the flooding extent to the disaster management authority. There have been numerous studies proposing mathematical or statistical classification models for flood mapping. However, conventional pixel-wise classifications methods rely on the exact match of the spectral signature to label the target pixel. In this study, we propose a fully convolutional neural networks (F-CNNs) classification model to map the flooding extent from Landsat satellite images. We utilised the spatial information from the neighbouring area of target pixel in classification. A total of 64 different models were generated and trained with a variable neighbourhood size of training samples and number of learnable filters. The training results revealed that the model trained with 3 × 3 neighbourhood sized training samples and with 32 convolutional filters achieved the best performance out of the experiments. A new set of different Landsat images covering flooded areas across Australia were used to evaluate the classification performance of the model. A comparison of our proposed classification model to the conventional support vector machines (SVM) classification model shows that the F-CNNs model was able to detect flooded areas more efficiently than the SVM classification model. For example, the F-CNNs model achieved a maximum precision rate (true positives) of 76.7% compared to 45.27% for SVM classification.

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Section: Introductionmentioning

confidence: 99%

Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

et al. 2019

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“…By linear mixed pixel model, each part of a mixed pixel equals area ratio of the part and the whole pixel size [27]. So the DN of (j, k) cell, g (j, k) is,…”

Section: B Linear Mixed Resampling Modelmentioning

confidence: 99%

ASQ-MPHAAS: Multi-Payload Observation System From High Altitude Airship

Zhang

Chai

2019

IEEE Sensors J.

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One of the critical advantages of a high-altitude airship (HAA) is its ability to carry multiple-task payloads for remote sensing monitoring on the plateau. We developed a multipayload observation system based on the HAA (ASQ-MPHAAS) for remote sensing monitoring of the Qinghai plateau. In this paper, we demonstrate the design, development, implementation of the ASQ-MPHAAS. The ASQ-MPHAAS includes two parts; one part is our HAA (ASQ-HAA380), and the other part is our multi-payload observation system (ASQ-MPOS-1). The ASQ-MPOS-1 consists of two hyperspectral push-broom imaging sensors, a multispectral imaging sensor, a high-resolution RGB camera, a video sensor, a Position and Orientation System (POS), and other components. The ASQ-MPOS-1 is installed under the ASQ-HAA380. We also introduce some key methods of data processing we propose to solve unique problems brought by HAA. Four real-world field experiments are presented in this paper to demonstrate that the ASQ-MPHAAS can capture different kinds of remote sensing data at a high spatial resolution of a few or dozen centimeters. Index Terms-Multi-payload observation, high altitude airship time synchronization, hyperspectral imaging, multispectral imaging. I. INTRODUCTIONM OST parts of Qinghai Province are located at altitudes over 3000-4000 m. It is challenging to observe the grassland on Qinghai plateau using common airborne platforms such as manned aircraft and UAVs. Our research group developed a new multi-payload system based on an HAA (ASQ-MPHAAS) for remote sensing monitoring of the grasslands on Qinghai plateau. The ASQ-MPHAAS includes two parts, the HAA developed by our research group, called ASQ-HAA380, and the other part is our ASQ-MPOS-1. The ASQ-HAA380 is 38 meters long and 12 meters high and can fly at an altitude of 5000m above sea level. The ASQ-MPOS-1 includes a high spatial resolution camera with Manuscript

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“…In many cases, a MODIS pixel is a mixed pixel that is covered by multiple land cover types, which has a significant influence on the information that can be derived. 1,2 Thus, the decomposition of mixed pixels in MODIS images is critically important for the application of MODIS data in many fields, such as mapping land cover distributions, 3 evaluating vegetation/soil fractional cover, [4][5][6] monitoring and evaluating karst rocky desertification, 7 flood mapping, 8,9 and retrieving fire temperature and area. 10 The spectral characteristics of ground features are the basis not only for identifying them in remote sensing images but also for decomposing mixed pixels in images.…”

Section: Introductionmentioning

confidence: 99%

Decomposition of mixed pixels in MODIS data using Bernstein basis functions

Qin

Guo²,

Ren

et al. 2019

J. Appl. Rem. Sens.

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The decomposition of mixed pixels in Moderate Resolution Imaging Spectroradiometer (MODIS) images is essential for the application of MODIS data in many fields. Many existing methods for unmixing mixed pixels use principal component analysis to reduce the dimensionality of the image data and require the extraction of endmember spectra. We propose the pixel spectral unmixing index (PSUI) method for unmixing mixed pixels in MODIS images. In this method, a set of third-order Bernstein basis functions is applied to reduce the dimensionality of the image data and characterize the spectral curves of the mixed pixels in a MODIS image, and then the derived PSUIs (i.e., the coefficients of the basis functions) are calibrated by means of the abundance values of the ground features from the Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) classification images corresponding to the date and region of the MODIS image. The proposed method was tested on MODIS and ETM+/OLI images, and it obtained satisfying unmixing results. We compared the PSUI method with conventional methods, including the pixel purity index, the N-finder algorithm, the sequential maximum angle convex cone, and vertex component analysis and found that the PSUI method outperformed the other four methods. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Mixed Pixel Analysis for Flood Mapping Using Extended Support Vector Machine

Cited by 7 publications

References 9 publications

Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

ASQ-MPHAAS: Multi-Payload Observation System From High Altitude Airship

Decomposition of mixed pixels in MODIS data using Bernstein basis functions

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