Object-based hyperspectral image classification using a new latent block model based on hidden Markov random fields

Fatemighomi, Hamideh Sadat; Golalizadeh, Mousa; Amani, Meisam

doi:10.1007/s10044-021-01050-3

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…The first step in an object-based classification method is segmentation. Seg- It is widely reported that object-based classification results have a higher classification accuracy compared to a pixel-based method, especially when high-resolution images are available [23,24,[35][36][37]. Thus, habitat maps were produced using the object-based image analysis.…”

Section: Methodsmentioning

confidence: 99%

Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms

Amani,

Foroughnia,

Moghimi

et al. 2023

Remote Sensing

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Progress toward habitat protection goals can effectively be performed using satellite imagery and machine-learning (ML) models at various spatial and temporal scales. In this regard, habitat types and landscape structures can be discriminated against using remote-sensing (RS) datasets. However, most existing research in three-dimensional (3D) habitat mapping primarily relies on same/cross-sensor features like features derived from multibeam Light Detection And Ranging (LiDAR), hydrographic LiDAR, and aerial images, often overlooking the potential benefits of considering multi-sensor data integration. To address this gap, this study introduced a novel approach to creating 3D habitat maps by using high-resolution multispectral images and a LiDAR-derived Digital Surface Model (DSM) coupled with an object-based Random Forest (RF) algorithm. LiDAR-derived products were also used to improve the accuracy of the habitat classification, especially for the habitat classes with similar spectral characteristics but different heights. Two study areas in the United Kingdom (UK) were chosen to explore the accuracy of the developed models. The overall accuracies for the two mentioned study areas were high (91% and 82%), which is indicative of the high potential of the developed RS method for 3D habitat mapping. Overall, it was observed that a combination of high-resolution multispectral imagery and LiDAR data could help the separation of different habitat types and provide reliable 3D information.

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Section: Methodsmentioning

confidence: 99%

Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms

Amani,

Foroughnia,

Moghimi

et al. 2023

Remote Sensing

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“…As mentioned earlier, an object-based supervised classification algorithm was implemented in this study due to its higher accuracy and better quality in terms of visual perspective (e.g., fewer salt-and-pepper effects compared with pixel-based methods) [19,39,47,48,59]. The proposed method in this study can be divided into three distinct steps: image segmentation, first segment classification, and second classification.…”

Section: Classification Modelmentioning

confidence: 99%

“…For all three groups of classification approaches, the spatial resolution of the satellite imagery, number of spectral bands, repetition time of the RS datasets, and complexity of the study area have direct effects on the precision of the final classified wetlands [11]. Overall, it has been frequently reported that using object-based classification techniques improves the accuracy of the classified map, particularly when using high-resolution RS datasets [8,12,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

et al. 2023

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The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95).

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“…Recent progress in HCD has emphasized the value of using Siamese networks and double-stream architectures for improved spectral-spatial analysis [39,40]. Innovative methods such as meta-learning and self-supervised learning have proven effective in overcoming HCD challenges.…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module

Seydi,

Boueshagh,

Namjoo

et al. 2024

Remote Sensing

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Human activities and natural phenomena continually transform the Earth’s surface, presenting ongoing challenges to the environment. Therefore, the accurate and timely monitoring and prediction of these alterations are essential for devising effective solutions and mitigating environmental impacts in advance. This study introduces a novel framework, called HCD-Net, for detecting changes using bi-temporal hyperspectral images. HCD-Net is built upon a dual-stream deep feature extraction process, complemented by an attention mechanism. The first stream employs 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks to extract deep features, while the second stream utilizes 2D convolution and 2D SE blocks for the same purpose. The deep features from both streams are then concatenated and processed through dense layers for decision-making. The performance of HCD-Net is evaluated against existing state-of-the-art change detection methods. For this purpose, the bi-temporal Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset was utilized to assess the change detection performance. The findings indicate that HCD-Net achieves superior accuracy and the lowest false alarm rate among the compared methods, with an overall classification accuracy exceeding 96%, and a kappa coefficient greater than 0.9.

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Object-based hyperspectral image classification using a new latent block model based on hidden Markov random fields

Cited by 9 publications

References 31 publications

Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms

Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module

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