Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

Alakian, Alexandre; Achard, Véronique

doi:10.3390/rs12142335

Cited by 4 publications

(3 citation statements)

References 54 publications

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“…Airborne vehicles provide high-resolution images and can adjust the angles, positions, and instruments as required (Alakian and Achard, 2020). For example, Man et al (2020) extracted grass and trees in urban areas based on airborne hyperspectral and LiDAR data.…”

Section: Introductionmentioning

confidence: 99%

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Jia

Cui

Liu

2022

Front. Environ. Sci.

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Green infrastructure (GI), such as green roofs, is now widely used in sustainable urban development. An accurate mapping of GI is important to provide surface parameterization for model development. However, the accuracy and precision of mapping GI is still a challenge in identifying GI at the small catchment scale. We proposed a framework for blue-green-gray infrastructure classification using machine learning algorithms and unmanned aerial vehicle (UAV) images that contained digital surface model (DSM) information. We used the campus of the Southern University of Science and Technology in Shenzhen, China, as a study case for our classification method. The UAV was a DJI Phantom 4 Multispectral, which measures the blue, green, red, red-edge, and near-infrared bands and DSM information. Six machine learning algorithms, i.e., fuzzy classifier, k-nearest neighbor classifier, Bayes classifier, classification and regression tree, support vector machine (SVM), and random forest (RF), were used to classify blue (including water), green (including green roofs, grass, trees (shrubs), bare land), and gray (including buildings, roads) infrastructure. The highest kappa coefficient was observed for RF and the lowest was observed for SVM, with coefficients of 0.807 and 0.381, respectively. We optimized the sampling method based on a chessboard grid and got the optimal sampling interval of 11.6 m to increase the classification efficiency. We also analyzed the effects of weather conditions, seasons, and different image layers, and found that images in overcast days or winter days could improve the classification accuracy. In particular, the DSM layer was crucial for distinguishing green roofs and grass, and buildings and roads. Our study demonstrates the feasibility of using UAV images in urban blue-green-gray infrastructure classification, and our infrastructure classification framework based on machine learning algorithms is effective. Our results could provide the basis for the future urban stormwater management model development and aid sustainable urban planning.

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Section: Introductionmentioning

confidence: 99%

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Jia

Cui

Liu

2022

Front. Environ. Sci.

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“…Meanwhile, hyperspectral indices based on chemical knowledge of materials are well suited to focus on specific bands in order to highlight some spectral characteristics. For instance, they can easily discriminate plastics based on specific absorption peaks [5]. As far as spectral indices rely on the chemical composition of materials, they can be used for various sensors and geographic areas.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Classification Based on Spectral Indices Learned Through Soft Attention Units

Thoreau

Achard

Briottet

2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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Classification of hyperspectral scenes has been dominated in recent years by Convolutional Neural Networks (CNNs). Spectral and spatial convolutions have proven to be very effective in learning discriminative representations. The power of CNNs however is limited by their small receptive fields. Training deeper CNNs with wider receptive fields is a very difficult task with regard to the small amount of available training samples. Furthermore, CNNs seem to fail in capturing very local spectral features such as absorption peaks.In the present paper, we introduce a new paradigm inspired by physics-based hyperspectral indices and attention mechanisms. Our model learns spectral indices by focusing on specific spectral bands through soft attention units. It achieves high or better overall accuracy and kappa score than state-ofthe-art CNNs on Pavia University, Kennedy Space Center and our own real-world dataset while dramatically reducing the number of parameters needed and increasing interpretability.

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“…Remote sensing techniques are used on asphalt pavements to study their physical and chemical characteristics, and also to evaluate the condition of pavements such as ageing and material composition [1][2][3][4][5][6]. There are various techniques in remote sensing that can be used for the study of asphalt concrete's behavior, including spectral libraries, Unmanned Aerial Vehicles (UAV's), digital sensors (such as RGB and thermal imaging), Ground Penetrating Radars (GPRs), and satellite data [7][8][9][10][11]. The most common equipment to be used for ground truth data is a spectroradiometer.…”

Section: Introductionmentioning

confidence: 99%

The Use of Colorimeters to Support Remote Sensing Techniques on Asphalt Pavements

et al. 2020

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Characterization of asphalt pavements, based on ground spectroradiometers, has been studied in the past to determine their spectral response concerning the physical, chemical, and condition properties of the pavement. This paper suggests an alternative technique for characterizing ageing of asphalt pavements using a colorimeter. Colorimeters are considered as affordable equipment in laboratories in contrast to other scientific instruments and turn remote sensing ground techniques more accessible to industry. Therefore, the study proposes a new methodology indicating how colorimeters can be used in combination with satellite data for the age characterization of asphalt pavements. Spectroradiometer data are compared in a two-way methodology to colorimeter data. The final steps of the methodology used in the study show very similar results for both equipment after a comparison of separability indices (Euclidean and Mahalanobis distances). It is a fact that colorimeter data can be used as ground truth data. The application was performed using an in-band analysis of WorldView 3 (WV3) spectral bands situated in the visible electromagnetic spectrum. Based on the findings of this study, we proposed the Normalized Difference Equation/filter for asphalt Pavement Age characterization Index (NDPAI).

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Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

Cited by 4 publications

References 54 publications

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Urban Catchment-Scale Blue-Green-Gray Infrastructure Classification with Unmanned Aerial Vehicle Images and Machine Learning Algorithms

Hyperspectral Classification Based on Spectral Indices Learned Through Soft Attention Units

The Use of Colorimeters to Support Remote Sensing Techniques on Asphalt Pavements

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