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Do Deep Neural Networks Learn Full Waveform LiD...

teddy
November 18, 2019

Do Deep Neural Networks Learn Full Waveform LiDAR Data?

I have talked about raw full waveform lidar data analysis using deep learning method called pointnet at Joint Student Seminar on Remote Sensing and Geoinformatics.

teddy

November 18, 2019
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  1. Do Deep Neural Networks Learn Full Waveform LiDAR Data? Takayuki

    SHINOHARA Matsuoka Laboratory, Tokyo Institute of Technology 2019/11/18 Joint Student Seminar on Remote Sensing and Geoinformatics
  2. Full Waveform LiDAR nAdvantages • Recording the entire reflected signal

    “waveform” discretely. • Providing not only 3D point clouds, but also additional information about the target properties. nData Analysis • Costly and time consuming at manual processing. Automatic analysis method for "raw” waveform data is needed. 3 Cited from “Urban land cover classification using airborne LiDAR data: A review”
  3. Raw Waveform analysis nDeep learning (S. Zorzi et al., 2019)

    • 2 stages supervised classification ‣ 1st stage: Waveform analysis by 1D CNN ⁃ Highly miss classification ‣ 2nd stage: Spatial analysis by 2D CNN ⁃ Raw classification results are converted to grid data ⁃ Prediction the class each grid. • Problems ‣ Spatial raw waveforms are not used in 1st stage. Þ Spatial learning method for raw waveform data are needed to improve performance. 4 Waveform Spatial
  4. Solve problem nPoint Net(Qi et.al, 2017) • One of the

    deep learning method for spatially irregular data. • Input order invariant network. nAuto Encoder as representation learning • Representation learning is data driven feature extraction method. • Auto Encoder is one of the method. • Auto Encoder can extract low dimensional latent vector from high dimensional data such as Image or some spatial data. Objective in this study: Using a deep learning method, a new representation learning method for spatially distributed raw full-waveform data. 5
  5. Proposed network (FWNetAE) nAuto Encoder for full waveform data •

    Encoder: PointNet based • Decoder: simple Multi Layer Perceptron(MLP) 7 62 2,048 … 1D Conv features features features features features features … Input full waveform LiDAR Data Output full-waveform LiDAR Data Max Pool MLP 62 2,048 Latent vector PointNet based Encoder Decoder T-nets
  6. Proposed network (FWNetAE) nEncoder: Point Net based Architecture • 1D

    CNN: Extract local features • MaxPool: Extract grobal features • T-nets: Extract rotation invariant features 8 62 2,048 … 1D Conv features features features features features features … Input full waveform LiDAR Data Output full-waveform LiDAR Data Max Pool MLP 62 2,048 Latent vector PointNet based Encoder Decoder T-nets
  7. Proposed network (FWNetAE) nDecoder: MLP based architecture • Fully connected

    layers to produce reconstructed data ! of 2,048 × 62 dimensions, i.e., the same as those of input data 9 62 2,048 … 1D Conv features features features features features features … Input full waveform LiDAR Data Output full-waveform LiDAR Data Max Pool MLP 62 2,048 Latent vector PointNet based Encoder Decoder T-nets
  8. Loss function nLoss function for reconstruction • FWNetAE aims at

    reconstructing target reconstructed data ! . • Reconstruction loss functions. ‣ Spatial matching loss ‣ Waveform reconstruction 10 %&'() = 1 2N . /01 2 3 / − 6 / 7 + / − 6 / 7), 1 <'=)>?@A = 1 2N . /01 2 . B01 C /,B − ̂ /,B 7 . 2 Optimization process Minimize these function
  9. Dataset and Implementation nDataset • Dublin City Dataset ‣ Sample

    Size: ⁃ Train, Val, Test: 300,000, 100,000 100,000 nImplementation and training results • Implementation ‣ Framework: PyTorch ‣ Hardware: 7th generation 3.8 GHz i7 CPU, 32 GB of RAM, and a Quadro P6000 GPU • Result ‣ Training time: approximately 9 hours ‣ Reconstruction error(test data): spatial=0.051, waveform=0.29 12 Used data
  10. Latent Space Visualization nComparison of some method 15 Nonspatial AE

    PCA Proposed method (FWNetAE) Learnable algorithms Not Learnable
  11. Conclusions and Future Study nConclusions • This paper presents a

    novel representation learning method for spatially distributed full-waveform data observed from an ALS using an AE-based architecture called FWNetAE. • The results demonstrate a generalization error for invisible test data. • Moreover, the FWNetAE encoded a meaningful latent vector and the decoders reconstructed the spatial geometry and its waveform value from the encoded latent vector. • However, the PointNet-based encoders could not extract features at various resolutions. nFuture Study • Modern Hieratical learning: PointNet++, Dynamic Graph CNN • Application for Supervised Learning 17