deep learning in remote sensing image classification...

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Deep Learning in Remote Sensing Image Classification and Object Detection Weijia Li, Haohuan Fu, Le Yu Remote sensing background SAE based large-scale land cover classification CNN based palm tree detection Deep learning applications and models False color image RF land cover map SVM land cover map (1) Filename: LC81760392014090 (Zoomed to a part) (2) Location: Egypt (3) Path/Row: 176/039 ANN land cover map SAE land cover map Water Farmland Impervious Forest Bareland Grassland Snow/Ice Shrubland Overall flowchart Classification results comparison Based on the Stacked Autoencoder (SAE), one of the deep learning models, we built a classification framework for large-scale remote sensing image classification. The SAE classifier was trained with 534,396 training samples and assessed by 26,282 test samples. All these samples are collected by human interpretation. Our SAE-based approach achieves the highest classification accuracy of 78.99% with a relatively short predicting time and good land cover mapping results. RF SVM ANN SAE Overall accuracy 76.03% 77.74% 77.86% 78.99% Samples training time 6.062 187.557 41.666 34.154 Samples validation time 0.023 7.119 0.002 0.005 Predicting time (min) 33.605 16344.188 4.014 13.250 Training Samples Testing Samples Landsat Images NDVI & MNDWI Classifiers (RF, SVM, ANN, SAE) Parameters Optimization Accuracy Assessment Features Extraction and Normalization Best Models (RF, SVM, ANN, SAE) Land cover maps (RF, SVM, ANN, SAE) Land cover classification Resolution: 30 meters Classification methods: - Maximum Likelihood - Random Forest - Support vector machine Palm trees detection Resolution: 0.6 meters Classification methods: - Local maximum filter - Template matching - Support vector machine Computer vision applications Remote sensing applications ImageNet Classification Hyperspectral image classification Face detection Vehicle detection Pedestrian detection Building detection CNN ANN Template Local max Scene 1 96.85% 95.27% 93.53% 74.56% Scene 2 94.64% 93.43% 86.19% 75.70% Detection results of scene 1 Detections result of scene 2 Detection accuracy of each method Parameters Optimization CNN training CNN model High-resolution Remote Sensing Image Image Dataset Training Dataset Test Dataset Image dataset label prediction Samples merging Final detection results Overall flowchart Layer 1 2 3 x y z Layer 1 2 3 4 5 Convolutional Neural Network (LeNet) Autoencoder Stacked Autoencoder Convolutional layer Convolutional layer Max-pooling layer Max-pooling layer Fully connected layer (b) ANN (a) CNN (c) Template matching (d) Local maximum filter (a) CNN (b) ANN (c) Template matching (d) Local maximum filter Detection results of each method

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Page 1: Deep Learning in Remote Sensing Image Classification …thuhpgc.org/images/7/7e/Deep_Learning_in_Remote_Sensing_Image... · Deep Learning in Remote Sensing Image Classification

   

   

Deep Learning in Remote Sensing Image Classification and Object Detection Weijia Li, Haohuan Fu, Le Yu

Remote sensing background SAE based large-scale land cover classification

CNN based palm tree detection

Deep learning applications and models

!!!!!!!

False color image !

!!!!!!!

RF land cover map !

!!!!!!!SVM land cover map !

!(1) Filename: LC81760392014090!(Zoomed to a part)!(2) Location: Egypt!(3) Path/Row: 176/039!!!

!!!!!!!ANN land cover map !

!!!!!!!SAE land cover map !

!!! Water!

Farmland!

Impervious!

Forest!

Bareland!

Grassland!

Snow/Ice!

Shrubland!

Overall flowchart Classification results comparison

•  Based on the Stacked Autoencoder (SAE), one of the deep learning models, we built a classification framework for large-scale remote sensing image classification. •  The SAE classifier was trained with 534,396 training samples and

assessed by 26,282 test samples. All these samples are collected by human interpretation. • Our SAE-based approach achieves the highest classification

accuracy of 78.99% with a relatively short predicting time and good land cover mapping results.

RF SVM ANN SAE Overall accuracy 76.03% 77.74% 77.86% 78.99% Samples training time 6.062 187.557 41.666 34.154 Samples validation time 0.023! 7.119 0.002 0.005 Predicting time (min) 33.605 16344.188 4.014 13.250 !

Training Samples!!

Testing Samples!

Landsat Images!!NDVI & MNDWI!

Classifiers !(RF, SVM, ANN, SAE)!

Parameters Optimization!Accuracy Assessment !

Features Extraction and Normalization !

Best Models!(RF, SVM, ANN, SAE)!

Land cover maps !(RF, SVM, ANN, SAE)!

•  Land cover classification •  Resolution: 30 meters •  Classification methods: - Maximum Likelihood - Random Forest - Support vector machine

•  Palm trees detection •  Resolution: 0.6 meters •  Classification methods: - Local maximum filter - Template matching - Support vector machine

Computer vision applications Remote sensing applications

ImageNet Classification Hyperspectral image classification Face detection Vehicle detection

Pedestrian detection Building detection CNN ANN Template Local max

Scene 1 96.85% 95.27% 93.53% 74.56% Scene 2 94.64% 93.43% 86.19% 75.70%

Detection results of scene 1 Detections result of scene 2

Detection accuracy of each method

Parameters Optimization!

CNN training !

CNN model!

High-resolution!Remote Sensing Image!

Image Dataset! Training Dataset!Test Dataset!

Image dataset! label prediction!

Samples merging!

Final detection results!

Overall flowchart

Layer&&&&&1&&&&&&&&&&&&&&&&&2&&&&&&&&&&&&&&&&&3&

!!x!!!!!!!!!!!!!!!!!y!!!!!!!!!!!!!!!!!!z!

Layer&&&&&1&&&&&&&&&&&&&&&&&2&&&&&&&&&&&&&&&&&3&&&&&&&&&&&&&&&&&&4&&&&&&&&&&&&&&&5&&&&&&&&

Convolutional Neural Network (LeNet)

Autoencoder Stacked Autoencoder

Convolutional layer! Convolutional layer!Max-pooling layer! Max-pooling layer! Fully connected layer!

(b) ANN!(a) CNN!

(c) Template matching! (d) Local maximum filter!

(a) CNN! (b) ANN!

(c) Template matching! (d) Local maximum filter!

Detection results of each method