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hiperespectral:sae-cd [2018/01/16 17:39]
javier.lopez.fandino [Experimental setup]
hiperespectral:sae-cd [2018/04/03 11:00] (actual)
javier.lopez.fandino [STACKED AUTOENCODERS FOR MULTICLASS CHANGE DETECTION IN HYPERSPECTRAL IMAGES]
Liña 1: Liña 1:
-Experimental results and addictional information related to the paper "​STACKED AUTOENCODERS FOR MULTICLASS CHANGE DETECTION IN HYPERSPECTRAL IMAGES"​.+====== STACKED AUTOENCODERS FOR MULTICLASS CHANGE DETECTION IN HYPERSPECTRAL IMAGES ====== 
 + 
 +Experimental results and addictional information related to the paper "​STACKED AUTOENCODERS FOR MULTICLASS CHANGE DETECTION IN HYPERSPECTRAL IMAGES"​, accepted in the International Geoscience and Remote Sensing Symposium, IGARSS 2018.
  
 ==== Abstract ==== ==== Abstract ====
  
-Change detection (CD) in multitemporal datasets is a key task in remote sensing. In this paper, a scheme to perform multiclass CD for remote sensing hyperspectral datasets extracting features by means of Stacked Autoencoders (SAEs) is introduced. The scheme combines multiclass and binary CD to obtain an accurate multiclass change map. The multiclass +Change detection (CD) in multitemporal datasets is a key task in remote sensing. In this paper, a scheme to perform multiclass CD for remote sensing hyperspectral datasets extracting features by means of Stacked Autoencoders (SAEs) is introduced. The scheme combines multiclass and binary CD to obtain an accurate multiclass change map. The multiclass CD begins with the fusion of the multitemporal data followed by feature extraction by SAE. The binary CD is based on the spectral ​nformation ​by calculating pixel-wise distances and thresholding,​ and it also incorporates spatial information through watershed segmentation. The data coming from the multiclass CD is filtered by using the binary CD map and later classified by a Support Vector Machine or an Extreme Learning Machine algorithm. The scheme was evaluated over a multitemporal hyperspectral dataset obtained from the Hyperion sensor. Experimental results show the effectiveness of the proposed scheme using SAE for extracting the relevant features of the fused information when compared to other published ​feature extraction methods
-CD begins with the fusion of the multitemporal data followed by feature extraction by SAE. The binary CD is based on +
-the spectral ​information ​by calculating pixel-wise distances and thresholding,​ and it also incorporates spatial information through watershed segmentation. The data coming from the multiclass CD is filtered by using the binary CD map and later classified by a Support Vector Machine or an Extreme Learning Machine algorithm. The scheme was evaluated over a multitemporal hyperspectral dataset obtained from the Hyperion sensor. Experimental results show the effectiveness of the proposed scheme using SAE for extracting the relevant features of the fused information when compared to other pub- +
-lished ​feature extraction methods+
  
  
Liña 38: Liña 37:
  
 === Image files === === Image files ===
-{{:​hiperespectral:​referencedatacolorhermiston5.png?​200|}} +|Reference data of changes |Binary CD map |Multiclass CD map| 
-{{:​hiperespectral:​binarycd.png?​200|}} +|{{:​hiperespectral:​referencedatacolorhermiston5.png?​200|}}|{{:​hiperespectral:​binarycd.png?​200|}}|{{:​hiperespectral:​svmcolorhermiston.png?​200|}}|
-{{:​hiperespectral:​svmcolorhermiston.png?​200|}}+
  
-=== Accuracy results === 
-Binary CD accuracies. ​ 
-|  Corect | Missed Alarms| False Alarms | Total Error| 
-|---|---|---|---| 
-|  77020 (98.74%) | 509 | 471 | 980 (1.25%) | 
  
  
 +=== Accuracy results ===
 +==Binary CD accuracies==
 +|**Corect** |**Missed Alarms**|**False Alarms** |**Total Error**|
 +|77020 (98.74%) |509 |471 |980 (1.25%) |
  
-Multiclass CD accuracies. 
  
  
- Classifier | Parameters ​  ​ FE | OA (%)| AA (%)| Kappa+==Multiclass CD accuracies== 
-|---|---|---|---|---|---+|**Classifier**   **Parameters**     |**FE**   **OA (%)**  ​**AA (%)**  ​**Kappa**  ​
-| ELM | N=120  | PCA  | 91.73  | 76.06   86.83 | +| ELM             ​| N=120              | PCA     ​| 91.73       ​| 76.06       ​| 86.83      
-| ELM | N=120  | NWFE |  91.76 |  76.75 |  86.83 | +| ELM             ​| N=120              | NWFE    | 91.76       ​| 76.75       ​| 86.83      
-| ELM | N=60  | SAE  | 95.19  | 90.45  | 92.31  +| ELM             ​| N=60               ​| SAE     ​| 95.19       ​| 90.45       ​| 92.31      
-| SVM | C: 64.0 γ: 32.0  | PCA  | 91.46  | 71.16  | 86.46  +| SVM             ​| C: 64.0 γ: 32.0    | PCA     ​| 91.46       ​| 71.16       ​| 86.46      
-| SVM | C: 32.0 γ: 16.0   ​| NWFE  | 91.29  | 90.61   86.05 | +| SVM             ​| C: 32.0 γ: 16.0    | NWFE    | 91.29       ​| 90.61       ​| 86.05      
-| SVM | C: 32.0 γ: 0.0625 ​ | SAE   95.52 | 92.56  | 92.90  |+| SVM             ​| C: 32.0 γ: 0.0625 ​ | SAE     ​| 95.52       ​| 92.56       ​| 92.90      |
  
 C: penalty term in the training of the SVM. γ: radius of the gaussian function of the SVM. N: Number of neurons in the hidden layer of the ELM. FE: Feature Extraction method. C: penalty term in the training of the SVM. γ: radius of the gaussian function of the SVM. N: Number of neurons in the hidden layer of the ELM. FE: Feature Extraction method.
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  • Última modificación: 2018/01/16 17:39
  • por javier.lopez.fandino