| DC 欄位 |
值 |
語言 |
| DC.contributor | 太空科學研究所 | zh_TW |
| DC.creator | 黃智鉉 | zh_TW |
| DC.creator | Chih-Hsuan Huang | en_US |
| dc.date.accessioned | 2019-1-24T07:39:07Z | |
| dc.date.available | 2019-1-24T07:39:07Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=100683004 | |
| dc.contributor.department | 太空科學研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 合成孔徑雷達(synthetic aperture radar, SAR)影像經常被使用在監測地面的目
標物，因為其主動提供能量源以及對雲雨的穿透特性，在任何時間或天氣
狀況之下，都可以得到地面觀測的結果。因此利用前後期影像配合變遷偵
測技術，可以監測自然環境的事件或者人為活動對於環境的改變。理論上
合成孔徑訊號可以用空間混沌理論模型來表示，因為一個像素解析度內的
散射訊號是同調訊號的總合，而渾沌理論模型的特性是碎形維度。在這個
研究中，使用兩種估算碎形維度的方法，一種是差分盒維度數(differential
box-counting, DBC) ，另一種是改進 z 方向的碎形維度。我們提出一個簡單
的基於空間混沌模型理論的合成孔徑雷達影像變遷偵測方法。首先先計算
各個時期合成孔徑雷達影像的碎形維度之差值，偵測建物和植被復育區域
造成的改變。接著各自使用固定誤警率 (constant false alarm rate, CFAR)和
支持向量機 (support vector machine, SVM) 去分類變遷和未變遷區域。實驗
結果顯示出差分盒維度數和改進的方法對於偵測建物區域的準確度是差不
多的。然而，植被的復育部分，改進的碎形方法相較於差分盒維度數有較
好的分類準確度。最後的結果也顯示支持向量機相較於固定誤警率，不管
在建物區域或者植被復育區域都有比較好的分類效能。前後期影像的碎形
數值直接交由支持向量機作變遷偵測分類比前後期影像強度差值的準確度
還高。 | zh_TW |
| dc.description.abstract | Due to their all-weather, all-time and penetration characteristics, synthetic aperture radar (SAR) images are frequently used to monitor ground targets. As a result, environmental changes via natural events or human activities can be observed by applying a change detection technique. Theoretically, SAR signals can be characterized as chaotic phenomena since the scattering of signals within a resolution cell can be summed coherently. Accordingly, an SAR signal can be represented by spatial chaotic model (SCM) and characterized by its fractal dimension. In this study, two approaches for estimating fractal dimensions are conducted, which are estimated by the differential box-counting (DBC) and improved fractal dimension methods in the z-direction. Based on the spatial chaotic model, a simplified SAR image change detection procedure is proposed. This method first calculates the differences in fractal dimensions among multitemporal SAR images to detect the changes in building and grass-recovery areas. Both the constant false alarm rate (CFAR) and support vector machine (SVM) are applied to classify the changed and unchanged areas, respectively. The experimental results reveal that both the DBC and improved fractal dimension methods are similar for detecting changes in building areas. However, regarding the changes in grass recovery areas, the improved fractal dimension method outperforms the DBC method. The results also show that the SVM performs better than the CFAR for both building and grass areas. SVM with all fractal dimensions has higher accuracy than only the difference of fractal
dimensions difference between SAR images. | en_US |
| DC.subject | 碎形維度 | zh_TW |
| DC.subject | 變遷偵測 | zh_TW |
| DC.subject | 空間混沌模型 | zh_TW |
| DC.subject | 支持向量機 | zh_TW |
| DC.subject | 差分盒維度數 | zh_TW |
| DC.subject | 合成孔徑雷達 | zh_TW |
| DC.subject | fractal dimension | en_US |
| DC.subject | change detection | en_US |
| DC.subject | spatial chaotic model | en_US |
| DC.subject | support vector machine (SVM) | en_US |
| DC.subject | differential box-counting (DBC) | en_US |
| DC.subject | synthetic aperture radar (SAR) | en_US |
| DC.title | Comparison of Change Detection Methods Based on the Spatial Chaotic Model for Synthetic Aperture Radar Imagery | en_US |
| dc.language.iso | en_US | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |