كليدواژه :
طبقه بندي تك مقداري , طبقه بندي فازي , سيستم هاي طبقه بندي كننده چندگانه , داده ليدار , داده فراطيفي
چكيده فارسي :
محدوديتهاي سنجنده هاي مختلف سنجش از دور و ضعف آنها در شناسايي عوارض مختلف باعث شد ادغام دادههاي حاصل از سنجنده هاي مختلف به منظور بهبود نتايج طبقه بندي مورد توجه قرار گيرد. در ميان سنجنده هاي مختلف كنوني، در سالهاي اخير دو سنجنده فراطيفي و ليدار به منظور طبقه بندي زمين بسيار پركاربرد بودهاند. دادههاي حاصل از ليدار اطلاعات ارتفاعي مناسبي را از عوارض زميني به ويژه عوارض داراي ارتفاع مانند ساختمان فراهم ميكنند در حالي كه اطلاعات طيفي ضعيفي از عوارض ايجاد ميكنند. در همين حال داده هاي حاصل از سنجندههاي فراطيفي اطلاعات طيفي مناسبي از عوارضي مانند خاك، آب و چمنزارها فراهم ميكنند ولي اطلاعات ارتفاعي مناسبي از آنها در دسترس نيست. تحقيق پيش رو به منظور طبقه بندي بهتر عوارض زميني، از مفهوم سيستم طبقه بندي كننده چندگانه فازي براي طبقه بندي داده هاي فراطيفي و ليدار استفاده ميكند. پس از استخراج فضاي ويژگي براي هر دو داده فراطيفي و ليدار، از روش طبقه بندي فازي نزديكترين همسايگي براي طبقه بندي فازي هر داده استفاده خواهد شد. روش ادغام مورد استفاده براي ادغام تصميمات فازي، روش الگوي تصميم گيري ميباشد. به منظور ارزيابي هر چه بيشتر روش پيشنهادي، يك سيستم طبقه بندي كننده چندگانه تك مقداري نيز به منظور مقايسه نتايج با روش پيشنهادي فازي ارائه ميشود. در قسمت تك مقداري از طبقه بندي كننده ماشين بردار پشتيبان (SVM) براي طبقه بندي هر داده بصورت مجزا استفاده ميشود و در ادامه روش ادغام بيزين براي ادغام نتايج طبقه بندي در سطح تصميم گيري مورد استفاده قرار ميگيرد. روش پيشنهادي روي داده فراطيفي و ليدار دانشگاه هيوستون و محوطه شهري اطراف آن پياده سازي شد. نتايج طبقه بندي فازي دادههاي ليدار و فراطيفي 75% و 88% بوده است. اين درحالي است كه سيستم فازي ادغام طبقه بندي كننده ها دقت 96% را ايجاد كرده است. استفاده از ادغام طبقه بندي كننده ها باعث بهبود طبقه بندي كلاسهاي زميني ميشود. به علاوه اين تحقيق نشان ميدهد سيستم طبقه بندي كننده چندگانه در حالت فازي در نهايت بيشترين افزايش دقت را در مقايسه با سيستمهاي تك مقداري حاصل ميكن
چكيده لاتين :
Regarding to the limitations and benefits of remote sensing sensors, fusion of remote sensing data from multiple sensors is effective at land cover classification. All these data have different characteristics, e.g., different spatial and spectral resolutions, different angle of view, and different abilities and disabilities. For many applications, the information provided by individual sensors is incomplete, inconsistent, or imprecise. Fusion of information from different sensors can produce a better understanding of the observed site, which is not possible with single sensor. Particularly, Light Detection and Ranging (LiDAR) provides accurate height information for objects on the earth, which makes LiDAR become more and more popular in terrain and land surveying. On the other hand, hyperspectral imaging is a relatively new technique in remote sensing that acquires hundreds of images corresponding to different spectral channels. The rich spectral information of HS data increases the capability to distinguish different physical materials, leading to the potential of a more accurate image classification. As hyperspectral and LIDAR data provide complementary information (spectral reflectance, and vertical structure, respectively), a promising and challenging approach is to fuse these data in the information extraction procedure.
This paper presents a multiple fuzzy classifier system (Multiple Classifier System or MCS) for fusions of hyperspectral and LiDAR data based on Decision Template (DT). After feature extraction on each data, the classification was performed by fuzzy K-Nearest Neighbor (KNN) on hyperspectral and LiDAR data separately. In a multiple fuzzy decision system, a set of decisions is first produced and then combined by a specific fusion method. The output of the fuzzy classifiers that provide the class belongingness of an input pattern to different classes is arranged in a matrix form defined as decision profile (DP) matrix. Then, a fuzzy decision fusion method (Decision Tempate) is utilized to fuse the results of fuzzy KNNs on hyperspectral and LiDAR data. In order to assess the fuzzy MCS proposed method, a crisp MCS based on (Support Vector Machine) SVM as crisp classifier and Naive Bayes (NB) as crisp classifier fusion method is applied on hyperspectral and LiDAR data. The experiments were executed on a hyperspectral image and a LiDAR derived Digital Surface Model (DSM); both with spatial resolution of 2.5 m. The dataset have captured over the University of Houston campus and the neighbouring urban area by the NSF-funded Centre for Airborne Laser Mapping (NCALM). Also hyperspectral image has 144 spectral bands in 380 nm to 1050 nm region. Training and testing samples were selected from different areas of the images. They are spatially disjointed.
Fuzzy MCS on hyperspectral and LiDAR data provide interesting conclusions on the effectiveness and potentialities of the joint use of these two data. Overall accuracies of fuzzy classifiers on LiDAR and hyperspectral data are %75 and %88 respectively. Fusion of these two fuzzy classifiers produced %96 as overall accuracy. Second scenario for joint use of hyperspectral and LiDAR data is fusion of these two data through a crisp decision fusion system. The results show that fuzzy classifier provided higher accuracies than crisp classification based on SVM for both data. In the presence of mixed coverage pixels in remote sensing data, crisp classifiers may produce errors while fuzzy classifiers are not affected by such errors and in principle can produce a classification that is more accurate than any crisp classifier. Also, fusion of ensemble of fuzzy classifiers based on Decision Template method produced more accuracy than fusion of crisp SVMs based on Bayesian Theory.
