Abstract

Land use and Land cover (LULC) mapping is one of the most important application areas of remote sensing which requires both spectral and spatial resolutions in order to decrease the spectral ambiguity of different land cover types. Airborne hyperspectral images are among those data which perfectly suits to that kind of applications because of their high number of spectral bands and the ability to see small details on the field. As this technology has newly developed, most of the image processing methods are for the medium resolution sensors and they are not capable of dealing with high resolution images. Therefore, in this study a new framework is proposed to improve the classification accuracy of land use/cover mapping applications and to achieve a greater reliability in the process of mapping land use map using high resolution hyperspectral image data. In order to achieve it, spatial information is incorporated together with spectral information by exploiting feature extraction methods like Grey Level Co-occurrence Matrix (GLCM), Gabor and Morphological Attribute Profile (MAP) on dimensionally reduced image with highest accuracy. Then, machine learning algorithms like Random Forest (RF) and Support Vector Machine (SVM) are used to investigate the contribution of texture information in the classification of high resolution hyperspectral images. In addition to that, further analysis is conducted with object based RF classification to investigate the contribution of contextual information. Finally, overall accuracy, producer’s/user’s accuracy, the quantity and allocation based disagreements and location and quantity based kappa agreements are calculated together with McNemar tests for the accuracy assessment. According to our results, proposed framework which incorporates Gabor texture information and exploits Discrete Wavelet Transform based dimensionality reduction method increase the overall classification accuracy up to 9%. Amongst individual classes, Gabor features boosted classification accuracies of all the classes (soil, road, vegetation, building and shadow) to 7%, 6%, 6%,8%,9%, and 24% respectively with producer’s accuracy. Besides, 17% and 10% increase obtained in user’s accuracy with MAP(area) feature in classifying road and shadow classes respectively. Moreover, when the object based classification is conducted, it is seen that the OA of pixel based classification is increased further by 1.07%. An increase between 2% and 4% is achieved with producer’s accuracy in soil, vegetation and building classes and an increase between 1% and 3% is achieved by user’s accuracy in soil, road, vegetation and shadow classes. In the end, accurate LULC map is produced with object based RF classification of gabor features added airborne hyperspectral image which is dimensionally reduced with DWT method.

摘要

土地利用和土地覆盖（LULC）制图是遥感最重要的应用领域之一，它需要光谱和空间分辨率，以减少不同土地覆盖类型的光谱模糊性。机载高光谱图像是非常适合此类应用的数据之一，因为它们具有大量光谱带并且能够查看现场的小细节。由于这项技术是新开发的，大多数图像处理方法是针对中等分辨率传感器的，它们不能处理高分辨率图像。所以，在这项研究中，提出了一个新的框架，以提高土地利用/覆盖制图应用的分类精度，并在使用高分辨率高光谱图像数据绘制土地利用图的过程中实现更高的可靠性。为了实现它，通过在降维图像上以最高准确度利用灰度共生矩阵 (GLCM)、Gabor 和形态属性剖面 (MAP) 等特征提取方法，将空间信息与光谱信息结合在一起。然后，使用随机森林 (RF) 和支持向量机 (SVM) 等机器学习算法来研究纹理信息在高分辨率高光谱图像分类中的贡献。在此之上，使用基于对象的 RF 分类进行进一步分析，以研究上下文信息的贡献。最后，总体准确度、生产者/用户的准确度、基于数量和分配的分歧以及基于位置和数量的 kappa 协议与 McNemar 测试一起计算以进行准确度评估。根据我们的结果，提出的框架结合了 Gabor 纹理信息并利用基于离散小波变换的降维方法将整体分类精度提高了 9%。在单个类别中，Gabor 特征将所有类别（土壤、道路、植被、建筑物和阴影）的分类准确率分别提高到 7%、6%、6%、8%、9% 和 24%，达到生产者的准确度。除了，使用 MAP(area) 特征对道路和阴影类进行分类的用户准确度分别提高了 17% 和 10%。此外，当进行基于对象的分类时，可以看到基于像素的分类的OA进一步增加了1.07%。生产者在土壤、植被和建筑类别中的准确度提高了 2% 到 4%，用户在土壤、道路、植被和阴影类别中的准确度提高了 1% 到 3%。最后，通过基于对象的射频分类 Gabor 特征添加机载高光谱图像，使用 DWT 方法进行降维，生成准确的 LULC 地图。可以看出，基于像素的分类的OA进一步增加了1.07%。生产者在土壤、植被和建筑类别中的准确度提高了 2% 到 4%，用户在土壤、道路、植被和阴影类别中的准确度提高了 1% 到 3%。最后，通过基于对象的射频分类 Gabor 特征添加机载高光谱图像，使用 DWT 方法进行降维，生成准确的 LULC 地图。可以看出，基于像素的分类的OA进一步增加了1.07%。生产者在土壤、植被和建筑类别中的准确度提高了 2% 到 4%，用户在土壤、道路、植被和阴影类别中的准确度提高了 1% 到 3%。最后，通过基于对象的射频分类 Gabor 特征添加机载高光谱图像，使用 DWT 方法进行降维，生成准确的 LULC 地图。