论文标题
霜冻过滤的比例不变特征提取和多层感知到高光谱图像分类
Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification
论文作者
论文摘要
高光谱图像（HSI）分类在遥感领域起着重要的作用，因为它能够提供空间和光谱信息。由于高光谱遥感技术的快速发展和增加，已经开发出许多用于HSI分类的方法，但仍然缺乏实现更好的性能。介绍了基于霜冻过滤的比例不变特征转换特征转换分类（FFSIFT-MLPC）技术，用于以更高的精度和最小的时间消耗来对高光谱图像进行分类。 FFSIFT-MLPC技术执行了三个主要过程，即使用多个层进行预处理，特征提取和分类。最初，高光谱图像分为光谱带的数量。这些频段作为输入层的输入给出。然后，在FFSIFT-MLPC技术中使用了霜滤器，以预处理输入频段，这有助于消除第一个隐藏层处的超光谱图像中的噪声。在预处理任务之后，使用高斯分布式尺度不变特征变换在第二个隐藏层中提取了超光谱图像的纹理，颜色和对象特征。在第三层隐藏层，欧几里得距离是在提取的特征和测试特征之间测量的。最后，在输出层进行特征匹配以进行超光谱图像分类。分类输出是根据光谱带（即不同颜色）产生的。用PSNR，分类精度，假阳性速率和分类时间与光谱频段数量进行实验分析。结果可以明显看出，提出的FFSIFT-MLPC技术可提高高光谱图像分类的精度，PSNR并最小化误报率和分类时间，而不是最先进的方法。
Hyperspectral image (HSI) classification plays a significant in the field of remote sensing due to its ability to provide spatial and spectral information. Due to the rapid development and increasing of hyperspectral remote sensing technology, many methods have been developed for HSI classification but still a lack of achieving the better performance. A Frost Filtered Scale-Invariant Feature Transformation based MultiLayer Perceptron Classification (FFSIFT-MLPC) technique is introduced for classifying the hyperspectral image with higher accuracy and minimum time consumption. The FFSIFT-MLPC technique performs three major processes, namely preprocessing, feature extraction and classification using multiple layers. Initially, the hyperspectral image is divided into number of spectral bands. These bands are given as input in the input layer of perceptron. Then the Frost filter is used in FFSIFT-MLPC technique for preprocessing the input bands which helps to remove the noise from hyper-spectral image at the first hidden layer. After preprocessing task, texture, color and object features of hyper-spectral image are extracted at second hidden layer using Gaussian distributive scale-invariant feature transform. At the third hidden layer, Euclidean distance is measured between the extracted features and testing features. Finally, feature matching is carried out at the output layer for hyper-spectral image classification. The classified outputs are resulted in terms of spectral bands (i.e., different colors). Experimental analysis is performed with PSNR, classification accuracy, false positive rate and classification time with number of spectral bands. The results evident that presented FFSIFT-MLPC technique improves the hyperspectral image classification accuracy, PSNR and minimizes false positive rate as well as classification time than the state-of-the-art methods.
