[PDF] Top 20 A hyperspectral image classification algorithm based on atrous convolution

... In recent years, many researchers have used Convo- lutional Neural Networks (CNN) to classify hyper- spectral images and achieved good results. CNN learn the feature maps of samples through convolution and ... See full document

... CNN based ap- ...trieval. Convolution neural network (CNN) is very inter- ested in machine learning and has excellent performance in hyperspectral image ...a classification framework ... See full document

... PSO algorithm was introduced by Eberhart and Kennedy in 1995 ...patterns. Based on the concept of PSO inheritance, DPSO was ...an algorithm can be designed to adapt to the fitness function, adjust ... See full document

... binary classification model for ...are based on the LeNet technique of convolutional neural network (CNN) in deep ...first algorithm detects the DCT ...final algorithm detects the LSB ... See full document

... 3-D convolution neural network (CNN) classification model has also been proposed ...multiple convolution filter sizes. Residual connection and 3-D convolution have also been applied to HSIC ... See full document

... problem, based on the good performance of convolution neural network, this paper applies it to the detection of power network ...A classification method of power network icing detection image ... See full document

... content-based image retrieval (CBIR) system, training the optimal weights for the IMFs in a field- programmable gate array (FPGA) based speech recognition system [28] as well as escaping from the ... See full document

... SOMP algorithm is either conducted by setting the iteration number, ...SOMP algorithm can also be found in [3]. The proposed NN-SOMP algorithm is devised on the basis of the SOMP algorithm; it ... See full document

... The models with shallow and deep feature representations are used to determine the utility of self-taught training method [21]. Each model is trained on multiple unlabeled HSI datasets for adding spatiospectral variation ... See full document

... ABSTRACT: Hyperspectral remote sensing images contain hundreds of data ...the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such ...novel ... See full document

... pragmatic hyperspectral data acquired recently, not just on the above-mentioned open-source ...for Hyperspectral Image Classification and A Fast Dense Spectral–Spatial Convolution Network ... See full document

... for classification in the previous classification ...proposed algorithm Co-occurrence features, Run Length features, Principal Components and Independent Components are combined together to form the ... See full document

... of hyperspectral imagers provides an avenue to exploit it as a bio-optical taxonomic identification ...species based on their spectral characteristic, spectral similarity measures are usually ...positive ... See full document

... bands based on the characteristics of the original ...for hyperspectral band selection in ...saliency based band selection algorithm which puts the band vectors in the manifold space and ... See full document

... IV. Image Fusion of Selected Hyperspectral Bands The hyperspectral data present abundant multidimensional information that contains far more image bands than those that can be displayed on the ... See full document

... after label query and added into the training set. Then the neural network is retrained and such active batch selection will iterate until the budget (the upper limit of label queries) is reached (Licciardi et al., ... See full document

... and image classification, but it is almost impossible to run a large-scale CNN network structure in the platform and application scenarios with limited calculate ...each image domain independently ... See full document

... tral image classification. Hyperspectral image classification accuracy depends on the number of classes, training samples and features space ...The classification performance ... See full document

... genetic algorithm for the optimization to solve the drawback ...the classification of hyperspectral images with ...standard hyperspectral images demonstrates that the performance of the ... See full document

... pixel classification for regional crop area estimation using time series of monthly NDVI- ...data classification. In the HFNN classification, each FNN system is composed of FNN classifier and further ... See full document