Evrişimsel sinir ağı ve iki-boyutlu karmaşık gabor dönüşümü kullanılarak hiperspektral görüntü sınıflandırma

Abstract

Highlights:Graphical/Tabular Abstract  Deep-learned and Gabor filtering-based feature fusion approach is proposed.  A seven layer deep convolutional network is designed to learn deep features.  Classification performance of the proposed hybrid method is comprehensively evaluated.In this paper, a new hyperspectral image classification method based on 2-dimensional complex Gabor filtering and deep convolutional neural network is proposed. To extract a set of features at multiple orientations and frequencies, the 2-D complex Gabor filtering is performed on the first three principal components of the hyperspectral image. The designed deep neural network contains seven layers with weights, including the input layer, the three convolutional layers C1, C3 and C5, the three max pooling layer S2, S4 and S6, the full connection layer F7. In this paper, the deep-learned and Gabor features are simultaneously calculated for the input samples. The proposed Gabor-filtering-assisted deep neural network classifier has been tested on two hyperspectral datasets. The experiments have showed that the proposed deep neural network yields highly accurate classification results in most cases. Figure A. The overall flowchart of the proposed Gabor filtering and ESA-based hyperspectral image classification methodPurpose: The aim of this study is to investigate the contribution of the 2-dimensional complex Gabor transform to deep convolutional neural network architectures. The performance of the proposed hybrid method has been tested on the problem of classification of hyperspectral images. Theory and Methods:Two different kinds of features are calculated. The first one is the deep-learned features. The second one is the Gabor transform features, which are extracted by applying at multiple orientations and frequencies on three principal components of the hyperspectral image. A softmax classifier is trained on the obtained hybrid features for hyperspectral image classification. Results:Hyperspectral images have successfully classified. The proposed Gabor-filtering-assisted deep learning network generally provides higher accuracy than conventional deep learning-based classifier, support vector machine classifier and elastic net representation-based classifier. Conclusion:The experimental results demonstrated that conventional deep learning architectures can still be improved using hybrid feature extraction strategies. The superiority of the proposed method comes from the combination of Gabor transform-based textural features and deep-learned features.

Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model

“…Moreover, combined with the classifcation advantages of the CNN [51,52], a fusion network for image classifcation can be constructed based on an SAE optimization, improving classifcation performance compared to traditional data processing [53,54]. Te semisupervised classifcation algorithm based on multilabeled samples and deep learning [55], with labels from both the nearest domain information and training samples [56,57], and nonlabeled samples obtained from self-teaching learning, yields an efective semisupervised hyperspectral image classifcation method [58,59]. Numerous classifcation experiments based on deep learning algorithms on a variety of hyperspectral data found that deep learning algorithms are the optimal classifcation algorithms in most cases [60][61][62][63][64][65][66].…”