Convolutional neural networks (CNNs) have demonstrated significant performance in various visual recognition problems in recent years. Recent research has shown that training multilayer neural networks can extensively improve the performance of hyperspectral image (HSI) classification. In this paper, we apply a triplet constraint property on a 3D CNN. This method directly learns a mapping from images to a Euclidean space in which distances directly correspond to a measure of spectral-spatial similarity. Once this embedding has been established, classification can be implemented with such embeddings as feature vectors. Moreover, we also augment the size of the training samples in different band groups. This produces different yet useful estimation of spectral-spatial characteristics of HSI data and contributes considerably in accurate classification. This method is evaluated on a new dataset and compared with several state-of-the-art models, which shows the promising potential of our method.
Road sign recognition is considered to be one of the most fascinating and interesting field of research in intelligent vehicle and machine learning. Road signs are typically placed either by the roadside or above roads. They provide important information in order to make driving safer and easier. This paper proposes an algorithm that recognizes Bangla road sign with a better percentage. The algorithm starts with capture image from real video scene, text detection from images, character segmentation and recognition of characters through shape matrix. The constructed feature vectors for each individual Bangla road sign are learned into a neural network which later classifies new instance of Bangla road sign. The promising preliminary experimental results indicate a positive potential of our algorithm.