Hyperspectral Image Classification: An Analysis Employing CNN, LSTM, Transformer, and Attention Mechanism

Hyperspectral images contain tens to hundreds of bands, implying a high spectral resolution. This high spectral resolution allows for obtaining a precise signature of structures and compounds that make up the captured scene. Among the types of processing that may be applied to Hyperspectral Images, classification using machine learning models stands out. The classification process is one of the most relevant steps for this type of image. It can extract information using spatial and spectral information and spatial-spectral fusion. Artificial Neural Network models have been gaining prominence among existing classification techniques. They can be applied to data with one, two, or three dimensions. Given the above, this work evaluates Convolutional Neural Network models with one, two, and three dimensions to identify the impact of classifying Hyperspectral Images with different types of convolution. We also expand the comparison to Recurrent Neural Network models, Attention Mechanism, and the Transformer architecture. Furthermore, a novelty pre-processing method is proposed for the classification process to avoid generating data leaks between training, validation, and testing data. The results demonstrated that using 1 Dimension Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and Transformer architectures reduces memory consumption and sample processing time and maintain a satisfactory classification performance up to 99% accuracy on larger datasets. In addition, the Transfomer architecture can approach the 2D-CNN and 3D-CNN architectures in accuracy using only spectral information. The results also show that using two or three dimensions convolution layers improves accuracy at the cost of greater memory consumption and processing time per sample. Furthermore, the pre-processing methodology guarantees the disassociation of training and testing data.N/

UAV data modeling for geoinformation update

A dissertação visa avaliar a relevância e o desempenho dos dados obtidos por Veículos Aéreos Não Tripulados (VANT) na atualização de Geoinformação. Os dados obtidos por VANT serão utilizados quer em conjunto com outros dados – obtidos por plataformas tradicionais de deteção remota –, quer isoladamente, recorrendo à técnica de Structure from Motion (SfM), para gerar o modelo digital de superfície e os ortomosaicos de alta precisão em diferentes momentos. Para a avaliação da precisão dos dados, os modelos digitais de terreno serão comparados. Por outro lado, os dados e informação gerados permitirão atualizar Geoinformação e quantificar as mudanças ocorridas no uso e ocupação do solo. Os resultados irão alimentar a discussão crítica da ação antrópica nos aglomerados urbanos e as propostas de intervenção.The dissertation aims to assess the relevance and performance of data obtained by Unmanned Aerial Vehicles (UAVs) in updating Geoinformation. The data obtained by UAVs will be used either in conjunction with other data – obtained by traditional remote sensing platforms – or on its own, using the Structure from Motion (SfM) technique, to generate high-precision digital surface models and orthomosaics at different times. For the accuracy assessment of the data, the digital terrain models will be compared. On the other hand, the data and information generated will make it possible to update Geoinformation and quantify changes in land use and occupation. The results will feed the critical discussion of anthropic action in urban areas and intervention proposals

Classification of Hyperspectral Data Using an AdaBoostSVM Technique Applied on Band Clusters

Supervised classification of hyperspectral image data using conventional statistical classification methods is difficult because a sufficient number of training samples is often not available for the wide range of spectral bands. In addition, spectral bands are usually highly correlated and contain data redundancies because of the short spectral distance between the adjacent bands. To address these limitations, a multiple classifier system based on Adaptive Boosting (AdaBoost) is proposed and evaluated to classify hyperspectral data. In this method, the hyperspectral datasets are first split into several band clusters based on the similarities between the contiguous bands. In an AdaBoost classification system, the redundant and noninformative bands in each cluster are then removed using an optimal band selection technique. Next, a support vector machine (SVM) is applied to each refined cluster based on the classification results of previous clusters, and the results of these classifiers are fused using the weights obtained from the AdaBoost processing. Experimental results with standard hyperspectral datasets clearly demonstrate the superiority of the proposed algorithm with respect to both global and class accuracies, when compared to another ensemble classifiers such as simple majority voting and Naïve Bayes to combine decisions from each cluster, a standard SVM applied on the selected bands of entire datasets and on all the spectral bands. More specifically, the proposed method performs better than other approaches, especially in datasets which contain classes with greater complexity and fewer available training samples