Evaluación Realista de Modelos de Aprendizaje Profundo para Imágenes Hiperespectrales

Con los recientes avances realizados en el campo de Observación de la Tierra (EO), el uso de información de detección remota capturada por sensores disponibles (ubicados en plataformas aéreas y / o satelitales) ha adquirido un papel muy importante en una amplia gama de actividades humanas como la gestión del medio ambiente y recursos naturales (incluidos bosques, agua, recursos geológicos y mineralógicos), prevención de riesgos y catástrofes, planificación de espacios urbanos y rurales, detección de objetivos militares y tareas de inteligencia, etc. Hasta la fecha, se han desarrollado múltiples métodos de análisis de imágenes hiperespectrales, enmarcados en el campo del machine learning, cubriendo un rango amplio de objetivos como information retrieval, unmixing, clasificación, compresión. En los últimos años, gracias a los avances en hardware y software, un campo pequeño del machine learning denominado Deep learning ha cobrado especial interés para todas las comunidades científicas en general y en la de remote sensing en particular. Este tipo de aproximaciones en teledetección es muy interesante ya que no solo permite el procesado espectral, sino que además podemos analizar la imagen espacial o espacio-espectralmente. En este sentido, se están publicando una cantidad considerable de artículos sobre esta temática dejando casi en segundo plano métodos tan ampliamente utilizados como la Máquina de Vectores de Soporte (SVM), Regresión Logistica Multinomial (MLR), etc. Si nos centramos en el campo de las imágenes hiperespectrales, observamos que existe una gran cantidad de artículos con unos porcentajes de precisión muy altos, que en parte puede estar provocado por la manera en que la comunidad científica está trabajando con este tipo de datos y el sobre-entrenamiento. En esta charla, se dan pautas para que los investigadores puedan demostrar el poder de generalización de sus métodos espaciales o espacio-espectrales de una manera práctica y sencilla

GPU Parallel Implementation of Dual-Depth Sparse Probabilistic Latent Semantic Analysis for Hyperspectral Unmixing

Hyperspectral unmixing (HU) is an important task for remotely sensed hyperspectral (HS) data exploitation. It comprises the identification of pure spectral signatures (endmembers) and their corresponding fractional abundances in each pixel of the HS data cube. Several methods have been developed for (semi-) supervised and automatic identification of endmembers and abundances. Recently, the statistical dual-depth sparse probabilistic latent semantic analysis (DEpLSA) method has been developed to tackle the HU problem as a latent topic-based approach in which both endmembers and abundances can be simultaneously estimated according to the semantics encapsulated by the latent topic space. However, statistical models usually lead to computationally demanding algorithms and the computational time of the DEpLSA is often too high for practical use, in particular, when the dimensionality of the HS data cube is large. In order to mitigate this limitation, this article resorts to graphical processing units (GPUs) to provide a new parallel version of the DEpLSA, developed using the NVidia compute device unified architecture. Our experimental results, conducted using four well-known HS datasets and two different GPU architectures (GTX 1080 and Tesla P100), show that our parallel versions of the DEpLSA and the traditional pLSA approach can provide accurate HU results fast enough for practical use, accelerating the corresponding serial versions in at least 30x in the GTX 1080 and up to 147x in the Tesla P100 GPU, which are quite significant acceleration factors that increase with the image size, thus allowing for the possibility of the fast processing of massive HS data repositories

Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification

Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral-spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral-spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral-spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods

A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution

Super-resolution (SR) brings an excellent opportunity to improve a wide range of different remote sensing applications. SR techniques are concerned about increasing the image resolution while providing finer spatial details than those captured by the original acquisition instrument. Therefore, SR techniques are particularly useful to cope with the increasing demand remote sensing imaging applications requiring fine spatial resolution. Even though different machine learning paradigms have been successfully applied in SR, more research is required to improve the SR process without the need of external high-resolution (HR) training examples. This paper proposes a new convolutional generator model to super-resolve low-resolution (LR) remote sensing data from an unsupervised perspective. That is, the proposed generative network is able to initially learn relationships between the LR and HR domains throughout several convolutional, downsampling, batch normalization, and activation layers. Then, the data are symmetrically projected to the target resolution while guaranteeing a reconstruction constraint over the LR input image. An experimental comparison is conducted using 12 different unsupervised SR methods over different test images. Our experiments reveal the potential of the proposed approach to improve the resolution of remote sensing imagery

Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification

Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models typically limit their application in on-board remote sensing scenarios. In this context, low-power consumption architectures are promising platforms that may provide acceptable on-board computing capabilities to achieve satisfactory classification results with reduced energy demand. For instance, the new NVIDIA Jetson Tegra TX2 device is an efficient solution for on-board processing applications using deep-learning (DL) approaches. So far, very few efforts have been devoted to exploiting this or other similar computing platforms in on-board remote sensing procedures. This letter explores the use of low-power consumption architectures and DL algorithms for HSI classification. The conducted experimental study reveals that the NVIDIA Jetson Tegra TX2 device offers a good choice in terms of performance, cost, and energy consumption for on-board HSI classification tasks

Remote Sensing Single-Image Superresolution Based on a Deep Compendium Model

This letter introduces a novel remote sensing single-image superresolution (SR) architecture based on a deep efficient compendium model. The current deep learning-based SR trend stands for using deeper networks to improve the performance. However, this practice often results in the degradation of visual results. To address this issue, the proposed approach harmonizes several different improvements on the network design to achieve state-of-the-art performance when superresolving remote sensing imagery. On the one hand, the proposal combines residual units and skip connections to extract more informative features on both local and global image areas. On the other hand, it makes use of parallelized 1x1 convolutional filters (network in network) to reconstruct the superresolved result while reducing the information loss through the network. Our experiments, conducted using seven different SR methods over the well-known UC Merced remote sensing data set, and two additional GaoFen-2 test images, show that the proposed model is able to provide competitive advantages

Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion

Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of probabilistic latent semantic analysis (pLSA) and latent Dirichlet allocation to remote sensing synthetic aperture radar (SAR) and multispectral imaging (MSI) unsupervised land-cover categorization. Then, a novel pLSA-based image fusion approach is presented, which pursues to uncover multimodal feature patterns from SAR and MSI data in order to effectively fuse and categorize Sentinel-1 and Sentinel-2 remotely sensed data. Experiments conducted over two different data sets reveal the advantages of the proposed approach for unsupervised land-cover categorization tasks

A Comparative Study of Techniques for Hyperspectral Image Classification

[EN] Hyperspectral images are very important in many Earth Observation programs. The large amount of information is contained in hyperspectral images (hundreds of narrow and continuous spectral channels) is very useful for applications in which the characterization of the Earth surface materials is relevant. This is due to the fact that each observed element can be uniquely characterized by its spectral signature, for instance in precision agriculture, urban planning or detection/prevention of natural disasters, among others. However, the large dimensionality of hyperspectral images represents a challenge for analysis algorithms, both from the storage and processing viewpoints, resulting from data variability and correlation. Several algorithms have been proposed in the literature for the analysis of hyperspectral images. In this paper, we review the most popular techiques for hyperspectral classification. These techniques are inter-compared using three publicly available hyperspectral data sets.[ES] Las imágenes hiperespectrales constituyen el núcleo de varios programas de observación remota de la Tierra. La cantidad de información que contienen estas imágenes, formadas por cientos de canales espectrales estrechos y casi continuos, resulta de gran utilidad en aplicaciones en las que la caracterización de los materiales observados en la superficie terrestre resulta de gran relevancia. Esto se debe a la posibilidad de caracterizar de forma inequívoca cada material a través de su firma espectral. Algunas de estas aplicaciones son la agricultura de precisión, la planificación de espacios urbanos, o la prevención y seguimiento de desastres naturales. Sin embargo, la gran dimensión de las imágenes hiperespectrales supone un reto en su tratamiento, almacenamiento y procesamiento, debido a la gran variabilidad espectral y la correlación existente en los datos. En la literatura se han desarrollado múltiples algoritmos de análisis de imágenes hiperespectrales. En este artículo revisamos los algoritmos más utilizados para la clasificación de este tipo de imágenes, realizando experimentos con tres imágenes públicas y presentando una comparativa entre los métodos más ampliamente utilizados en este campo.Este trabajo ha sido financiado por el Ministerio de Educación (Resolución de 26 de diciembre de 2014 y de 19 de noviembre de 2015, de la Secretaría de Estado de Educación, Formación Profesional y Universidades, por la que se convocan ayudas para la formación de profesorado universitario, de los subprogramas de Formación y de Movilidad incluidos en el Programa Estatal de Promoción del Talento y su Empleabilidad, en el marco del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016). Este trabajo también ha sido financiado por la Junta de Extremadura (decreto 279/2014, ayudas para la realización de actividades de investigación y desarrollo tecnológico, de divulgación y de transferencia de conocimiento por los Grupos de Investigación de Extremadura, Ref.GR15005) y por el MINECO (TIN2015-63646-C5-5-R).Paoletti, ME.; Haut, JM.; Plaza, J.; Plaza, A. (2019). Estudio Comparativo de Técnicas de Clasificación de Imágenes Hiperespectrales. Revista Iberoamericana de Automática e Informática. 16(2):129-137. https://doi.org/10.4995/riai.2019.11078SWORD12913716

Capsule Networks for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral-spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral-spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton's capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral-spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral-spatial capsules, which are able to learn HSI spectral-spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral-spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time

Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification

Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral-spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral-spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral-spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods