Search

Classifying remotely sensed hyperspectral images (HSIs) became a computationally demanding task given the extensive information contained throughout the spectral dimension. Furthermore, burgeoning data volumes compound inherent computational and storage challenges for data processing and classification purposes. Given their distributed processing capabilities, cloud environments have emerged as feasible solutions to handle these hurdles. This encourages the development of innovative distributed classification algorithms that take full advantage of the processing capabilities of such environments. Recently, computational-efficient methods have been implemented to boost network convergence by reducing the required training calculations. This letter develops a novel cloud-based distributed implementation of the extreme learning machine (CC-ELM) algorithm for efficient HSI classification. The proposal implements a fault-tolerant and scalable computing design while avoiding traditional batch-based backpropagation. CC-ELM has been evaluated over state-of-the-art HSI classification benchmarks, yielding promising results and proving the feasibility of cloud environments for large remote sensing and HSI data volumes processing. The code available at https://github.com/mhaut/scalable-ELM-HSI. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have gained importance in hyperspectral image (HSI) segmentation for earth observation (EO) due to their unparalleled data-driven feature extraction capability. However, in many real-life situations, ground truth is not available, and the performance of unsupervised NNs is still susceptible to enhancement. To overcome this challenge, this letter presents a new loss function to improve the performance of unsupervised HSI segmentation models. The spectral loss function, $Sl$ , which can be included in different models, is based on the purity of the unmixing endmembers and the spectral similarity of the clusters provided by the NN to determine the classes. It is incorporated into a 3-D convolutional autoencoder (AE) to validate its performance on four standard HSI benchmarks. Furthermore, its performance has been qualitatively examined in a real case study, an oil spill without ground truth. The results show that $Sl$ is a breakthrough in unsupervised HS segmentation, obtaining the best overall performance and highlighting the importance of spectral signatures. Additionally, the dimensional reduction is also vital in compacting the spectral information, which facilitates its segmentation. The source code is available at https://github.com/mhaut/HSI-3DSpLoss. [ABSTRACT FROM AUTHOR]

With the recent development of remote sensing technology, large image repositories have been collected. In order to retrieve the desired images of massive remote sensing data sets effectively and efficiently, we propose a novel central cohesion gradual hashing (CCGH) mechanism for remote sensing image retrieval. First, we design a deep hashing model based on ResNet-18 which has a shallow architecture and extracts features of remote sensing imagery effectively and efficiently. Then, we propose a new training model by minimizing a central cohesion loss which guarantees that remote-sensing hash codes are as close to their hash code centers as possible. We also adopt a quantization loss which promotes that outputs are binary values. The combination of both loss functions produces highly discriminative hash codes. Finally, a gradual sign-like function is used to reduce quantization errors. By means of the aforementioned developments, our CCGH achieves state-of-the-art accuracy in the task of remote sensing image retrieval. Extensive experiments are conducted on two public remote sensing image data sets. The obtained results support the fact that our newly developed CCGH is competitive with other existing deep hashing methods. [ABSTRACT FROM AUTHOR]

Land-cover information is of paramount importance in a wide range of environmental and socioeconomic applications. Deep learning (DL) provides a large variety of potential models for extracting useful information from raw images. However, remote sensing image (RSI) classification remains a challenging goal due to the intrinsic features of the data, such as the high sample variability and lack of labeled data. This provides a challenge to the reliability of deep classifiers. In particular, convolution-based models are greatly affected by overfitting and vanishing gradient problems. To overcome these drawbacks, this letter presents a new attention-based architecture, including attention modular blocks. These blocks divide their input feature maps into several groups and split them along the channel dimension and then combine them to create an attention mask encoding global contextual information. The mask is applied to obtain a refined feature representation, strengthening those features that affect most significantly the classification and attenuating the rest. Our new method reduces significantly the number of trainable parameters. Our results, obtained using several widely used RSIs, demonstrate that the new method exhibits higher classification performance when compared to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) exhibit great performance in the task of hyperspectral image (HSI) classification. However, these models are usually overparameterized and require large amounts of training data in order to properly avoid the curse of dimensionality and the variability of spectral signatures, thus suffering from overfitting problems when very few training samples are available, due to poor generalization ability in this particular case. The traditional regularization dropout (DO) strategy has been shown to be effective in fully connected DNNs but not in convolutional-based ones. This is mainly due to the way these architectures manage the spatial information. In this letter, we introduce a new approach to improve the generalization of convolutional-based models for HSI classification. Specifically, we develop a neighboring region DO technique that selectively cuts off certain neighboring outputs, creating spatial dropped regions. Our experimental results with two well-known HSIs reveal that the newly proposed method helps to achieve better classification accuracy than the traditional DO strategy, with a low computational cost. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have become a powerful tool for remotely sensed hyperspectral image (HSI) classification due to their great generalization ability and high accuracy. However, owing to the huge amount of parameters that need to be learned and to the complex nature of HSI data itself, these approaches must deal with the important problem of overfitting, which can lead to inadequate generalization and loss of accuracy. In order to mitigate this problem, in this letter, we adopt random occlusion, a recently developed data augmentation (DA) method for training CNNs, in which the pixels of different rectangular spatial regions in the HSI are randomly occluded, generating training images with various levels of occlusion and reducing the risk of overfitting. Our results with two well-known HSIs reveal that the proposed method helps to achieve better classification accuracy with low computational cost. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]