Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Automatic Features Extraction From Time Series Of Passive Microwave Images For Snowmelt Detection Using Deep-Learning – A Bidirectional Long-Short Term Memory Autoencoder (Bi-Lstm-Ae) Approach.

The Antarctic surface snowmelt is prone to the polar climate and is common in its coastal regions. With about 90 percent of the planet\u27s glaciers, if all of the Antarctica glaciers melted, sea levels will rise about 58 meters around the planet. The development of an effective automated ice-sheet snowmelt monitoring system is therefore crucial. Microwave remote sensing instruments, on the one hand, are very sensitive to snowmelt and can see day and night through clouds, allowing us to distinguish melting from dry snow and to better understand when, where, and for how long melting has taken place. On the other hand, deep-learning (DL) algorithms, which can learn from linear and non-linear data in a hierarchical way robust representations and discriminative features, have recently become a hotspot in the field of machine learning and have been implemented with success in the geospatial and remote sensing field. This study demonstrates that deep learning, particularly long-short memory autoencoder architecture (LSTM-AE) is capable of fully exploiting archives of passive microwave time series data. In this thesis, An LSTM-AE algorithm was used to reduce and capture essential relationships between attributes stored as brightness temperature within pixel time series and k-means clustering is applied to cluster the leaned representations. The final output map highlights the melt extent in Antarctica

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

Anomalien havaitseminen GNSS signaaleissa kompleksiarvoisilla LSTM neuroverkoilla

Today, Global Navigation Satellite Systems (GNSS) provide services that many critical systems [1] as well as normal users, need in everyday life. These signals are threatened by unintentional and intentional interference. The received satellite signals are complex-valued by nature, however, state-of-the-art anomaly detection approaches operate in the real domain. Changing the anomaly detection into the complex domain allows for preserving the phase component of the signal data. In this thesis, I developed and tested a fully complex-valued Long Short-Term Memory (LSTM) based autoencoder for anomaly detection. I also developed a method for scaling of complex-numbers that forces both real and imaginary units into the range [-1,1] and does not change the direction of a complex vector. The model is trained and tested both in the time and frequency domains, and the frequency domain is divided into two parts: real and complex domain. The developed model’s training data consists only of clean sample data, and the output of the model is the reconstruction of the model’s input. In testing, it can be determined whether the output is clean or anomalous based on the reconstruction error and the computed threshold value. The results show that the autoencoder model in the real domain outperforms the model trained in the complex domain. This does not indicate that the anomaly detection in the complex domain does not work; rather, the model’s architecture needs improvements, and the amount of training data must be increased to reduce the overfitting of the complex domain and thus improve the anomaly detection capability. It was also investigated that some anomalous sample sequences contain a few large valued spikes while other values in the same data snapshot are smaller. After scaling, the values other than in the spikes get closer to zero. This phenomenon causes small reconstruction errors in the model and yields false predictions in the complex domain

Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder

Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to capture correlation structures among exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional multivariate distribution as a generating process. As a result, the variational lower bound of the joint likelihood can be optimized via a conditional variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was motivated by two real-world applications in computational sustainability: one studies the spatial correlation among multiple bird species using the eBird data and the other models multi-dimensional landscape composition and human footprint in the Amazon rainforest with satellite images. We show that MEDL_CVAE captures rich dependency structures, scales better than previous methods, and further improves on the joint likelihood taking advantage of very large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall

UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation

Change detection (CD) by comparing two bi-temporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention in the community. However, existing unsupervised CD approaches rarely consider the seasonal and style differences incurred by the illumination and atmospheric conditions in multi-temporal images. To this end, we propose a change detection with domain shift setting for remote sensing images. Furthermore, we present a novel unsupervised CD method using a light-weight transformer, called UCDFormer. Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency. After image translation, we can generate the difference map between the translated before-event image and the original after-event image. Then, a novel reliable pixel extraction module is proposed to select significantly changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy c-means clustering and adaptive threshold. Finally, a binary change map is obtained based on these selected pixel pairs and a binary classifier. Experimental results on different unsupervised CD tasks with seasonal and style changes demonstrate the effectiveness of the proposed UCDFormer. For example, compared with several other related methods, UCDFormer improves performance on the Kappa coefficient by more than 12\%. In addition, UCDFormer achieves excellent performance for earthquake-induced landslide detection when considering large-scale applications. The code is available at \url{https://github.com/zhu-xlab/UCDFormer}Comment: 16 pages, 7 figures, IEEE Transactions on Geoscience and Remote Sensin

Unsupervised Automatic Detection Of Transient Phenomena In InSAR Time-Series using Machine Learning

The detection and measurement of transient episodes of crustal deformation from global InSAR datasets are crucial for a wide range of solid earth and natural hazard applications. But the large volumes of unlabelled data captured by satellites preclude manual systematic analysis, and the small signal-to-noise ratio makes the task difficult. In this thesis, I present a state-of-the-art, unsupervised and event-agnostic deep-learning based approach for the automatic identification of transient deformation events in noisy time-series of unwrapped InSAR images. I adopt an anomaly detection framework that learns the ‘normal’ spatio-temporal pattern of noise in the data, and which therefore identifies any transient deformation phenomena that deviate from this pattern as ‘anomalies’. The deep-learning model is built around a bespoke autoencoder that includes convolutional and LSTM layers, as well as a neural network which acts as a bridge between the encoder and decoder. I train our model on real InSAR data from northern Turkey and find it has an overall accuracy and true positive rate of around 85% when trying to detect synthetic deformation signals of length-scale > 350 m and magnitude > 4 cm. Furthermore, I also show the method can detect (1) a real Mw 5.7 earthquake in InSAR data from an entirely different region- SW Turkey, (2) a volcanic deformation in Domuyo, Argentina, (3) a synthetic slow-slip event and (4) an interseismic deformation around NAF in a descending frame in northern Turkey. Overall I show that my method is suitable for automated analysis of large, global InSAR datasets, and for robust detection and separation of deformation signals from nuisance signals in InSAR data