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

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. Besides, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with several state-of-the-art methods, including the CNN framework, on three widely used HSIs. The obtained results show that Bi-CLSTM can improve the classification performance as compared to other methods

Integration of Spatial and Spectral Information for Hyperspectral Image Classification

Hyperspectral imaging has become a powerful tool in biomedical and agriculture fields in the recent years and the interest amongst researchers has increased immensely. Hyperspectral imaging combines conventional imaging and spectroscopy to acquire both spatial and spectral information from an object. Consequently, a hyperspectral image data contains not only spectral information of objects, but also the spatial arrangement of objects. Information captured in neighboring locations may provide useful supplementary knowledge for analysis. Therefore, this dissertation investigates the integration of information from both the spectral and spatial domains to enhance hyperspectral image classification performance. The major impediment to the combined spatial and spectral approach is that most spatial methods were only developed for single image band. Based on the traditional singleimage based local Geary measure, this dissertation successfully proposes a Multidimensional Local Spatial Autocorrelation (MLSA) for hyperspectral image data. Based on the proposed spatial measure, this research work develops a collaborative band selection strategy that combines both the spectral separability measure (divergence) and spatial homogeneity measure (MLSA) for hyperspectral band selection task. In order to calculate the divergence more efficiently, a set of recursive equations for the calculation of divergence with an additional band is derived to overcome the computational restrictions. Moreover, this dissertation proposes a collaborative classification method which integrates the spectral distance and spatial autocorrelation during the decision-making process. Therefore, this method fully utilizes the spatial-spectral relationships inherent in the data, and thus improves the classification performance. In addition, the usefulness of the proposed band selection and classification method is evaluated with four case studies. The case studies include detection and identification of tumor on poultry carcasses, fecal on apple surface, cancer on mouse skin and crop in agricultural filed using hyperspectral imagery. Through the case studies, the performances of the proposed methods are assessed. It clearly shows the necessity and efficiency of integrating spatial information for hyperspectral image processing

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Geographic Vector Agents from Pixels to Intelligent Processing Units

Spatial modelling methods usually utilise pixels and image objects as the fundamental processing unit to address real-world objects (geo-objects) in image space. To do this, both pixel-based and object-based approaches typically employ a linear two-staged workflow of segmentation and classification. Pixel-based methods often segment a classified image to address geo-objects in image space. In contrast, object-based approaches classify a segmented image to determine geo-objects. These methods lack the ability to simultaneously integrate the geometry and theme of geo-objects in image space. This thesis explores Vector Agents (VA) as an automated and intelligent processing unit to directly address real-world objects in the image space. A VA, is an object that can represent (non)dynamic and (ir)regular vector boundaries (Moore, 2011; Hammam et al., 2007). This aim is achieved by modelling geometry, state, and temporal changes of geo-objects in spatial space. To reach this aim, we first defined and formulated the main components of the VA, including geometry, state and neighbourhood, and their respective rules in accordance with the properties of raster datasets (e.g. satellite images), as a representation of a geographical space (the Earth). The geometry of the VA was formulated according to a directional planar graph that includes a set of spatial reasoning relationships and geometric operators, in order to implement a set of dynamic geometric behaviours, such as growing, joining or splitting. Transition rules were defined by using a classifier (e.g. Support Vector Machines (SVMs)), a set of image analysis operators (e.g. edge detection, median filter), and the characteristics of the objects in real world. VAs used the transition rules in order to find and update their states in image space. The proximity between VAs was explicitly formulated according to the minimum distance between VAs in image space. These components were then used to model the main elements of our software agent (e.g. geo-objects), namely sensors, effectors, states, rules and strategies. These elements allow a VA to perceive its environment, change its geometry and interact with other VAs to evolve inconsistency together with their thematic meaning. It also enables VAs to adjust their thematic meaning based on changes in their own attributes and those of their neighbours. We then tested this concept by using the VA to extract geo-objects from different types of raster datasets (e.g. multispectral and hyperspectral images). The results of the VA model confirmed that: (a) The VA is flexible enough to integrate thematic and geometric components of geo-objects in order to extract them directly from image space, and (b) The VA has sufficient capability to be applied in different areas of image analysis. We discuss the limitations of this work and present the possible solutions in the last chapter

Hyperspectral Image Classification Using a Spectral-Spatial Sparse Coding Model

We present a sparse coding based spectral-spatial classification model for hyperspectral image (HSI) datasets. The proposed method consists of an efficient sparse coding method in which the l1/lq regularized multi-class logistic regression technique was utilized to achieve a compact representation of hyperspectral image pixels for land cover classification. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center and compared our algorithm to a recently proposed method, Gaussian process maximum likelihood (GP-ML) classifier. Experimental results show that the proposed method can achieve significantly better performances than the GP-ML classifier when training data is limited with a compact pixel representation, leading to more efficient HSI classification systems

Automatic Image Classification for Planetary Exploration

Autonomous techniques in the context of planetary exploration can maximize scientific return and reduce the need for human involvement. This thesis work studies two main problems in planetary exploration: rock image classification and hyperspectral image classification. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. The proposed feature method is flexible and can outperform manually selected features. In order to take advantage of the unlabelled rock images, we also propose self-taught learning technique to learn the feature representation from unlabelled rock images and then apply the features for the classification of the subclass of rock images. Since combining spatial information with spectral information for classifying hyperspectral images (HSI) can dramatically improve the performance, we first propose an innovative framework to automatically generate spatial-spectral features for HSI. Two unsupervised learning methods, K-means and PCA, are utilized to learn the spatial feature bases in each decorrelated spectral band. Then spatial-spectral features are generated by concatenating the spatial feature representations in all/principal spectral bands. In the second work for HSI classification, we propose to stack the spectral patches to reduce the spectral dimensionality and generate 2-D spectral quilts. Such quilts retain all the spectral information and can result in less convolutional parameters in neural networks. Two light convolutional neural networks are then designed to classify the spectral quilts. As the third work for HSI classification, we propose a combinational fully convolutional network. The network can not only take advantage of the inherent computational efficiency of convolution at prediction time, but also perform as a collection of many paths and has an ensemble-like behavior which guarantees the robust performance