Deep Learning for Remote Sensing Image Processing

Remote sensing images have many applications such as ground object detection, environmental change monitoring, urban growth monitoring and natural disaster damage assessment. As of 2019, there were roughly 700 satellites listing “earth observation” as their primary application. Both spatial and temporal resolutions of satellite images have improved consistently in recent years and provided opportunities in resolving fine details on the Earth\u27s surface. In the past decade, deep learning techniques have revolutionized many applications in the field of computer vision but have not fully been explored in remote sensing image processing. In this dissertation, several state-of-the-art deep learning models have been investigated and customized for satellite image processing in the applications of landcover classification and ground object detection. First, a simple and effective Convolutional Neural Network (CNN) model is developed to detect fresh soil from tunnel digging activities near the U.S. and Mexico border by using pansharpened synthetic hyperspectral images. These tunnels’ exits are usually hidden under warehouses and are used for illegal activities, for example, by drug dealers. Detecting fresh soil nearby is an indirect way to search for these tunnels. While multispectral images have been used widely and regularly in remote sensing since the 1970s, with the fast advances in hyperspectral sensors, hyperspectral imagery is becoming popular. A combination of 80 synthetic hyperspectral channels with the original eight multispectral channels collected by the WorldView-2 satellite are used by CNN to detect fresh soil. Experimental results show that detection performance can be significantly improved by the combination of synthetic hyperspectral images with those original multispectral channels. Second, an end-to-end, pixel-level Fully Convolutional Network (FCN) model is implemented to estimate the number of refugee tents in the Rukban area near the Syrian-Jordan border using high-resolution multispectral satellite images collected by WordView-2. Rukban is a desert area crossing the border between Syria and Jordan, and thousands of Syrian refugees have fled into this area since the Syrian civil war in 2014. In the past few years, the number of refugee shelters for the forcibly displaced Syrian refugees in this area has increased rapidly. Estimating the location and number of refugee tents has become a key factor in maintaining the sustainability of the refugee shelter camps. Manually counting the shelters is labor-intensive and sometimes prohibitive given the large quantities. In addition, these shelters/tents are usually small in size, irregular in shape, and sparsely distributed in a very large area and could be easily missed by the traditional image-analysis techniques, making the image-based approaches also challenging. The FCN model is also boosted by transfer learning with the knowledge in the pre-trained VGG-16 model. Experimental results show that the FCN model is very accurate and has less than 2% of error. Last, we investigate the Generative Adversarial Networks (GAN) to augment training data to improve the training of FCN model for refugee tent detection. Segmentation based methods like FCN require a large amount of finely labeled images for training. In practice, this is labor-intensive, time consuming, and tedious. The data-hungry problem is currently a big hurdle for this application. Experimental results show that the GAN model is a better tool as compared to traditional methods for data augmentation. Overall, our research made a significant contribution to remote sensing image processin

Weighted Chebyshev Distance Algorithms for Hyperspectral Target Detection and Classification Applications

In this study, an efficient spectral similarity method referred to as Weighted Chebyshev Distance (WCD) is introduced for supervised classification of hyperspectral imagery (HSI) and target detection applications. The WCD is based on a simple spectral similarity based decision rule using limited amount of reference data. The estimation of upper and lower spectral boundaries of spectral signatures for all classes across spectral bands is referred to as a vector tunnel (VT). To obtain the reference information, the training signatures are provided randomly from existing data for a known class. After determination of the parameters of the WCD algorithm with the training set, classification or detection procedures are accomplished at each pixel. The comparative performances of the algorithms are tested under various cases. The decision criterion for classification of an input vector is based on choosing its class corresponding to the narrowest VT that the input vector fits in to. This is also shown to be approximated by the WCD in which the weights are chosen as an inverse power of the generalized standard deviation per spectral band. In computer experiments, the WCD classifier is compared with the Euclidian Distance (ED) classifier and the Spectral Angle Map (SAM) classifier. The WCD algorithm is also used for HSI target detection purpose. Target detection problem is considered as a two-class classification problem. The WCD is characterized only by the target class spectral information. Then, this method is compared with ED, SAM, Spectral Matched Filter (SMF), Adaptive Cosine Estimator (ACE) and Support Vector Machine (SVM) algorithms. During these studies, threshold levels are evaluated based on the Receiver Operating Characteristic Curves (ROC)

Large kernel spectral and spatial attention networks for hyperspectral image classification.

Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Remote sensing bio-control damage on aquatic invasive alien plant species

Aquatic Invasive Alien Plant (AIAP) species are a major threat to freshwater ecosystems, placing great strain on South Africa’s limited water resources. Bio-control programmes have been initiated in an effort to mitigate the negative environmental impacts associated with their presence in non-native areas. Remote sensing can be used as an effective tool to detect, map and monitor bio-control damage on AIAP species. This paper  reconciles previous and current research concerning the application of remote sensing to detect and map bio-control damage on AIAP species. Initially, the spectral characteristics of bio-control damage are  described. Thereafter, the potential of remote sensing chlorophyll content and chlorophyll fluorescence as  pre-visual indicators of bio-control damage are reviewed and synthesised. The utility of multispectral and  hyperspectral sensors for mapping different severities of bio-control damage are also discussed. Popular  machine learning algorithms that offer operational potential to classify bio-control damage are proposed. This paper concludes with the challenges of remote sensing bio-control damage as well as proposes  recommendations to guide future research to successfully detect and map bio-control damage on AIAP  species

Spatial Analysis for Landscape Changes

Recent increasing trends of the occurrence of natural and anthropic processes have a strong impact on landscape modification, and there is a growing need for the implementation of effective instruments, tools, and approaches to understand and manage landscape changes. A great improvement in the availability of high-resolution DEMs, GIS tools, and algorithms of automatic extraction of landform features and change detections has favored an increase in the analysis of landscape changes, which became an essential instrument for the quantitative evaluation of landscape changes in many research fields. One of the most effective ways of investigating natural landscape changes is the geomorphological one, which benefits from recent advances in the development of digital elevation model (DEM) comparison software and algorithms, image change detection, and landscape evolution models. This Special Issue collects six papers concerning the application of traditional and innovative multidisciplinary methods in several application fields, such as geomorphology, urban and territorial systems, vegetation restoration, and soil science. The papers include multidisciplinary studies that highlight the usefulness of quantitative analyses of satellite images and UAV-based DEMs, the application of Landscape Evolution Models (LEMs) and automatic landform classification algorithms to solve multidisciplinary issues of landscape changes. A review article is also presented, dealing with the bibliometric analysis of the research topic