Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion

Land cover datasets are essential for modeling Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, and finding quality satellite remote sensing datasets to produce such maps is difficult due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The datasets from hyperspectral, multispectral, synthetic aperture radar (SAR) platforms, and terrain datasets were fused together using unsupervised and supervised classification techniques over a 343 km2 region to generate high-resolution (5 m) vegetation type maps. A unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters and a quantitative technique to add supervision to the unlabeled clusters was employed, producing a fully labeled vegetation map. Deep neural networks (DNNs) were developed using multi-sensor remote sensing datasets to map vegetation distributions using the original labels and the labels produced by the unsupervised method for training [1]. Fourteen different combinations of remote sensing imagery were analyzed to explore the optimization of multi-sensor remote sensing fusion. To validate the resulting DNN-based vegetation maps, field vegetation observations were conducted at 30 plots during the summer of 2016 and developed vegetation maps were evaluated against them for accuracy. Our analysis showed that the DNN models based on hyperspectral EO-1 Hyperion, integrated with the other remote sensing data, provided the most accurate mapping of vegetation types, increasing the average validation score from 0.56 to 0.70 based on field observation-based vegetation. REFERENCES: 1. Langford, Z. L., Kumar, J., and Hoffman, F. M., "Convolutional Neural Network Approach for Mapping Arctic Vegetation Using Multi-Sensor Remote Sensing Fusion," 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, 2017, pp. 322-331. doi: 10.1109/ICDMW.2017.4

Segmentation and Classification of Multimodal Imagery

Segmentation and classification are two important computer vision tasks that transform input data into a compact representation that allow fast and efficient analysis. Several challenges exist in generating accurate segmentation or classification results. In a video, for example, objects often change the appearance and are partially occluded, making it difficult to delineate the object from its surroundings. This thesis proposes video segmentation and aerial image classification algorithms to address some of the problems and provide accurate results. We developed a gradient driven three-dimensional segmentation technique that partitions a video into spatiotemporal objects. The algorithm utilizes the local gradient computed at each pixel location together with the global boundary map acquired through deep learning methods to generate initial pixel groups by traversing from low to high gradient regions. A local clustering method is then employed to refine these initial pixel groups. The refined sub-volumes in the homogeneous regions of video are selected as initial seeds and iteratively combined with adjacent groups based on intensity similarities. The volume growth is terminated at the color boundaries of the video. The over-segments obtained from the above steps are then merged hierarchically by a multivariate approach yielding a final segmentation map for each frame. In addition, we also implemented a streaming version of the above algorithm that requires a lower computational memory. The results illustrate that our proposed methodology compares favorably well, on a qualitative and quantitative level, in segmentation quality and computational efficiency with the latest state of the art techniques. We also developed a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are fused at the initial layers of deep neural networks as opposed to final layers. The early fusion architecture has fewer parameters and thereby reduces the computational time and GPU memory during training and inference. We also introduce a composite architecture that fuses features throughout the network. The methods were validated on four different datasets: ISPRS Potsdam, Vaihingen, IEEE Zeebruges, and Sentinel-1, Sentinel-2 dataset. For the Sentinel-1,-2 datasets, we obtain the ground truth labels for three classes from OpenStreetMap. Results on all the images show early fusion, specifically after layer three of the network, achieves results similar to or better than a decision level fusion mechanism. The performance of the proposed architecture is also on par with the state-of-the-art results

Large Area Land Cover Mapping Using Deep Neural Networks and Landsat Time-Series Observations

This dissertation focuses on analysis and implementation of deep learning methodologies in the field of remote sensing to enhance land cover classification accuracy, which has important applications in many areas of environmental planning and natural resources management. The first manuscript conducted a land cover analysis on 26 Landsat scenes in the United States by considering six classifier variants. An extensive grid search was conducted to optimize classifier parameters using only the spectral components of each pixel. Results showed no gain in using deep networks by using only spectral components over conventional classifiers, possibly due to the small reference sample size and richness of features. The effect of changing training data size, class distribution, or scene heterogeneity were also studied and we found all of them having significant effect on classifier accuracy. The second manuscript reviewed 103 research papers on the application of deep learning methodologies in remote sensing, with emphasis on per-pixel classification of mono-temporal data and utilizing spectral and spatial data dimensions. A meta-analysis quantified deep network architecture improvement over selected convolutional classifiers. The effect of network size, learning methodology, input data dimensionality and training data size were also studied, with deep models providing enhanced performance over conventional one using spectral and spatial data. The analysis found that input dataset was a major limitation and available datasets have already been utilized to their maximum capacity. The third manuscript described the steps to build the full environment for dataset generation based on Landsat time-series data using spectral, spatial, and temporal information available for each pixel. A large dataset containing one sample block from each of 84 ecoregions in the conterminous United States (CONUS) was created and then processed by a hybrid convolutional+recurrent deep network, and the network structure was optimized with thousands of simulations. The developed model achieved an overall accuracy of 98% on the test dataset. Also, the model was evaluated for its overall and per-class performance under different conditions, including individual blocks, individual or combined Landsat sensors, and different sequence lengths. The analysis found that although the deep model performance per each block is superior to other candidates, the per block performance still varies considerably from block to block. This suggests extending the work by model fine-tuning for local areas. The analysis also found that including more time stamps or combining different Landsat sensor observations in the model input significantly enhances the model performance

Beyond Measurement: {E}xtracting Vegetation Height from High Resolution Imagery with Deep Learning

Measuring and monitoring the height of vegetation provides important insights into forest age and habitat quality. These are essential for the accuracy of applications that are highly reliant on up-to-date and accurate vegetation data. Current vegetation sensing practices involve ground survey, photogrammetry, synthetic aperture radar (SAR), and airborne light detection and ranging sensors (LiDAR). While these methods provide high resolution and accuracy, their hardware and collection effort prohibits highly recurrent and widespread collection. In response to the limitations of current methods, we designed Y-NET, a novel deep learning model to generate high resolution models of vegetation from highly recurrent multispectral aerial imagery and elevation data. Y-NET’s architecture uses convolutional layers to learn correlations between different input features and vegetation height, generating an accurate vegetation surface model (VSM) at 1×1 m resolution. We evaluated Y-NET on 235 km2 of the East San Francisco Bay Area and find that Y-NET achieves low error from LiDAR when tested on new locations. Y-NET also achieves an R2 of 0.83 and can effectively model complex vegetation through side-by-side visual comparisons. Furthermore, we show that Y-NET is able to identify instances of vegetation growth and mitigation by comparing aerial imagery and LiDAR collected at different times

Advancing Land Cover Mapping in Remote Sensing with Deep Learning

Automatic mapping of land cover in remote sensing data plays an increasingly significant role in several earth observation (EO) applications, such as sustainable development, autonomous agriculture, and urban planning. Due to the complexity of the real ground surface and environment, accurate classification of land cover types is facing many challenges. This thesis provides novel deep learning-based solutions to land cover mapping challenges such as how to deal with intricate objects and imbalanced classes in multi-spectral and high-spatial resolution remote sensing data. The first work presents a novel model to learn richer multi-scale and global contextual representations in very high-resolution remote sensing images, namely the dense dilated convolutions' merging (DDCM) network. The proposed method is light-weighted, flexible and extendable, so that it can be used as a simple yet effective encoder and decoder module to address different classification and semantic mapping challenges. Intensive experiments on different benchmark remote sensing datasets demonstrate that the proposed method can achieve better performance but consume much fewer computation resources compared with other published methods. Next, a novel graph model is developed for capturing long-range pixel dependencies in remote sensing images to improve land cover mapping. One key component in the method is the self-constructing graph (SCG) module that can effectively construct global context relations (latent graph structure) without requiring prior knowledge graphs. The proposed SCG-based models achieved competitive performance on different representative remote sensing datasets with faster training and lower computational cost compared to strong baseline models. The third work introduces a new framework, namely the multi-view self-constructing graph (MSCG) network, to extend the vanilla SCG model to be able to capture multi-view context representations with rotation invariance to achieve improved segmentation performance. Meanwhile, a novel adaptive class weighting loss function is developed to alleviate the issue of class imbalance commonly found in EO datasets for semantic segmentation. Experiments on benchmark data demonstrate the proposed framework is computationally efficient and robust to produce improved segmentation results for imbalanced classes. To address the key challenges in multi-modal land cover mapping of remote sensing data, namely, 'what', 'how' and 'where' to effectively fuse multi-source features and to efficiently learn optimal joint representations of different modalities, the last work presents a compact and scalable multi-modal deep learning framework (MultiModNet) based on two novel modules: the pyramid attention fusion module and the gated fusion unit. The proposed MultiModNet outperforms the strong baselines on two representative remote sensing datasets with fewer parameters and at a lower computational cost. Extensive ablation studies also validate the effectiveness and flexibility of the framework

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Extracting Information from Multimodal Remote Sensing Data for Sea Ice Characterization

Remote sensing is the discipline that studies acquisition, preparation and analysis of spectral, spatial and temporal properties of objects without direct touch or contact. It is a field of great importance to understanding the climate system and its changes, as well as for conducting operations in the Arctic. A current challenge however is that most sensory equipment can only capture one or fewer of the characteristics needed to accurately describe ground objects through their temporal, spatial, spectral and radiometric resolution characteristics. This in turn motivates the fusing of complimentary modalities for potentially improved accuracy and stability in analysis but it also leads to problems when trying to merge heterogeneous data with different statistical, geometric and physical qualities. Another concern in the remote sensing of arctic regions is the scarcity of high quality labeled data but simultaneous abundance of unlabeled data as the gathering of labeled data can be both costly and time consuming. It could therefore be of great value to explore routes that can automate this process in ways that target both the situation regarding available data and the difficulties from fusing of heterogeneous multimodal data. To this end Semi-Supervised methods were considered for their ability to leverage smaller amounts of carefully labeled data in combination with more widely available unlabeled data in achieving greater classification performance. Strengths and limitations of three algorithms for real life applications are assessed through experiments on datasets from arctic and urban areas. The first two algorithms, Deep Semi-Supervised Label Propagation (LP) and MixMatch Holistic SSL (MixMatch), consider simultaneous processing of multimodal remote sensing data with additional extracted Gray Level Co-occurrence Matrix texture features for image classification. LP trains in alternating steps of supervised learning on potentially pseudolabeled data and steps of deciding new labels through node propagation while MixMatch mixes loss terms from several leading algorithms to gain their respective benefits. Another method, Graph Fusion Merriman Bence Osher (GMBO), explores processing of modalities in parallel by constructing a fused graph from complimentary input modalities and Ginzburg-Landau minimization on an approximated Graph Laplacian. Results imply that inclusion of extracted GLCM features could be beneficial for classification of multimodal remote sensing data, and that GMBO has merits for operational use in the Arctic given that certain data prerequisites are met

Status and trends of wetland studies in Canada using remote sensing technology with a focus on wetland classification: a bibliographic analysis

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.Peer ReviewedPostprint (published version