Pixel-wise segmentation of SAR imagery using encoder-decoder network and fully-connected CRF

Synthetic Aperture Radar (SAR) image segmentation is an important step in SAR image interpretation. Common Patch-based methods treat all the pixels within the patch as a single category and do not take the label consistency between neighbor patches into consideration, which makes the segmentation results less accurate. In this paper, we use an encoder-decoder network to conduct pixel-wise segmentation. Then, in order to make full use of the contextual information between patches, we use fully-connected conditional random field to optimize the combined probability map output from encoder-decoder network. The testing results on our SAR data set shows that our method can effectively maintain contextual information of pixels and achieve better segmentation results

Near real-time monitoring of cassava cultivation area

Remote sensing technologies and deep learning/machine learning approaches play valuable roles in crop inventory, yield estimation, cultivated area estimation, and crop status monitoring. Satellite-based remote sensing has led to increased spatial and temporal resolution, leading to a better quality of land-cover mapping (greater precision, and detail in the number of land cover classes). In this work, we propose to use a long short-term memory neural network (LSTM), an advanced technical model adapted from artificial neural networks (ANN) to estimate cassava cultivation area in southern Laos. LSTM is a modified version of a Recurrent Neural Network (RNN) that uses internal memory to store the information received prior to a given time. This property of LSTMs makes them advantageous for time series regression. We employ Landsat-7/8 and Sentinel-2 time-series datasets and crop phenology information to identify and classify cassava fields using multi-sources remote sensing time-series in a highly fragmented landscape. The results indicate an overall accuracy of > 89% for cassava and > 84% for all-class (barren, bush/grassland, cassava, coffee, forest, seasonal, and water) validating the feasibility of the proposed method. This study demonstrates the potential of LSTM approaches for crop classification using multi-temporal, multi-sources remote sensing time series

Swarm intelligence in cooperative environments: N-step dynamic tree search algorithm extended analysis

Reinforcement learning tree-based planning methods have been gaining popularity in the last few years due to their success in single-agent domains, where a perfect simulator model is available, e.g., Go and chess strategic board games. This paper pretends to extend tree search algorithms to the multi-agent setting in a decentralized structure, dealing with scalability issues and exponential growth of computational resources. The N-Step Dynamic Tree Search combines forward planning and direct temporal-difference updates, outperforming markedly state-of-the-art algorithms such as Q-Learning and SARSA. Future state transitions and rewards are predicted with a model built and learned from real interactions between agents and the environment. As an extension of previous work, this paper analyses the developed algorithm in the Hunter-Pursuit cooperative game against intelligent evaders. The N-Step Dynamic Tree Search aims to adapt the most successful single-agent learning methods to the multi-agent boundaries and demonstrates to be a remarkable advance compared to conventional temporal-difference techniques.Engineering and Physical Sciences Research Council (EPSRC): 2454254. BAE System

Urban Material Classification Using Spectral and Textural Features Retrieved from Autoencoders

Classification of urban materials using remote sensing data, in particular hyperspectral data, is common practice. Spectral libraries can be utilized to train a classifier since they provide spectral features about selected urban materials. However, urban materials can have similar spectral characteristic features due to high inter-class correlation which can lead to misclassification. Spectral libraries rarely provide imagery of their samples, which disables the possibility of classifying urban materials with additional textural information. Thus, this paper conducts material classification comparing the benefits of using close-range acquired spectral and textural features. The spectral features consist of either the original spectra, a PCA-based encoding or the compressed spectral representation of the original spectra retrieved using a deep autoencoder. The textural features are generated using a deep denoising convolutional autoencoder. The spectral and textural features are gathered from the recently published spectral library KLUM. Three classifiers are used, the two well-established Random Forest and Support Vector Machine classifiers in addition to a Histogram-based Gradient Boosting Classification Tree. The achieved overall accuracy was within the range of 70–80% with a standard deviation between 2–10% across all classification approaches. This indicates that the amount of samples still is insufficient for some of the material classes for this classification task. Nonetheless, the classification results indicate that the spectral features are more important for assigning material labels than the textural features

Swarm intelligence in cooperative environments: introducing the N-step dynamic tree search algorithm

Uncertainty and partial or unknown information about environment dynamics have led reward-based methods to play a key role in the Single-Agent and Multi-Agent Learning problem. Tree-based planning approaches such as Monte Carlo Tree Search algorithm have been a striking success in single-agent domains where a perfect simulator model is available, e.g., Go and chess strategic board games. This paper presents a decentralized tree-based planning scheme, that combines forward planning with direct reinforcement learning temporal-difference updates applied to the multi-agent setting. Forward planning requires an engine model which is learned from experience and represented via function approximation. Evaluation and validation are carried out in the Hunter-Prey Pursuit cooperative environment and performance is compared with state-of-the-art RL techniques. N-Step Dynamic Tree Search (NSDTS) pretends to adapt the most successful single-agent learning methods to the multi-agent boundaries in a decentralized system structure, dealing with scalability issues and exponential growth of computational resources suffered by centralized systems. NSDTS demonstrates to be a remarkable advance compared to the conventional Q-Learning temporal-difference method

Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesWater has been playing a key role in human life since the dawn of civilization. It is an integral part of our lives. In recent years, water bodies specially, urban water bodies are in a poor state due to climate change and rapid urban expansion. Though some cities have become aware of this poor state of water bodies, many cities around the world are not contemplating this issue. Because less research has been conducted on water bodies than other land covers in urban areas like built-up. Besides, many advanced algorithms are currently being utilized in different fields, but in terms of water body study, these advancements are still missing. That is why this study aims at investigating the spatio-temporal changes in urban water bodies in Chittagong city using deep learning and freely available Landsat data. Looking at the significance of the study, firstly, as this study has adopted two different deep learning (DL) models and evaluated the performance, the findings can help to understand the suitability of applying deep learning algorithms to extract information from mid to low resolution imagery like Landsat. Secondly, this work will help us to understand why the conservation of the existing water bodies is so important. Finally, this study will encourage further research in the field of deep learning and water bodies by opening the door for monitoring other environmental resources