DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation

Deep learning and remote sensing techniques have significantly advanced water monitoring abilities; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this paper, we present DeepAqua, a self-supervised deep learning model that leverages knowledge distillation (a.k.a. teacher-student model) to eliminate the need for manual annotations during the training phase. We utilize the Normalized Difference Water Index (NDWI) as a teacher model to train a Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture Radar (SAR) images, and to train the student model, we exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. DeepAqua represents a significant advancement in computer vision techniques by effectively training semantic segmentation models without any manually annotated data. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 7%, Intersection Over Union by 27%, and F1 score by 14%. This approach offers a practical solution for monitoring wetland water extent changes without needing ground truth data, making it highly adaptable and scalable for wetland conservation efforts.Comment: 29 pages, 8 figures, 1 tabl

Object Detection in High Resolution Aerial Images and Hyperspectral Remote Sensing Images

With rapid developments in satellite and sensor technologies, there has been a dramatic increase in the availability of remotely sensed images. However, the exploration of these images still involves a tremendous amount of human interventions, which are tedious, time-consuming, and inefficient. To help imaging experts gain a complete understanding of the images and locate the objects of interest in a more accurate and efficient way, there is always an urgent need for developing automatic detection algorithms. In this work, we delve into the object detection problems in remote sensing applications, exploring the detection algorithms for both hyperspectral images (HSIs) and high resolution aerial images. In the first part, we focus on the subpixel target detection problem in HSIs with low spatial resolutions, where the objects of interest are much smaller than the image pixel spatial resolution. To this end, we explore the detection frameworks that integrate image segmentation techniques in designing the matched filters (MFs). In particular, we propose a novel image segmentation algorithm to identify the spatial-spectral coherent image regions, from which the background statistics were estimated for deriving the MFs. Extensive experimental studies were carried out to demonstrate the advantages of the proposed subpixel target detection framework. Our studies show the superiority of the approach when comparing to state-of-the-art methods. The second part of the thesis explores the object based image analysis (OBIA) framework for geospatial object detection in high resolution aerial images. Specifically, we generate a tree representation of the aerial images from the output of hierarchical image segmentation algorithms and reformulate the object detection problem into a tree matching task. We then proposed two tree-matching algorithms for the object detection framework. We demonstrate the efficiency and effectiveness of the proposed tree-matching based object detection framework. In the third part, we study object detection in high resolution aerial images from a machine learning perspective. We investigate both traditional machine learning based framework and end-to-end convolutional neural network (CNN) based approach for various object detection tasks. In the traditional detection framework, we propose to apply the Gaussian process classifier (GPC) to train an object detector and demonstrate the advantages of the probabilistic classification algorithm. In the CNN based approach, we proposed a novel scale transfer module that generates enhanced feature maps for object detection. Our results show the efficiency and competitiveness of the proposed algorithms when compared to state-of-the-art counterparts

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

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.Comment: This paper contains 27 pages and accepted for publication in MDPI remote sensing journal. GitHub Repository: https://github.com/Jakaria08/EESRGAN (Implementation