Adaptive Rotated Convolution for Rotated Object Detection

Rotated object detection aims to identify and locate objects in images with arbitrary orientation. In this scenario, the oriented directions of objects vary considerably across different images, while multiple orientations of objects exist within an image. This intrinsic characteristic makes it challenging for standard backbone networks to extract high-quality features of these arbitrarily orientated objects. In this paper, we present Adaptive Rotated Convolution (ARC) module to handle the aforementioned challenges. In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image. The two designs work seamlessly in rotated object detection problem. Moreover, ARC can conveniently serve as a plug-and-play module in various vision backbones to boost their representation ability to detect oriented objects accurately. Experiments on commonly used benchmarks (DOTA and HRSC2016) demonstrate that equipped with our proposed ARC module in the backbone network, the performance of multiple popular oriented object detectors is significantly improved (e.g. +3.03% mAP on Rotated RetinaNet and +4.16% on CFA). Combined with the highly competitive method Oriented R-CNN, the proposed approach achieves state-of-the-art performance on the DOTA dataset with 81.77% mAP

Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances

Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than 300 papers relevant to the RSOD filed were reviewed in this surve

A Review on Deep Learning in UAV Remote Sensing

Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both "deep learning" and "UAV remote sensing" thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.Comment: 38 pages, 10 figure

Monitoring Al Ain City urban growth dynamics Between 1972 and 2000 by integrating remote Sensing and GIS techniques

We live in a world full of change as a result of either natural process or human activities. This study focuses on evaluating and monitoring the urban changes of Al Ain city by quantifying and visualizing various aspects of that change between 1972 and 2000. The results of the actual study will serve for effective city planning and decision making process for better life quality of the residents of the city. Rapid changes of the land use and land cover especially built up and impervious areas, make it necessary to involve innovative techniques such as remote sensing and GIS to depict these changes and analyze its dynamics. In this study remote sensing and Geographic Information System (GIS) were integrated to monitoring and mapping the urban growth of Al Ain city between 1972 and 2000. Landsat MSS, TM and ETM+ for the year 1972, 1990 and 2000 respectively were chosen to study and analyze the urban growth of Al Ain city. Remote sensing techniques have the ability to delineate land cover categories by means of classification process. A hybrid unsupervised-supervised classification approach was used for detecting the changes using multi-sensor, multi-temporal remotely sensed images. Prior knowledge of the study area, unsupervised classification schema concluded to the application of a classification schema consisting of 6 classes: urban, vegetation, sand and gravel, sand dune, lime stone, water bodies and shadow. Changes in the land cover between successive dates are detected by combining two techniques in a multi stage approach. Post classification comparison and GIS overlay are adopted with given attention to urban areas. The urban change detection is used to identify the pattern of urban growth. Using special metrics like Land Consumption Rate and Land Absorption Coefficient was very helpful to analyze and understand the urban growth pattern. Driving forces behind Al Ain urban growth along with associated impacts on the city were identified, discussed and analyzed. An attempt to predict future trends to help the municipality to plan for better future of the city was investigated. The outcomes from this study are a demonstration of the embedded powerfulness of remote sensing and GIS techniques for studying spatial and temporal changes of land use in general and urban areas in particular. These outcomes reveal that Al Ain city lived through a period of huge and rapid development after the foundation of the United Arab Emirates in 1971. The expansion of the city generally occurred at the expense of sand dune, gravel sand and limestone with a high density in areas close to the city centre