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

DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images

The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions, since nowadays multiple source and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over the time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches

초고해상도 영상 분류를 위한 순환 적대적 생성 신경망 기반의 준지도 학습 프레임워크

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2021.8. 김용일.고해상도 영상 분류는 토지피복지도 제작, 식생 분류, 도시 계획 등에서 다양하게 활용되는 대표적인 영상 분석 기술이다. 최근, 심층 합성곱 신경망 (deep convolutional neural network)은 영상 분류 분야에서 두각을 보여왔다. 특히, 심층 합성곱 신경망 기반의 의미론적 영상 분할 (semantic segmentation) 기법은 연산 비용을 매우 감소시키며, 이러한 점은 지속적으로 고해상도 데이터가 축적되고 있는 고해상도 영상을 분석할 때 중요하게 작용된다. 심층 학습 (deep learning) 기반 기법이 안정적인 성능을 달성하기 위해서는 일반적으로 충분한 양의 라벨링된 데이터 (labeled data)가 확보되어야 한다. 그러나, 원격탐사 분야에서 고해상도 영상에 대한 참조데이터를 얻는 것은 비용적으로 제한적인 경우가 많다. 이러한 문제를 해결하기 위해 본 논문에서는 라벨링된 영상과 라벨링되지 않은 영상 (unlabeled image)을 함께 사용하는 준지도 학습 프레임워크를 제안하였으며, 이를 통해 고해상도 영상 분류를 수행하였다. 본 논문에서는 라벨링되지 않은 영상을 사용하기 위해서 개선된 순환 적대적 생성 신경망 (CycleGAN) 방법을 제안하였다. 순환 적대적 생성 신경망은 영상 변환 모델 (image translation model)로 처음 제안되었으며, 특히 순환 일관성 손실 함수 (cycle consistency loss function)를 통해 페어링되지 않은 영상 (unpaired image)을 모델 학습에 활용한 연구이다. 이러한 순환 일관성 손실 함수에 영감을 받아, 본 논문에서는 라벨링되지 않은 영상을 참조데이터와 페어링되지 않은 데이터로 간주하였으며, 이를 통해 라벨링되지 않은 영상으로 분류 모델을 함께 학습시켰다. 수많은 라벨링되지 않은 데이터와 상대적으로 적은 라벨링된 데이터를 함께 활용하기 위해, 본 논문은 지도 학습과 개선된 준지도 학습 기반의 순환 적대적 생성 신경망을 결합하였다. 제안된 프레임워크는 순환 과정(cyclic phase), 적대적 과정(adversarial phase), 지도 학습 과정(supervised learning phase), 세 부분을 포함하고 있다. 라벨링된 영상은 지도 학습 과정에서 분류 모델을 학습시키는 데에 사용된다. 적대적 과정과 지도 학습 과정에서는 라벨링되지 않은 데이터가 사용될 수 있으며, 이를 통해 적은 양의 참조데이터로 인해 충분히 학습되지 못한 분류 모델을 추가적으로 학습시킨다. 제안된 프레임워크의 결과는 공공 데이터인 ISPRS Vaihingen Dataset을 통해 평가되었다. 정확도 검증을 위해, 제안된 프레임워크의 결과는 5개의 벤치마크들 (benchmarks)과 비교되었으며, 이때 사용된 벤치마크 모델들은 지도 학습과 준지도 학습 방법 모두를 포함한다. 이에 더해, 본 논문에서는 라벨링된 데이터와 라벨링되지 않은 데이터의 구성에 따른 영향을 확인하였으며, 다른 분류 모델에 대한 본 프레임워크의 적용가능성에 대한 추가적인 실험도 수행하였다. 제안된 프레임워크는 다른 벤치마크들과 비교해서 가장 높은 정확도 (세 실험 지역에 대해 0.796, 0.786, 0.784의 전체 정확도)를 달성하였다. 특히, 객체의 크기나 모양과 같은 특성이 다른 실험 지역에서 가장 큰 정확도 상승을 확인하였으며, 이러한 결과를 통해 제안된 준지도 학습이 모델을 우수하게 정규화(regularization)함을 확인하였다. 또한, 준지도 학습을 통해 향상되는 정확도는 라벨링된 데이터에 비해 라벨링되지 않은 데이터가 상대적으로 많았을 때 그 증가 폭이 더욱 커졌다. 마지막으로, 제안된 준지도 학습 기반의 순환 적대적 생성 신경망 기법이 UNet 외에도 FPN과 PSPNet이라는 다른 분류 모델에서도 유의미한 정확도 상승을 보였다. 이를 통해 다른 분류 모델에 대한 제안된 프레임워크의 적용가능성을 확인하였다Image classification of Very High Resolution (VHR) images is a fundamental task in the remote sensing domain for various applications such as land cover mapping, vegetation mapping, and urban planning. In recent years, deep convolutional neural networks have shown promising performance in image classification studies. In particular, semantic segmentation models with fully convolutional architecture-based networks demonstrated great improvements in terms of computational cost, which has become especially important with the large accumulation of VHR images in recent years. However, deep learning-based approaches are generally limited by the need of a sufficient amount of labeled data to obtain stable accuracy, and acquiring reference labels of remotely-sensed VHR images is very labor-extensive and expensive. To overcome this problem, this thesis proposed a semi-supervised learning framework for VHR image classification. Semi-supervised learning uses both labeled and unlabeled data together, thus reducing the model’s dependency on data labels. To address this issue, this thesis employed a modified CycleGAN model to utilize large amounts of unlabeled images. CycleGAN is an image translation model which was developed from Generative Adversarial Networks (GAN) for image generation. CycleGAN trains unpaired dataset by using cycle consistency loss with two generators and two discriminators. Inspired by the concept of cycle consistency, this thesis modified CycleGAN to enable the use of unlabeled VHR data in model training by considering the unlabeled images as images unpaired with their corresponding ground truth maps. To utilize a large amount of unlabeled VHR data and a relatively small amount of labeled VHR data, this thesis combined a supervised learning classification model with the modified CycleGAN architecture. The proposed framework contains three phases: cyclic phase, adversarial phase, and supervised learning phase. Through the three phase, both labeled and unlabeled data can be utilized simultaneously to train the model in an end-to-end manner. The result of the proposed framework was evaluated by using an open-source VHR image dataset, referred to as the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen dataset. To validate the accuracy of the proposed framework, benchmark models including both supervised and semi-supervised learning methods were compared on the same dataset. Furthermore, two additional experiments were conducted to confirm the impact of labeled and unlabeled data on classification accuracy and adaptation of the CycleGAN model for other classification models. These results were evaluated by the popular three metrics for image classification: Overall Accuracy (OA), F1-score, and mean Intersection over Union (mIoU). The proposed framework achieved the highest accuracy (OA: 0.796, 0.786, and 0.784, respectively in three test sites) in comparison to the other five benchmarks. In particular, in a test site containing numerous objects with various properties, the largest increase in accuracy was observed due to the regularization effect from the semi-supervised method using unlabeled data with the modified CycleGAN. Moreover, by controlling the amount of labeled and unlabeled data, results indicated that a relatively sufficient amount of unlabeled and labeled data is required to increase the accuracy when using the semi-supervised CycleGAN. Lastly, this thesis applied the proposed CycleGAN method to other classification models such as the feature pyramid network (FPN) and the pyramid scene parsing network (PSPNet), in place of UNet. In all cases, the proposed framework returned significantly improved results, displaying the framework’s applicability for semi-supervised image classification on remotely-sensed VHR images.1. Introduction 1 2. Background and Related Works 6 2.1. Deep Learning for Image Classification 6 2.1.1. Image-level Classifiaction 6 2.1.2. Fully Convolutional Architectures 7 2.1.3. Semantic Segmentation for Remote Sensing Images 9 2.2. Generative Adversarial Networks (GAN) 12 2.2.1. Introduction to GAN 12 2.2.2. Image Translation 14 2.2.3. GAN for Semantic Segmentation 16 3. Proposed Framework 20 3.1. Modification of CycleGAN 22 3.2. Feed-forward Path of the Proposed Framework 23 3.2.1. Cyclic Phase 23 3.2.2. Adversarial Phase 23 3.2.3. Supervised Learning Phase 24 3.3. Loss Function for Back-propagation 25 3.4. Proposed Network Architecture 28 3.4.1. Generator Architecture 28 3.4.2. Discriminator Architecture 29 4. Experimental Design 31 4.1. Overall Workflow 33 4.2. Vaihingen Dataset 38 4.3. Implementation Details 40 4.4. Metrics for Quantitative Evaluation 41 5. Results and Discussion 42 5.1. Performance Evaluation of the Proposed Feamwork 42 5.2. Comparison of Classification Performance in the Proposed Framework and Benchmarks 45 5.3. Impact of labeled and Unlabeled Data for Semi-supervised Learning 52 5.4. Cycle Consistency in Semi-supervised Learning 55 5.5. Adaptation of the GAN Framework for Other Classification Models 59 6. Conclusion 62 Reference 65 국문 초록 69석