Progressive Contextual Instance Refinement for Weakly Supervised Object Detection in Remote Sensing Images

Feng, Xiao; Han, Junwei; Yao, Xiwen; Cheng, Gong

doi:10.1109/tgrs.2020.2985989

Cited by 99 publications

(53 citation statements)

References 46 publications

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“…To provide high-quality initial samples and obtain optimal object detectors with only image-level annotations, Yao et al [34] proposed a dynamic curriculum learning strategy with an entropy-based criterion and designed an effective instanceaware focal loss function, which can progressively learn the object detectors by feeding training images with increasing difficulty that matches current detection ability. To avoid selecting only one top-scoring proposal that usually results in learning a suboptimal object detector, Feng et al [35]proposed a novel end-to-end progressive contextual instance refinement method by leveraging both local and global context information for weakly supervised object detection.…”

Section: Semi-supervised Semantic Segmentationmentioning

confidence: 99%

BAS$^{4}$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images

Sun

Shi

Huang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Semantic segmentation is a fundamental task in remote sensing image understanding. Recently, Deep Convolutional Neural Networks (DCNNs) have considerably improved the performance of the semantic segmentation of natural scenes. However, it is still challenging for Very High Resolution (VHR) remote sensing images. Due to the large and complex scenes as well as the influence of illumination and imaging angle, it is particularly difficult for the existing methods to accurately obtain the category of pixels at object boundaries-the so-called boundary blur. We propose a framework called Boundary-Aware Semi-Supervised Semantic Segmentation Network (BAS 4 Net), which obtains more accurate segmentation results without additional annotation workload, especially at the object boundaries. The Channel-weighted Multi-scale Feature (CMF) module balances semantic and spatial information and the Boundary Attention Module (BAM) weights the features with rich semantic boundary information to alleviate the boundary blur. Additionally, to decrease the amount of difficult and tedious manual labeling of remote sensing images, a discriminator network infers pseudolabels from unlabeled images to assist semi-supervised learning and further improves the performance of the segmentation network. To validate the effectiveness of the proposed framework, extensive experiments have been performed on both the ISPRS Vaihingen dataset and the novel remote sensing dataset AIR-SEG with more categories and complex boundaries. The results demonstrate a significant improvement of accuracy especially on boundaries and for small objects.

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Section: Semi-supervised Semantic Segmentationmentioning

confidence: 99%

BAS$^{4}$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images

Sun

Shi

Huang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The latest works on WSL based object detection from RSIs are [46,47]. In [46], Yao et al propose a dynamic curriculum learning strategy to perform weakly supervised object detection from high-resolution RSIs. This work can progressively learn the object detectors by feeding training images with increasing difficulty that matches current detection ability.…”

Section: B Weakly Supervised Learningmentioning

confidence: 99%

Convolutional Neural Network Based Weakly Supervised Learning for Aircraft Detection From Remote Sensing Image

Weise

Wang

et al. 2020

IEEE Access

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Object detection methods based on Convolutional Neural Networks (CNNs) require a large number of images with annotation information to train. In aircraft detection from remote sensing images (RSIs), aircraft targets are usually small and the cost of manual annotation is very high. In this paper, we tackle the problem of weakly supervised aircraft detection from RSIs, which aims to learn detectors with only image-level annotations, i.e., without bounding-box labeled data during the training stage. Based on the fact that the feature maps learned from the CNN network are localizable, we propose a simple yet efficient aircraft detection algorithm called Weakly Supervised Learning in AlexNet (AlexNet-WSL). In AlexNet-WSL, we utilize the AlexNet CNN as backbone network, but replace the last two fully connected layers with a Global Average Pooling (GAP) and two convolutional layers. Based on the class activation maps, we generate heat maps via reverse weighting for locating the target object. Unlike object detection methods that require object location data for training, our proposal only needs image-level labelled data. We furthermore build a set of remote sensing aircraft images, the Weakly Supervised Aircraft Detection Dataset (WSADD) for algorithm benchmarking. The experimental results on the WSADD show that AlexNet-WSL effectively detects the aircraft and achieves a detection effect equivalent to the Faster R-CNN method and the YOLOv3 method, which both require bounding-box labelled data for training, with a lower false alarm rate and a shorter training time.

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“…They propose a dynamic curriculum learning strategy that progressively learns an object detector by feeding training images of increasing difficulty that matches current detection capability. Further improvements to WSOD can be found in ( Feng et al., 2020 ) and ( Cheng et al., 2020 ). Other methods have also been explored for improving the discriminative capability of CNN.…”

Section: Related Workmentioning

confidence: 99%

Identifying patterns in urban housing density in developing countries using convolutional networks and satellite imagery

Sanya

Mwebaze

2020

Heliyon

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Progressive Contextual Instance Refinement for Weakly Supervised Object Detection in Remote Sensing Images

Cited by 99 publications

References 46 publications

BAS$^{4}$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images

BAS$^{4}$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images

Convolutional Neural Network Based Weakly Supervised Learning for Aircraft Detection From Remote Sensing Image

Identifying patterns in urban housing density in developing countries using convolutional networks and satellite imagery

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