Learning to Identify Illegal Landfills through Scene Classification in Aerial Images

Illegal landfills are uncontrolled disposals of waste that cause severe environmental and health risk. Discovering them as early as possible is of prominent importance for preventing hazards, such as fire pollution and leakage. Before the digital era, the only means to detect illegal waste dumps was the on site inspection of potentially suspicious sites, a procedure extremely costly and impossible to scale to a vast territory. With the advent of Earth observation technology, scanning the territory via aerial images has become possible. However, manual image interpretation remains a complex and time-consuming task that requires expert skill. Photo interpretation can be partially automated by embedding the expert knowledge within a data driven classifier trained with samples provided by human annotators. In this paper, the detection of illegal landfills is formulated as a multi-scale scene classification problem. Scene elements positioning and spatial relations constitute hints of the presence of illegal waste dumps. A dataset of ≈3000 images (20 cm resolution per pixel) was created with the help of expert photo interpreters. A combination of ResNet50 and Feature Pyramid Network (FPN) elements accounting for different object scales achieves 88% precision with an 87% of recall in a test area. The results proved the feasibility of applying convolutional neural networks for scene classification in this scenario to optimize the process of waste dumps detection.

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PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs13071243 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1243

Author(s):

Wenxin Yin ◽

Wenhui Diao ◽

Peijin Wang ◽

Xin Gao ◽

Ya Li ◽

...

Keyword(s):

Remote Sensing ◽

Power Plants ◽

State Of The Art ◽

Thermal Power ◽

Image Interpretation ◽

Remote Sensing Image ◽

Thermal Power Plants ◽

Average Precision ◽

Deep Convolutional Neural Networks ◽

Multi Scale

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.

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A Multi-Task Network with Distance–Mask–Boundary Consistency Constraints for Building Extraction from Aerial Images

Remote Sensing ◽

10.3390/rs13142656 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2656

Author(s):

Furong Shi ◽

Tong Zhang

Keyword(s):

Distance Estimation ◽

Image Data ◽

Learning Technologies ◽

Aerial Images ◽

Superior Performance ◽

Aerial Image ◽

Great Success ◽

Building Extraction ◽

Shape Information ◽

Multi Scale

Deep-learning technologies, especially convolutional neural networks (CNNs), have achieved great success in building extraction from areal images. However, shape details are often lost during the down-sampling process, which results in discontinuous segmentation or inaccurate segmentation boundary. In order to compensate for the loss of shape information, two shape-related auxiliary tasks (i.e., boundary prediction and distance estimation) were jointly learned with building segmentation task in our proposed network. Meanwhile, two consistency constraint losses were designed based on the multi-task network to exploit the duality between the mask prediction and two shape-related information predictions. Specifically, an atrous spatial pyramid pooling (ASPP) module was appended to the top of the encoder of a U-shaped network to obtain multi-scale features. Based on the multi-scale features, one regression loss and two classification losses were used for predicting the distance-transform map, segmentation, and boundary. Two inter-task consistency-loss functions were constructed to ensure the consistency between distance maps and masks, and the consistency between masks and boundary maps. Experimental results on three public aerial image data sets showed that our method achieved superior performance over the recent state-of-the-art models.

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Multi-scale Fusion and Channel Weighted CNN for Acoustic Scene Classification

Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning ◽

10.1145/3372806.3372809 ◽

2019 ◽

Cited By ~ 1

Author(s):

Liping Yang ◽

Xinxing Chen ◽

Lianjie Tao ◽

Xiaohua Gu

Keyword(s):

Scene Classification ◽

Multi Scale

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MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images

Energies ◽

10.3390/en14051426 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1426

Author(s):

Chuanyang Liu ◽

Yiquan Wu ◽

Jingjing Liu ◽

Jiaming Han

Keyword(s):

Feature Detection ◽

Feature Fusion ◽

Memory Storage ◽

Aerial Images ◽

Detection Accuracy ◽

Composite Insulator ◽

Running Time ◽

Scale Feature ◽

Multi Scale ◽

Good Trade

Insulator detection is an essential task for the safety and reliable operation of intelligent grids. Owing to insulator images including various background interferences, most traditional image-processing methods cannot achieve good performance. Some You Only Look Once (YOLO) networks are employed to meet the requirements of actual applications for insulator detection. To achieve a good trade-off among accuracy, running time, and memory storage, this work proposes the modified YOLO-tiny for insulator (MTI-YOLO) network for insulator detection in complex aerial images. First of all, composite insulator images are collected in common scenes and the “CCIN_detection” (Chinese Composite INsulator) dataset is constructed. Secondly, to improve the detection accuracy of different sizes of insulator, multi-scale feature detection headers, a structure of multi-scale feature fusion, and the spatial pyramid pooling (SPP) model are adopted to the MTI-YOLO network. Finally, the proposed MTI-YOLO network and the compared networks are trained and tested on the “CCIN_detection” dataset. The average precision (AP) of our proposed network is 17% and 9% higher than YOLO-tiny and YOLO-v2. Compared with YOLO-tiny and YOLO-v2, the running time of the proposed network is slightly higher. Furthermore, the memory usage of the proposed network is 25.6% and 38.9% lower than YOLO-v2 and YOLO-v3, respectively. Experimental results and analysis validate that the proposed network achieves good performance in both complex backgrounds and bright illumination conditions.

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A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

Remote Sensing ◽

10.3390/rs13030433 ◽

2021 ◽

Vol 13 (3) ◽

pp. 433

Author(s):

Junge Shen ◽

Tong Zhang ◽

Yichen Wang ◽

Ruxin Wang ◽

Qi Wang ◽

...

Keyword(s):

Remote Sensing ◽

Feature Representation ◽

Dual Model ◽

Scene Classification ◽

Remote Sensing Images ◽

Single Model ◽

Fusion Strategy ◽

Multi Scale ◽

The Arts ◽

Scene Representation

Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the classifier. Although multiple models possess better properties than a single model on these aspects, the fusion strategy for these models is a key component to maximize the final accuracy. In this paper, we construct a novel dual-model architecture with a grouping-attention-fusion strategy to improve the performance of scene classification. Specifically, the model employs two different convolutional neural networks (CNNs) for feature extraction, where the grouping-attention-fusion strategy is used to fuse the features of the CNNs in a fine and multi-scale manner. In this way, the resultant feature representation of the scene is enhanced. Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. Extensive experiments are conducted on four scene classification datasets include the UCM land-use dataset, the WHU-RS19 dataset, the AID dataset, and the OPTIMAL-31 dataset. The experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-arts.

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Multi-Scale Convolutional Neural Network for Remote Sensing Scene Classification

2018 IEEE International Conference on Electro/Information Technology (EIT) ◽

10.1109/eit.2018.8500107 ◽

2018 ◽

Cited By ~ 5

Author(s):

Haikel Alhichri ◽

Naif Alajlan ◽

Yakoub Bazi ◽

Timon Rabczuk

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Scene Classification ◽

Multi Scale

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Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images

Remote Sensing ◽

10.3390/rs12050784 ◽

2020 ◽

Vol 12 (5) ◽

pp. 784 ◽

Cited By ~ 1

Author(s):

Wei Guo ◽

Weihong Li ◽

Weiguo Gong ◽

Jinkai Cui

Keyword(s):

Neural Network ◽

Object Detection ◽

Semantic Information ◽

Aerial Images ◽

Training Strategy ◽

The Public ◽

Multi Scale ◽

Shallow Layer ◽

The Neural Network ◽

Feature Pyramid

Multi-scale object detection is a basic challenge in computer vision. Although many advanced methods based on convolutional neural networks have succeeded in natural images, the progress in aerial images has been relatively slow mainly due to the considerably huge scale variations of objects and many densely distributed small objects. In this paper, considering that the semantic information of the small objects may be weakened or even disappear in the deeper layers of neural network, we propose a new detection framework called Extended Feature Pyramid Network (EFPN) for strengthening the information extraction ability of the neural network. In the EFPN, we first design the multi-branched dilated bottleneck (MBDB) module in the lateral connections to capture much more semantic information. Then, we further devise an attention pathway for better locating the objects. Finally, an augmented bottom-up pathway is conducted for making shallow layer information easier to spread and further improving performance. Moreover, we present an adaptive scale training strategy to enable the network to better recognize multi-scale objects. Meanwhile, we present a novel clustering method to achieve adaptive anchors and make the neural network better learn data features. Experiments on the public aerial datasets indicate that the presented method obtain state-of-the-art performance.

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