Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Luisa Verdoliva,et al.  Land Use Classification in Remote Sensing Images by Convolutional Neural Networks , 2015, ArXiv.

[3]  José González,et al.  Saliency Fusion in Eigenvector Space with Multi-Channel Pulse Coupled Neural Network , 2017, ArXiv.

[4]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[5]  Dongjian He,et al.  An Improved Hybrid Model for Automatic Salient Region Detection , 2012, IEEE Signal Processing Letters.

[6]  Gui-Song Xia,et al.  Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery , 2015, Remote. Sens..

[7]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[9]  José González,et al.  An Improved Saliency for RGB-D Visual Tracking and Control Strategies for a Bio-monitoring Mobile Robot , 2013, EvAAL.

[10]  Jefersson Alex dos Santos,et al.  Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[11]  J. Bullier.  Hierarchies of Cortical Areas , 2003 .

[12]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Yide Ma,et al.  Medical image fusion using m-PCNN , 2008, Inf. Fusion.

[14]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Jürgen Schmidhuber,et al.  Deep Networks with Internal Selective Attention through Feedback Connections , 2014, NIPS.

[16]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[17]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[18]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Yoshihiko Mochizuki,et al.  Detection by classification of buildings in multispectral satellite imagery , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[21]  Jason M. Kinser,et al.  Image Processing using Pulse-Coupled Neural Networks , 1998, Perspectives in Neural Computing.

[22]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Igor Sevo,et al.  Convolutional Neural Network Based Automatic Object Detection on Aerial Images , 2016, IEEE Geoscience and Remote Sensing Letters.

[24]  Xianming Liu,et al.  Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation , 2016, ArXiv.

[25]  Weisi Lin,et al.  Saliency Detection in the Compressed Domain for Adaptive Image Retargeting , 2012, IEEE Transactions on Image Processing.

[26]  Wei Xu,et al.  Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).