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

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