Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feedforward) 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 low-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 panel classification on multi-spectral satellite data. Moreover, we propose a class activation mapping method (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for pixel level detection of the solar power plants. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both mega-solar classification and detection task.