Deforestation in the Amazon rainforest results in reduced biodiversity, habitat loss, climate change, and other destructive impacts. Hence obtaining location information on human activities is essential for scientists and governments working to protect the Amazon rainforest. We propose a novel remote sensing image classification framework that provides us with the key data needed to more effectively manage deforestation and its consequences. We introduce the attention module to separate the features which are extracted from CNN(Convolutional Neural Network) by channel, then further send the separated features to the LSTM(Long-Short Term Memory) network to predict labels sequentially. Moreover, we propose a loss function by calculating the co-occurrence matrix of all labels in the dataset and assigning different weights to each label. Experimental results on the satellite image dataset of the Amazon rainforest show that our model obtains a better $F_2$ score compared to other methods, which indicates that our model is effective in utilizing label dependencies to improve the performance of multi-label image classification.

In this paper, a novel reconfigurable single-pole double-throw (SPDT) switch/power divider (PD) employing substrate-integrated waveguide (SIW) technology is presented for V-band applications. By controlling the dc-bias of PIN diodes, the proposed reconfigurable function can be achieved. Simulations show that, when functioned as a SPDT switch, the circuit exhibits an isolation of larger than 32 dB and an insertion loss of less than 1.4 dB from 58-62 GHz. On the other hand, a minimum insertion loss of 0.9 dB is calculated at 60 GHz when functioned as a 3-dB PD. The total area of the device is 3λ 0 × 2.36λ 0 .