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Recently, FCNs have attracted widespread attention in the CD field. Inpursuit of better CD performance, it has become a tendency to design deeper andmore complicated FCNs, which inevitably brings about huge numbers of parametersand an unbearable computational burden. With the goal of designing a quite deeparchitecture to obtain more precise CD results while simultaneously decreasingparameter numbers to improve efficiency, in this work, we present a very deepand efficient CD network, entitled EffCDNet. In EffCDNet, to reduce thenumerous parameters associated with deep architecture, an efficient convolutionconsisting of depth-wise convolution and group convolution with a channelshuffle mechanism is introduced to replace standard convolutional layers. Interms of the specific network architecture, EffCDNet does not use mainstreamUNet-like architecture, but rather adopts the architecture with a very deepencoder and a lightweight decoder. In the very deep encoder, two very deepsiamese streams stacked by efficient convolution first extract two highlyrepresentative and informative feature maps from input image-pairs.Subsequently, an efficient ASPP module is designed to capture multi-scalechange information. In the lightweight decoder, a recurrent criss-crossself-attention (RCCA) module is applied to efficiently utilize non-localsimilar feature representations to enhance discriminability for each pixel,thus effectively separating the changed and unchanged regions. Moreover, totackle the optimization problem in confused pixels, two novel loss functionsbased on information entropy are presented. On two challenging CD datasets, ourapproach outperforms other SOTA FCN-based methods, with only benchmark-levelparameter numbers and quite low computational overhead.

Towards Deep and Efficient: A Deep Siamese Self-Attention Fully Efficient Convolutional Network for Change Detection in VHR Images