  Recently, FCNs have attracted widespread attention in the CD field. In
pursuit of better CD performance, it has become a tendency to design deeper and
more complicated FCNs, which inevitably brings about huge numbers of parameters
and an unbearable computational burden. With the goal of designing a quite deep
architecture to obtain more precise CD results while simultaneously decreasing
parameter numbers to improve efficiency, in this work, we present a very deep
and efficient CD network, entitled EffCDNet. In EffCDNet, to reduce the
numerous parameters associated with deep architecture, an efficient convolution
consisting of depth-wise convolution and group convolution with a channel
shuffle mechanism is introduced to replace standard convolutional layers. In
terms of the specific network architecture, EffCDNet does not use mainstream
UNet-like architecture, but rather adopts the architecture with a very deep
encoder and a lightweight decoder. In the very deep encoder, two very deep
siamese streams stacked by efficient convolution first extract two highly
representative and informative feature maps from input image-pairs.
Subsequently, an efficient ASPP module is designed to capture multi-scale
change information. In the lightweight decoder, a recurrent criss-cross
self-attention (RCCA) module is applied to efficiently utilize non-local
similar feature representations to enhance discriminability for each pixel,
thus effectively separating the changed and unchanged regions. Moreover, to
tackle the optimization problem in confused pixels, two novel loss functions
based on information entropy are presented. On two challenging CD datasets, our
approach outperforms other SOTA FCN-based methods, with only benchmark-level
parameter numbers and quite low computational overhead.
