  To reduce the storage requirements, remote sensing (RS) images are usually
stored in compressed format. Existing scene classification approaches using
deep neural networks (DNNs) require to fully decompress the images, which is a
computationally demanding task in operational applications. To address this
issue, in this paper we propose a novel approach to achieve scene
classification in JPEG 2000 compressed RS images. The proposed approach
consists of two main steps: i) approximation of the finer resolution sub-bands
of reversible biorthogonal wavelet filters used in JPEG 2000; and ii)
characterization of the high-level semantic content of approximated wavelet
sub-bands and scene classification based on the learnt descriptors. This is
achieved by taking codestreams associated with the coarsest resolution wavelet
sub-band as input to approximate finer resolution sub-bands using a number of
transposed convolutional layers. Then, a series of convolutional layers models
the high-level semantic content of the approximated wavelet sub-band. Thus, the
proposed approach models the multiresolution paradigm given in the JPEG 2000
compression algorithm in an end-to-end trainable unified neural network. In the
classification stage, the proposed approach takes only the coarsest resolution
wavelet sub-bands as input, thereby reducing the time required to apply
decoding. Experimental results performed on two benchmark aerial image archives
demonstrate that the proposed approach significantly reduces the computational
time with similar classification accuracies when compared to traditional RS
scene classification approaches (which requires full image decompression).
