Airborne remote sensing has important applications in agriculture monitoring because of the flexibility of system deployment. The major obstacle in practical use is its high cost. To reduce the cost, a multispectral system can be assembled by using individual cameras onboard a small aerial platform, such as a miniature unmanned aerial vehicle (mini-UAV). In such a case, the cameras may have shifting and rotational misalignment, even after careful adjustment. Contiguous frames are captured as the platform flies. So multi-band registration within a single frame and frame-toframe mosaicking are necessary to obtain a co-registered multispectral image for the entire monitoring area before any commercial product can be generated to support practical decision-making. In this paper, we present automatic algorithms to achieve this goal. These algorithms are particularly useful to the image scenes where no distinctive features are available. Both automatic and manual evaluations confirm the effectiveness of the developed algorithms in multi-sensor data fusion for overall flat terrain without distinctive features.

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Real-time digital implementation of radar waveform pulse compression and match filtering on FPGA platform is studied. In this work, different types of radar waveforms including phase coded, linear frequency (LFM) and non-linear frequency modulated (NLFM) signals are generated digitally in Xilinx Virtex-5 FPGA platform. Waveforms with different time bandwidth products are tested both in FPGA platform and computer. Digital matched filtering implementation procedure used in FPGA is presented and comparison of theoretical calculations and FPGA implementation results along with implementation resource utilization are presented. Results indicate that precise generation of real-time waveform matched filtering implementations deviate at most 1 dB on range sidelobe levels from theoretical results. Moreover adopted segmentation and parallel implementation of the received pulse both allows processing of divided pulses without SNR degradation and uses less FPGA resources in general compared to processing full PRI at once.

In this paper, we consider automatic radar pulse detection and intra-pulse modulation classification for cognitive electronic warfare applications. In this manner, we introduce an end-to-end framework for detection and classification of radar pulses. Our approach is complete, i.e., we provide raw radar signal at the input side and produce categorical output at the output. We use short time Fourier transform to obtain timefrequency image of the signal. Hough transform is used to detect pulses in time-frequency images and pulses are represented with a single line. Then, convolutional neural networks are used for pulse classification. In experiments, we provide classification results at different SNR levels.

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