Hyperspectral imaging (HSI) provides much more information than regular optical microscopy and spectroscopy, making it valuable in areas such as biomedicine, materials inspection and food safety. However HSI is challenging because of the large amount of data that has to be acquired, and large measurement times. Compressed sensing (CS) approaches to hyperspectral imaging have been developed to address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types objects or scenes. Here, we develop improved compressed sensing approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. This is an augmentation of single-pixel-camera-style acquisition for HSI, where a single spectrum is measured per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing, and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased, while maintaining reconstruction speed as well as accuracy. The methods are validated computationally, via noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ~10 times smaller measurement plus reconstruction time as compared to full sampling HSI, without compromise to reconstruction accuracies across the different sample images tested. Multitrack non-adaptive CS (sparse recovery) suffers a large reconstruction time, but is the most robust to Poisson noise.