BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Santara, Anirban; Mani, Kaustubh; Hatwar, Pranoot; Singh, Ankit; Garg, Ankur; Padia, Kirti; Mitra, Pabitra

doi:10.1109/tgrs.2017.2705073

Cited by 138 publications

(94 citation statements)

References 43 publications

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“…In [21], authors propose a self-improving CNN model, which combines a 2D CNN with a fractional order Darwinian particle swarm optimization algorithm to iteratively select the most informative bands that are suitable for training the designed CNN. Santara et al [29] propose an end-to-end band-adaptive spectral-spatial feature learning network to address the problems of the curse of dimensionality. In [30], to allow CNN appropriately trained using limited labeled data, authors present a novel pixel-pair CNN to significantly augment the number of training samples.…”

Section: A Hyperspectral Image Analysismentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,432

1,204

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a "black-box" solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization. Following this wave of success and thanks to the increased availability of data and computational resources, the use of deep learning in remote sensing is finally taking off in remote sensing as well. Remote sensing data bring some new challenges for deep learning, since satellite image analysis raises some unique questions that translate into challenging new scientific questions:• Remote sensing data are often multi-modal, e.g. from optical (multi-and hyperspectral) and synthetic aperture radar (SAR) sensors, where both the imaging geometries and the content are completely different. Data and information fusion uses these complementary

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Section: A Hyperspectral Image Analysismentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,432

1,204

View full text Add to dashboard Cite

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“…In most object detection works, first a classifier is trained and then it is applied on a number of candidate windows. Recently, deep learning CNNs have started to be used for scene tagging and object detection in remotely-sensed images ( [17,[20][21][22][23]). However, as far as we know, there are no studies in the literature on the use of CNNs for the detection of plant species individuals in remotely-sensed images and any comparison between OBIA and deep CNNs methods.…”

Section: Land Cover Mappingmentioning

confidence: 99%

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

et al. 2017

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There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing).

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“…Several approaches have been explored in literature for HSI classification. K-nearest neighbors (k-NN) based methods use Eucledian distance in the input space to find the k nearest training examples and a class is assigned on the basis of them [17]. Support Vector Machine (SVM) based methods introduce dimensionality reduction in order to address the problem of high spectral dimensionality and limited number of labeled training examples, with SVM classifiers used in the reduced dimensional space.…”

Section: Hyperspectral Imagerymentioning

confidence: 99%

“…Among the approaches explored for HSI classification, convolutional neural network (CNN) based methods such as BASS Net [17] and HSI-CNN [13] are favourable over the others because of their greatly improved accuracy for some popular benchmark datasets, with the ability to use extensive parameters to learn spectral features of a HSI. However, these CNN-based algorithms have great computational complexity due to the large dimensionality of hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

“…In order to address the above challenges and achieve fast processing speed on embedded devices, this work proposes a novel CNN architecture based on BASS Net [17], and our model is more hardware efficient for implementation on FPGAs while maintaining similar accuracy as the original BASS Net. Besides, we propose and optimize the hardware architecture to accelerate our proposed network in FPGA by parallel processing, data pre-fetching and design space exploration.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing CNN-Based Hyperspectral Image Classification on FPGAs

Liu

Chu

Wang

et al. 2019

Lecture Notes in Computer Science

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Hyperspectral image (HSI) classification has been widely adopted in applications involving remote sensing imagery analysis which require high classification accuracy and real-time processing speed. Methods based on Convolutional neural networks (CNNs) have been proven to achieve state-of-the-art accuracy in classifying HSIs. However, CNN models are often too computationally intensive to achieve real-time response due to the high dimensional nature of HSI, compared to traditional methods such as Support Vector Machines (SVMs). Besides, previous CNN models used in HSI are not specially designed for efficient implementation on embedded devices such as FPGAs. This paper proposes a novel CNN-based algorithm for HSI classification which takes into account hardware efficiency. A customized architecture which enables the proposed algorithm to be mapped effectively onto FPGA resources is then proposed to support real-time on-board classification with low power consumption. Implementation results show that our proposed accelerator on a Xilinx Zynq 706 FPGA board achieves more than 70× faster than an Intel 8-core Xeon CPU and 3× faster than an NVIDIA GeForce 1080 GPU. Compared to previous SVM-based FPGA accelerators, we achieve comparable processing speed but provide a much higher classification accuracy.

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BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Cited by 138 publications

References 43 publications

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Optimizing CNN-Based Hyperspectral Image Classification on FPGAs

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