Data Leakage in Patch-Based Hyperspectral Image Classification: Quantifying the Impact of Spatial Overlap

Mohammed Q. Alkhatib

cs.CVOct 6, 2026

Data Leakage in Patch-Based Hyperspectral Image Classification: Quantifying the Impact of Spatial Overlap

Authors: Mohammed Q. Alkhatib

Organizations: College of Engineering and IT, University of Dubai, Dubai, 14143, UAE

Abstract

Patch-based learning improves hyperspectral image (HSI) classification by exploiting local spectral-spatial information, but random train-test sampling from the same image can cause spatial patch overlap, leading to data leakage and optimistic performance estimates. This paper investigates same-class train-test spatial overlap in patch-based HSI classification using two measures: overlap percentage (OP), which quantifies the global amount of overlapped testing patch pixels, and average overlap ratio (AOR), which measures the local severity among affected testing patches. Experiments on the Pavia University dataset compare random and non-random spatial sampling using SVM, MLP, 2D-CNN, 3D-CNN, ViT, and MorpMamba. The results show that deep patch-based models achieve high accuracy under random sampling, with 3D-CNN reaching 96.17% Overall Accuracy (OA), but drop substantially under non-random spatial sampling, where 3D-CNN decreases to 55.20% and ViT and 2D-CNN drop by 40.71 and 38.81 percentage points (PP), respectively. Patch-size analysis further shows that increasing the patch size from 5x5 to 19x19 raises the random-sampling overlap percentage from 23.28% to 77.02%. These findings demonstrate that random patch-based evaluation can substantially inflate classification performance, especially for models that strongly exploit spatial context. The code associated with this paper is available at: https://github.com/mqalkhatib/Data_Leakage_in_HSI_Classification.

Figures & tables

Figure 1 : General Patch-based hyperspectral image classification framework using PCA and deep learning models.

Figure 2 : Examples of the sampling settings used in this study: (a) random sampling, where training and testing patches may overlap; (b) non-random spatial sampling, where training and testing regions are selected with greater spatial separation; (c) Class Labels.

No	Name	Train	Val	Test	Total	SVM	MLP	2D-CNN	3D-CNN	ViT	MorpMamba
1	Asphalt	94	18	6,519	6,631	62.83 $\pm$ 1.01	64.51 $\pm$ 3.11	83.07 $\pm$ 8.19	94.28 $\pm$ 1.95	89.26 $\pm$ 3.32	75.67 $\pm$ 6.86
2	Meadows	94	18	18,537	18,649	49.46 $\pm$ 4.91	64.17 $\pm$ 8.45	84.98 $\pm$ 5.47	97.63 $\pm$ 1.17	91.32 $\pm$ 4.04	78.05 $\pm$ 7.50
3	Gravel	94	18	1,987	2,099	21.40 $\pm$ 8.34	69.26 $\pm$ 12.62	83.04 $\pm$ 4.28	91.59 $\pm$ 3.10	88.94 $\pm$ 3.86	74.49 $\pm$ 16.57
4	Trees	94	18	2,952	3,064	95.32 $\pm$ 1.48	96.14 $\pm$ 1.50	96.98 $\pm$ 1.04	97.74 $\pm$ 1.11	97.71 $\pm$ 1.11	95.45 $\pm$ 1.64
5	Metal Sheet	94	18	1,233	1,345	99.63 $\pm$ 0.15	99.65 $\pm$ 0.20	99.50 $\pm$ 0.53	99.99 $\pm$ 0.02	99.99 $\pm$ 0.02	99.94 $\pm$ 0.06
6	Bare Soil	94	18	4,917	5,029	67.18 $\pm$ 5.16	64.71 $\pm$ 11.94	88.51 $\pm$ 3.45	96.38 $\pm$ 1.56	89.44 $\pm$ 4.82	88.41 $\pm$ 6.44

Table 1 : Class-wise and overall classification performance under the random patch-based evaluation setting with potential same-class train-test spatial overlap. The reported values are presented as mean

\pm

standard deviation over 10 runs.

No	Name	Train	Val	Test	Total	SVM	MLP	2D-CNN	3D-CNN	ViT	MorpMamba
1	Asphalt	94	18	6,519	6,631	61.04 $\pm$ 12.66	64.43 $\pm$ 9.44	50.30 $\pm$ 18.10	46.27 $\pm$ 16.32	46.63 $\pm$ 14.61	49.11 $\pm$ 15.48
2	Meadows	94	18	18,537	18,649	38.72 $\pm$ 12.41	33.66 $\pm$ 14.46	41.56 $\pm$ 10.45	49.78 $\pm$ 18.88	50.26 $\pm$ 17.19	37.60 $\pm$ 13.61
3	Gravel	94	18	1,987	2,099	29.85 $\pm$ 12.74	36.51 $\pm$ 22.81	34.83 $\pm$ 22.72	43.99 $\pm$ 20.93	31.36 $\pm$ 17.46	30.97 $\pm$ 20.54
4	Trees	94	18	2,952	3,064	76.88 $\pm$ 19.95	80.39 $\pm$ 13.05	62.17 $\pm$ 18.31	82.36 $\pm$ 11.12	57.62 $\pm$ 21.13	73.17 $\pm$ 19.35
5	Metal Sheet	94	18	1,233	1,345	99.60 $\pm$ 0.22	99.45 $\pm$ 0.49	92.99 $\pm$ 4.96	99.76 $\pm$ 0.47	98.90 $\pm$ 1.57	97.31 $\pm$ 2.93
6	Bare Soil	94	18	4,917	5,029	45.81 $\pm$ 11.61	48.65 $\pm$ 12.35	38.41 $\pm$ 6.55	47.89 $\pm$ 13.47	39.51 $\pm$ 11.01	42.46 $\pm$ 14.88

Table 2 : Class-wise and overall classification performance under the non-random spatial sampling setting designed to reduce same-class train-test spatial overlap. The reported values are presented as mean

\pm

standard deviation over repeated runs.

Model	Random OA (%)	Non-Random OA (%)	OA Drop (pp)
SVM	62.15	51.25	10.90
MLP	69.50	50.15	19.35
2D-CNN	86.97	48.16	38.81
3D-CNN	96.17	55.20	40.97
ViT	91.78	51.07	40.71
MorpMamba	81.07	48.45	32.62

Table 3 : Overall accuracy degradation, in percentage points, from random sampling to non-random spatial sampling.

Patch Size	Overlap Percentage (%)		Average Overlap Ratio (%)
Patch Size	Random	Non-Random	Random	Non-Random
$5\times 5$	23.28	0.83	47.29	45.26
$7\times 7$	35.03	1.28	52.98	43.79
$9\times 9$	45.29	1.75	58.45	42.26
$11\times 11$	54.04	2.26	63.61	40.50
$13\times 13$	61.48	2.80	68.31	38.98

Table 4 : Effect of patch size on same-class overlap percentage and average overlap ratio under random and non-random spatial sampling settings. The values are computed from the training and testing patch locations, not from model predictions. Only mean values are reported.

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